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diff --git a/build/lib/prope/__init__.py b/build/lib/prope/__init__.py
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diff --git a/build/lib/prope/jax.py b/build/lib/prope/jax.py
new file mode 100644
index 0000000000000000000000000000000000000000..da6cd499d1fa89756ea97f1677f0f6c1b85adaa1
--- /dev/null
+++ b/build/lib/prope/jax.py
@@ -0,0 +1,315 @@
+# MIT License
+#
+# Copyright (c) Authors of
+# "PRoPE: Projective Positional Encoding for Multiview Transformers"
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import itertools
+from functools import partial
+from typing import Callable
+
+import jax
+import jax.nn
+import jax.numpy as jnp
+
+
+class PropeDotProductAttention:
+    """PRoPE attention with precomputed RoPE coefficients."""
+
+    def __init__(
+        self,
+        head_dim: int,
+        cameras: int,
+        patches_x: int,
+        patches_y: int,
+        image_width: int,
+        image_height: int,
+        freq_base: float = 100.0,
+        freq_scale: float = 1.0,
+    ):
+        self.head_dim = head_dim
+        self.cameras = cameras
+        self.patches_x = patches_x
+        self.patches_y = patches_y
+        self.image_width = image_width
+        self.image_height = image_height
+
+        # Precompute coefficients for x and y
+        self.coeffs_x = _rope_precompute_coeffs(
+            jnp.tile(jnp.arange(patches_x), patches_y * cameras),
+            freq_base=freq_base,
+            freq_scale=freq_scale,
+            feat_dim=head_dim // 4,
+        )
+        self.coeffs_y = _rope_precompute_coeffs(
+            jnp.tile(jnp.repeat(jnp.arange(patches_y), patches_x), cameras),
+            freq_base=freq_base,
+            freq_scale=freq_scale,
+            feat_dim=head_dim // 4,
+        )
+
+    def __call__(
+        self,
+        q: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+        k: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+        v: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+        viewmats: jax.Array,  # (batch, cameras, 4, 4)
+        Ks: jax.Array | None = None,  # (batch, cameras, 3, 3)
+        **kwargs,
+    ) -> jax.Array:
+        """Apply PRoPE attention with precomputed coefficients."""
+        return prope_dot_product_attention(
+            q,
+            k,
+            v,
+            viewmats=viewmats,
+            Ks=Ks,
+            patches_x=self.patches_x,
+            patches_y=self.patches_y,
+            image_width=self.image_width,
+            image_height=self.image_height,
+            coeffs_x=self.coeffs_x,
+            coeffs_y=self.coeffs_y,
+            **kwargs,
+        )
+
+
+def prope_dot_product_attention(
+    q: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+    k: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+    v: jax.Array,  # (batch, seqlen, num_heads, head_dim)
+    *,
+    viewmats: jax.Array,  # (batch, cameras, 4, 4)
+    Ks: jax.Array | None = None,  # (batch, cameras, 3, 3)
+    patches_x: int,  # How many patches wide is each image?
+    patches_y: int,  # How many patches tall is each image?
+    image_width: int,  # Width of the image. Used to normalize intrinsics.
+    image_height: int,  # Height of the image. Used to normalize intrinsics.
+    coeffs_x: tuple[jax.Array, jax.Array] | None = None,
+    coeffs_y: tuple[jax.Array, jax.Array] | None = None,
+    **kwargs,
+) -> jax.Array:
+    """Similar to jax.nn.dot_product_attention, but applies PRoPE-style
+    positional encoding.
+
+    Currently, we assume that the sequence length is equal to:
+
+        cameras * patches_x * patches_y
+
+    And token ordering allows the `(seqlen,)` axis to be reshaped into
+    `(cameras, patches_x, patches_y)`.
+    """
+
+    # We're going to assume self-attention: all inputs are the same shape.
+    (batch, seqlen, num_heads, head_dim) = q.shape
+    cameras = viewmats.shape[1]
+    assert q.shape == k.shape == v.shape
+    assert viewmats.shape == (batch, cameras, 4, 4)
+    assert Ks is None or Ks.shape == (batch, cameras, 3, 3)
+    assert seqlen == cameras * patches_x * patches_y
+
+    # Normalize camera intrinsics.
+    if Ks is not None:
+        Ks_norm = jnp.zeros_like(Ks)
+        Ks_norm = Ks_norm.at[..., 0, 0].set(Ks[..., 0, 0] / image_width)
+        Ks_norm = Ks_norm.at[..., 1, 1].set(Ks[..., 1, 1] / image_height)
+        Ks_norm = Ks_norm.at[..., 0, 2].set(Ks[..., 0, 2] / image_width - 0.5)
+        Ks_norm = Ks_norm.at[..., 1, 2].set(Ks[..., 1, 2] / image_height - 0.5)
+        Ks_norm = Ks_norm.at[..., 2, 2].set(1.0)
+        del Ks
+
+        # Compute the camera projection matrices we use in PRoPE.
+        # - K is an `image<-camera` transform.
+        # - viewmats is a `camera<-world` transform.
+        # - P = lift(K) @ viewmats is an `image<-world` transform.
+        P = jnp.einsum("...ij,...jk->...ik", _lift_K(Ks_norm), viewmats)
+        P_T = P.swapaxes(-1, -2)
+        P_inv = jnp.einsum(
+            "...ij,...jk->...ik",
+            _invert_SE3(viewmats),
+            _lift_K(_invert_K(Ks_norm)),
+        )
+    else:
+        # GTA formula. P is `camera<-world` transform.
+        P = viewmats
+        P_T = P.swapaxes(-1, -2)
+        P_inv = _invert_SE3(viewmats)
+
+    assert P.shape == P_inv.shape == (batch, cameras, 4, 4)
+
+    # Precompute cos/sin terms for RoPE if not provided.
+    if coeffs_x is None:
+        coeffs_x = _rope_precompute_coeffs(
+            jnp.tile(jnp.arange(patches_x), patches_y * cameras),
+            freq_base=100.0,
+            freq_scale=1.0,
+            feat_dim=head_dim // 4,
+        )
+    if coeffs_y is None:
+        coeffs_y = _rope_precompute_coeffs(
+            jnp.tile(jnp.repeat(jnp.arange(patches_y), patches_x), cameras),
+            freq_base=100.0,
+            freq_scale=1.0,
+            feat_dim=head_dim // 4,
+        )
+
+    # Block-diagonal transforms to the inputs and outputs of the attention operator.
+    assert head_dim % 4 == 0
+    transforms_q = [
+        (partial(_apply_tiled_projmat, projmat=P_T), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y), head_dim // 4),
+    ]
+    transforms_kv = [
+        (partial(_apply_tiled_projmat, projmat=P_inv), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y), head_dim // 4),
+    ]
+    transforms_o = [
+        (partial(_apply_tiled_projmat, projmat=P), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x, inverse=True), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y, inverse=True), head_dim // 4),
+    ]
+    out = jax.nn.dot_product_attention(
+        query=_apply_block_diagonal(q, transforms_q),
+        key=_apply_block_diagonal(k, transforms_kv),
+        value=_apply_block_diagonal(v, transforms_kv),
+        **kwargs,
+    )
+    out = _apply_block_diagonal(out, transforms_o)
+    assert out.shape == (batch, seqlen, num_heads, head_dim)
+    return out
+
+
+def _apply_tiled_projmat(
+    feats: jax.Array,  # (batch, seqlen, num_heads, feat_dim)
+    projmat: jax.Array,  # (batch, cameras, 4, 4)
+) -> jax.Array:
+    """Apply projection matrix to features."""
+    # - seqlen => (cameras, patches_x * patches_y)
+    # - feat_dim => (feat_dim // 4, 4)
+    (batch, seqlen, num_heads, feat_dim) = feats.shape
+    cameras = projmat.shape[1]
+    assert seqlen > cameras and seqlen % cameras == 0
+    assert projmat.shape == (batch, cameras, 4, 4)
+    return jnp.einsum(
+        "bcij,bcpnkj->bcpnki",
+        projmat,
+        feats.reshape((batch, cameras, -1, num_heads, feat_dim // 4, 4)),
+    ).reshape(feats.shape)
+
+
+def _rope_precompute_coeffs(
+    positions: jax.Array,  # (seqlen,)
+    freq_base: float,
+    freq_scale: float,
+    feat_dim: int,
+) -> tuple[jax.Array, jax.Array]:
+    """Precompute RoPE coefficients."""
+    assert len(positions.shape) == 1
+    assert feat_dim % 2 == 0
+    num_freqs = feat_dim // 2
+    freqs = freq_scale * (
+        freq_base ** (-jnp.arange(num_freqs)[None, None, None, :] / num_freqs)
+    )
+    angles = positions[None, :, None, None] * freqs
+    # Shape should be: `(batch, seqlen, num_heads, num_freqs)`; we're
+    # broadcasting across `batch` and `num_heads`.
+    assert angles.shape == (1, positions.shape[0], 1, num_freqs)
+    return jnp.cos(angles), jnp.sin(angles)
+
+
+def _rope_apply_coeffs(
+    feats: jax.Array,  # (batch, seqlen, num_heads, feat_dim)
+    coeffs: tuple[jax.Array, jax.Array],
+    inverse: bool = False,
+) -> jax.Array:
+    """Apply RoPE coefficients to features. We adopt a 'split' ordering
+    convention. (in contrast to 'interleaved')"""
+    cos, sin = coeffs
+    assert len(feats.shape) == len(cos.shape) == len(sin.shape) == 4
+    assert cos.shape[-1] == sin.shape[-1] == feats.shape[-1] // 2
+    x_in = feats[..., : feats.shape[-1] // 2]
+    y_in = feats[..., feats.shape[-1] // 2 :]
+    return jnp.concatenate(
+        (
+            [cos * x_in + sin * y_in, -sin * x_in + cos * y_in]
+            if not inverse
+            else [cos * x_in - sin * y_in, sin * x_in + cos * y_in]
+        ),
+        axis=-1,
+    )
+
+
+def _apply_block_diagonal(
+    feats: jax.Array,  # (..., dim)
+    func_size_pairs: list[tuple[Callable[[jax.Array], jax.Array], int]],
+) -> jax.Array:
+    """Apply a block-diagonal function to an input array.
+
+    Each function is specified as a tuple with form:
+
+        ((Array) -> Array, int)
+
+    Where the integer is the size of the input to the function.
+    """
+    funcs, block_sizes = zip(*func_size_pairs)
+    assert feats.shape[-1] == sum(block_sizes)
+    x_blocks = jnp.split(feats, tuple(itertools.accumulate(block_sizes[:-1])), axis=-1)
+    out = jnp.concatenate(
+        [f(x_block) for f, x_block in zip(funcs, x_blocks)],
+        axis=-1,
+    )
+    assert out.shape == feats.shape, "Input/output shapes should match."
+    return out
+
+
+def _invert_SE3(transforms: jax.Array) -> jax.Array:
+    """Invert a 4x4 SE(3) matrix."""
+    assert transforms.shape[-2:] == (4, 4)
+    Rinv = transforms[..., :3, :3].swapaxes(-1, -2)
+    out = jnp.zeros_like(transforms)
+    out = out.at[..., :3, :3].set(Rinv)
+    out = out.at[..., :3, 3].set(
+        -jnp.einsum("...ij,...j->...i", Rinv, transforms[..., :3, 3])
+    )
+    out = out.at[..., 3, 3].set(1.0)
+    return out
+
+
+def _lift_K(Ks: jax.Array) -> jax.Array:
+    """Lift 3x3 matrices to homogeneous 4x4 matrices."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = jnp.zeros(Ks.shape[:-2] + (4, 4))
+    out = out.at[..., :3, :3].set(Ks)
+    out = out.at[..., 3, 3].set(1.0)
+    return out
+
+
+def _invert_K(Ks: jax.Array) -> jax.Array:
+    """Invert 3x3 intrinsics matrices. Assumes no skew."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = jnp.zeros(Ks.shape)
+    out = out.at[..., 0, 0].set(1.0 / Ks[..., 0, 0])
+    out = out.at[..., 1, 1].set(1.0 / Ks[..., 1, 1])
+    out = out.at[..., 0, 2].set(-Ks[..., 0, 2] / Ks[..., 0, 0])
+    out = out.at[..., 1, 2].set(-Ks[..., 1, 2] / Ks[..., 1, 1])
+    out = out.at[..., 2, 2].set(1.0)
+    return out
diff --git a/build/lib/prope/torch.py b/build/lib/prope/torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee63b4c647bb50f988e9e2d91ae8c2dc670e75aa
--- /dev/null
+++ b/build/lib/prope/torch.py
@@ -0,0 +1,347 @@
+# MIT License
+#
+# Copyright (c) Authors of
+# "PRoPE: Projective Positional Encoding for Multiview Transformers"
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from functools import partial
+from typing import Callable, Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+
+
+class PropeDotProductAttention(torch.nn.Module):
+    """PRoPE attention with precomputed RoPE coefficients."""
+
+    coeffs_x_0: torch.Tensor
+    coeffs_x_1: torch.Tensor
+    coeffs_y_0: torch.Tensor
+    coeffs_y_1: torch.Tensor
+
+    def __init__(
+        self,
+        head_dim: int,
+        cameras: int,
+        patches_x: int,
+        patches_y: int,
+        image_width: int,
+        image_height: int,
+        freq_base: float = 100.0,
+        freq_scale: float = 1.0,
+    ):
+        super().__init__()
+        self.head_dim = head_dim
+        self.cameras = cameras
+        self.patches_x = patches_x
+        self.patches_y = patches_y
+        self.image_width = image_width
+        self.image_height = image_height
+
+        coeffs_x: Tuple[torch.Tensor, torch.Tensor] = _rope_precompute_coeffs(
+            torch.tile(torch.arange(patches_x), (patches_y * cameras,)),
+            freq_base=freq_base,
+            freq_scale=freq_scale,
+            feat_dim=head_dim // 4,
+        )
+        coeffs_y: Tuple[torch.Tensor, torch.Tensor] = _rope_precompute_coeffs(
+            torch.tile(
+                torch.repeat_interleave(torch.arange(patches_y), patches_x),
+                (cameras,),
+            ),
+            freq_base=freq_base,
+            freq_scale=freq_scale,
+            feat_dim=head_dim // 4,
+        )
+        # Do not save coeffs to checkpoint as `cameras` might change during testing.
+        self.register_buffer("coeffs_x_0", coeffs_x[0], persistent=False)
+        self.register_buffer("coeffs_x_1", coeffs_x[1], persistent=False)
+        self.register_buffer("coeffs_y_0", coeffs_y[0], persistent=False)
+        self.register_buffer("coeffs_y_1", coeffs_y[1], persistent=False)
+
+    # override load_state_dict to not load coeffs if they exist (for backward compatibility)
+    def load_state_dict(self, state_dict, strict=True):
+        # remove coeffs from state_dict
+        state_dict.pop("coeffs_x_0", None)
+        state_dict.pop("coeffs_x_1", None)
+        state_dict.pop("coeffs_y_0", None)
+        state_dict.pop("coeffs_y_1", None)
+        super().load_state_dict(state_dict, strict)
+
+    def forward(
+        self,
+        q: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+        k: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+        v: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+        viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
+        Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
+        **kwargs,
+    ) -> torch.Tensor:
+        return prope_dot_product_attention(
+            q,
+            k,
+            v,
+            viewmats=viewmats,
+            Ks=Ks,
+            patches_x=self.patches_x,
+            patches_y=self.patches_y,
+            image_width=self.image_width,
+            image_height=self.image_height,
+            coeffs_x=(self.coeffs_x_0, self.coeffs_x_1),
+            coeffs_y=(self.coeffs_y_0, self.coeffs_y_1),
+            **kwargs,
+        )
+
+
+def prope_dot_product_attention(
+    q: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    k: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    v: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
+    *,
+    viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
+    Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
+    patches_x: int,  # How many patches wide is each image?
+    patches_y: int,  # How many patches tall is each image?
+    image_width: int,  # Width of the image. Used to normalize intrinsics.
+    image_height: int,  # Height of the image. Used to normalize intrinsics.
+    coeffs_x: Optional[torch.Tensor] = None,
+    coeffs_y: Optional[torch.Tensor] = None,
+    **kwargs,
+) -> torch.Tensor:
+    """Similar to torch.nn.functional.scaled_dot_product_attention, but applies PRoPE-style
+    positional encoding.
+
+    Currently, we assume that the sequence length is equal to:
+
+        cameras * patches_x * patches_y
+
+    And token ordering allows the `(seqlen,)` axis to be reshaped into
+    `(cameras, patches_x, patches_y)`.
+    """
+
+    # We're going to assume self-attention: all inputs are the same shape.
+    (batch, num_heads, seqlen, head_dim) = q.shape
+    cameras = viewmats.shape[1]
+    assert q.shape == k.shape == v.shape
+    assert viewmats.shape == (batch, cameras, 4, 4)
+    assert Ks is None or Ks.shape == (batch, cameras, 3, 3)
+    assert seqlen == cameras * patches_x * patches_y
+
+    # Normalize camera intrinsics.
+    if Ks is not None:
+        Ks_norm = torch.zeros_like(Ks)
+        Ks_norm[..., 0, 0] = Ks[..., 0, 0] / image_width
+        Ks_norm[..., 1, 1] = Ks[..., 1, 1] / image_height
+        Ks_norm[..., 0, 2] = Ks[..., 0, 2] / image_width - 0.5
+        Ks_norm[..., 1, 2] = Ks[..., 1, 2] / image_height - 0.5
+        Ks_norm[..., 2, 2] = 1.0
+        del Ks
+
+        # Compute the camera projection matrices we use in PRoPE.
+        # - K is an `image<-camera` transform.
+        # - viewmats is a `camera<-world` transform.
+        # - P = lift(K) @ viewmats is an `image<-world` transform.
+        P = torch.einsum("...ij,...jk->...ik", _lift_K(Ks_norm), viewmats)
+        P_T = P.transpose(-1, -2)
+        P_inv = torch.einsum(
+            "...ij,...jk->...ik",
+            _invert_SE3(viewmats),
+            _lift_K(_invert_K(Ks_norm)),
+        )
+
+    else:
+        # GTA formula. P is `camera<-world` transform.
+        P = viewmats
+        P_T = P.transpose(-1, -2)
+        P_inv = _invert_SE3(viewmats)
+
+    assert P.shape == P_inv.shape == (batch, cameras, 4, 4)
+
+    # Precompute cos/sin terms for RoPE. We use tiles/repeats for 'row-major'
+    # broadcasting.
+    if coeffs_x is None:
+        coeffs_x = _rope_precompute_coeffs(
+            torch.tile(
+                torch.arange(patches_x, device=q.device), (patches_y * cameras,)
+            ),
+            freq_base=100.0,
+            freq_scale=1.0,
+            feat_dim=head_dim // 4,
+        )
+    if coeffs_y is None:
+        coeffs_y = _rope_precompute_coeffs(
+            torch.tile(
+                torch.repeat_interleave(
+                    torch.arange(patches_y, device=q.device), patches_x
+                ),
+                (cameras,),
+            ),
+            freq_base=100.0,
+            freq_scale=1.0,
+            feat_dim=head_dim // 4,
+        )
+
+    # Block-diagonal transforms to the inputs and outputs of the attention operator.
+    assert head_dim % 4 == 0
+    transforms_q = [
+        (partial(_apply_tiled_projmat, matrix=P_T), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y), head_dim // 4),
+    ]
+    transforms_kv = [
+        (partial(_apply_tiled_projmat, matrix=P_inv), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y), head_dim // 4),
+    ]
+    transforms_o = [
+        (partial(_apply_tiled_projmat, matrix=P), head_dim // 2),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_x, inverse=True), head_dim // 4),
+        (partial(_rope_apply_coeffs, coeffs=coeffs_y, inverse=True), head_dim // 4),
+    ]
+    out = F.scaled_dot_product_attention(
+        query=_apply_block_diagonal(q, transforms_q),
+        key=_apply_block_diagonal(k, transforms_kv),
+        value=_apply_block_diagonal(v, transforms_kv),
+        **kwargs,
+    )
+    out = _apply_block_diagonal(out, transforms_o)
+    assert out.shape == (batch, num_heads, seqlen, head_dim)
+    return out
+
+
+def _apply_tiled_projmat(
+    feats: torch.Tensor,  # (batch, num_heads, seqlen, feat_dim)
+    matrix: torch.Tensor,  # (batch, cameras, D, D)
+) -> torch.Tensor:
+    """Apply projection matrix to features."""
+    # - seqlen => (cameras, patches_x * patches_y)
+    # - feat_dim => (feat_dim // 4, 4)
+    (batch, num_heads, seqlen, feat_dim) = feats.shape
+    cameras = matrix.shape[1]
+    assert seqlen > cameras and seqlen % cameras == 0
+    D = matrix.shape[-1]
+    assert matrix.shape == (batch, cameras, D, D)
+    assert feat_dim % D == 0
+    return torch.einsum(
+        "bcij,bncpkj->bncpki",
+        matrix,
+        feats.reshape((batch, num_heads, cameras, -1, feat_dim // D, D)),
+    ).reshape(feats.shape)
+
+
+def _rope_precompute_coeffs(
+    positions: torch.Tensor,  # (seqlen,)
+    freq_base: float,
+    freq_scale: float,
+    feat_dim: int,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Precompute RoPE coefficients."""
+    assert len(positions.shape) == 1
+    assert feat_dim % 2 == 0
+    num_freqs = feat_dim // 2
+    freqs = freq_scale * (
+        freq_base
+        ** (
+            -torch.arange(num_freqs, device=positions.device)[None, None, None, :]
+            / num_freqs
+        )
+    )
+    angles = positions[None, None, :, None] * freqs
+    # Shape should be: `(batch, num_heads, seqlen, num_freqs)`; we're
+    # broadcasting across `batch` and `num_heads`.
+    assert angles.shape == (1, 1, positions.shape[0], num_freqs)
+    return torch.cos(angles), torch.sin(angles)
+
+
+def _rope_apply_coeffs(
+    feats: torch.Tensor,  # (batch, num_heads, seqlen, feat_dim)
+    coeffs: Tuple[torch.Tensor, torch.Tensor],
+    inverse: bool = False,
+) -> torch.Tensor:
+    """Apply RoPE coefficients to features. We adopt a 'split' ordering
+    convention. (in contrast to 'interleaved')"""
+    cos, sin = coeffs
+    assert len(feats.shape) == len(cos.shape) == len(sin.shape) == 4
+    assert cos.shape[-1] == sin.shape[-1] == feats.shape[-1] // 2
+    x_in = feats[..., : feats.shape[-1] // 2]
+    y_in = feats[..., feats.shape[-1] // 2 :]
+    return torch.cat(
+        (
+            [cos * x_in + sin * y_in, -sin * x_in + cos * y_in]
+            if not inverse
+            else [cos * x_in - sin * y_in, sin * x_in + cos * y_in]
+        ),
+        dim=-1,
+    )
+
+
+def _apply_block_diagonal(
+    feats: torch.Tensor,  # (..., dim)
+    func_size_pairs: list[tuple[Callable[[torch.Tensor], torch.Tensor], int]],
+) -> torch.Tensor:
+    """Apply a block-diagonal function to an input array.
+
+    Each function is specified as a tuple with form:
+
+        ((Tensor) -> Tensor, int)
+
+    Where the integer is the size of the input to the function.
+    """
+    funcs, block_sizes = zip(*func_size_pairs)
+    assert feats.shape[-1] == sum(block_sizes)
+    x_blocks = torch.split(feats, block_sizes, dim=-1)
+    out = torch.cat(
+        [f(x_block) for f, x_block in zip(funcs, x_blocks)],
+        dim=-1,
+    )
+    assert out.shape == feats.shape, "Input/output shapes should match."
+    return out
+
+
+def _invert_SE3(transforms: torch.Tensor) -> torch.Tensor:
+    """Invert a 4x4 SE(3) matrix."""
+    assert transforms.shape[-2:] == (4, 4)
+    Rinv = transforms[..., :3, :3].transpose(-1, -2)
+    out = torch.zeros_like(transforms)
+    out[..., :3, :3] = Rinv
+    out[..., :3, 3] = -torch.einsum("...ij,...j->...i", Rinv, transforms[..., :3, 3])
+    out[..., 3, 3] = 1.0
+    return out
+
+
+def _lift_K(Ks: torch.Tensor) -> torch.Tensor:
+    """Lift 3x3 matrices to homogeneous 4x4 matrices."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = torch.zeros(Ks.shape[:-2] + (4, 4), device=Ks.device)
+    out[..., :3, :3] = Ks
+    out[..., 3, 3] = 1.0
+    return out
+
+
+def _invert_K(Ks: torch.Tensor) -> torch.Tensor:
+    """Invert 3x3 intrinsics matrices. Assumes no skew."""
+    assert Ks.shape[-2:] == (3, 3)
+    out = torch.zeros_like(Ks)
+    out[..., 0, 0] = 1.0 / Ks[..., 0, 0]
+    out[..., 1, 1] = 1.0 / Ks[..., 1, 1]
+    out[..., 0, 2] = -Ks[..., 0, 2] / Ks[..., 0, 0]
+    out[..., 1, 2] = -Ks[..., 1, 2] / Ks[..., 1, 1]
+    out[..., 2, 2] = 1.0
+    return out
diff --git a/data_splits/airvln/test/navigation_eval.pkl b/data_splits/airvln/test/navigation_eval.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..2de5c5e4591968034cd3dc947fe44c3b57d70367
--- /dev/null
+++ b/data_splits/airvln/test/navigation_eval.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a19c11ae615e506cc45f9109accb0e2824cc8b692aba6d3f3bd412873b0c8bc
+size 817
diff --git a/data_splits/airvln/test/rollout_traj_names.txt b/data_splits/airvln/test/rollout_traj_names.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fd171fec1b6ff3ba793ad52a8bbd930ee86fa28b
--- /dev/null
+++ b/data_splits/airvln/test/rollout_traj_names.txt
@@ -0,0 +1 @@
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diff --git a/data_splits/airvln/test/traj_names.txt b/data_splits/airvln/test/traj_names.txt
new file mode 100644
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diff --git a/data_splits/airvln/train/traj_names.txt b/data_splits/airvln/train/traj_names.txt
new file mode 100644
index 0000000000000000000000000000000000000000..666dbf7e8feb2b2bfeaa446c5ff879ec678ba549
--- /dev/null
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diff --git a/data_splits/airvln_16/test/traj_names_bak.txt b/data_splits/airvln_16/test/traj_names_bak.txt
new file mode 100644
index 0000000000000000000000000000000000000000..980474608fd1a90db9c9ccb865be444283a2f676
--- /dev/null
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diff --git a/data_splits/airvln_16/train/traj_names copy 3.txt b/data_splits/airvln_16/train/traj_names copy 3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3500457471857c46b59b65981016bfdd9945b649
--- /dev/null
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diff --git a/data_splits/airvln_16/train/traj_names copy.txt b/data_splits/airvln_16/train/traj_names copy.txt
new file mode 100644
index 0000000000000000000000000000000000000000..55111424d437d54b31adc7927ecfc8d54270b908
--- /dev/null
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\ No newline at end of file
diff --git a/data_splits/airvln_16/train/traj_names.txt b/data_splits/airvln_16/train/traj_names.txt
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index 0000000000000000000000000000000000000000..3500457471857c46b59b65981016bfdd9945b649
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diff --git a/data_splits/airvln_16/train/traj_names_bak.txt b/data_splits/airvln_16/train/traj_names_bak.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3500457471857c46b59b65981016bfdd9945b649
--- /dev/null
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diff --git a/data_splits/airvln_1episode/train/traj_names ori.txt b/data_splits/airvln_1episode/train/traj_names ori.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a17bfb55ae66c5cdf284ae90716e725f937eee6c
--- /dev/null
+++ b/data_splits/airvln_1episode/train/traj_names ori.txt	
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diff --git a/data_splits/airvln_airspace/test/traj_names.txt b/data_splits/airvln_airspace/test/traj_names.txt
new file mode 100644
index 0000000000000000000000000000000000000000..695e1e662639b4b46c8f1f60ff649d0bd0583194
--- /dev/null
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\ No newline at end of file
diff --git a/data_splits/airvln_airspace/train/traj_names copy.txt b/data_splits/airvln_airspace/train/traj_names copy.txt
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diff --git a/data_splits/sacson/train/traj_names.txt b/data_splits/sacson/train/traj_names.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ef399979e33aa8a506137083a275e2916e5803cc
--- /dev/null
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diff --git a/data_splits/scand/test/rollout.pkl b/data_splits/scand/test/rollout.pkl
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index 0000000000000000000000000000000000000000..8d84931d87258bc567e1191c732aa97421dbe5a3
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diff --git a/data_splits/scand/test/rollout_traj_names.txt b/data_splits/scand/test/rollout_traj_names.txt
new file mode 100644
index 0000000000000000000000000000000000000000..40cf9fa553ad29e37665f603aa45f99d73637d53
--- /dev/null
+++ b/data_splits/scand/test/rollout_traj_names.txt
@@ -0,0 +1,32 @@
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+random_mdps_B_Spot_Speedway_SAC_Wed_Nov_10_48_6
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_40
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_19
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_5
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_4
+random_mdps_A_Jackal_AHG_Library_Thu_Oct_28_7_0
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_36
+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_10
+random_mdps_B_Spot_Cola_Cola_Sat_Nov_13_97_2
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_2
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_128_8
+random_mdps_A_Spot_Library_AHG_Fri_Nov_12_86_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_52
+random_mdps_A_Spot_Parlin_Parlin_Wed_Nov_10_51_5
+random_mdps_B_Jackal_Sanjac_Stadium_Sat_Nov_13_89_0
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_30
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_69
+random_mdps_A_Jackal_AHG_Library_Thu_Oct_28_1_0
+random_mdps_B_Spot_RLM_OsCafe_Mon_Nov_15_117_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_27
+random_mdps_B_Spot_Ahg_Library_Thu_Nov_11_68_6
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_28_3
+random_mdps_B_Spot_Dobie_Library_Thu_Nov_11_74_2
+random_mdps_A_Spot_Stadium_PerformingArtsCenter_Sat_Nov_13_106_10
+random_mdps_C_Spot_Speedway_Butler_Fri_Nov_26_131_5
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_24
+random_mdps_B_Spot_Thompson_Thompson_Sat_Nov_13_100_0
+random_mdps_B_Spot_Union_Dobie_Thu_Nov_11_72_7
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_3
+random_mdps_C_Spot_SanJac_Bass_Fri_Nov_26_125_6
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_28
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+random_mdps_B_Spot_RLM_OsCafe_Mon_Nov_15_117_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_55
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_23
+random_mdps_A_Spot_Library_Fountain_Tue_Nov_9_35_2
+random_mdps_B_Spot_SAC_NHB_Mon_Nov_15_113_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_79
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_63_0
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+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_4
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_54
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+random_mdps_B_Spot_Cola_Cola_Sat_Nov_13_97_2
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+random_mdps_B_Spot_Speedway_SAC_Wed_Nov_10_48_6
+random_mdps_A_Spot_Union_Union_Wed_Nov_10_67_1
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_5
+random_mdps_C_Spot_Speedway_Butler_Fri_Nov_26_131_2
+random_mdps_A_Jackal_AHG_Library_Thu_Oct_28_7_0
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_16
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+random_mdps_A_Spot_NHB_Ahg_Wed_Nov_10_55_0
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_24
+random_mdps_A_Spot_Butler_LBJ_Sat_Nov_13_104_0
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_128_3
+random_mdps_B_Spot_SAC_SAC_Mon_Nov_15_109_0
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_69
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+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_10
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+random_mdps_A_Jackal_GDC_GDC_Fri_Oct_29_11_0
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+random_mdps_B_Spot_AHG_Union_Mon_Nov_15_111_1
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+random_mdps_A_Spot_Library_AHG_Thu_Nov_4_22_1
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+random_mdps_A_Jackal_REC_Abandon_Sat_Nov_13_92_0
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+random_mdps_B_Jackal_Sanjac_Stadium_Sat_Nov_13_89_0
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+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_29
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_2
+random_mdps_A_Spot_Dobie_Fountain_Fri_Nov_12_84_1
+random_mdps_A_Spot_Parlin_Parlin_Wed_Nov_10_51_5
+random_mdps_A_Spot_Ballstructure_UTTower_Wed_Nov_10_60_0
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_9
+random_mdps_A_Spot_Lbj_Thompson_Sat_Nov_13_99_1
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_3
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diff --git a/data_splits/scand/train/traj_names.txt b/data_splits/scand/train/traj_names.txt
new file mode 100644
index 0000000000000000000000000000000000000000..478cd14a90f570645d35f81035be112f17c00e32
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+++ b/data_splits/scand/train/traj_names.txt
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+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_2
+random_mdps_A_Spot_AHG_Library_Fri_Nov_5_21_5
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+random_mdps_A_Jackal_Fountain_Mccombs_Thu_Oct_28_6_0
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+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_46
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+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_13
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+random_mdps_A_Spot_LBJ_Stadium_Sat_Nov_13_105_2
+random_mdps_A_Spot_Library_MLK_Thu_Nov_18_122_3
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+random_mdps_B_Spot_RLM_OsCafe_Mon_Nov_15_117_1
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+random_mdps_B_Jackal_REC_Footballfield_Sat_Nov_13_93_0
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+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_0
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_64_4
+random_mdps_A_Spot_Ballstructure_UTTower_Wed_Nov_10_60_3
+random_mdps_A_Spot_Fountain_Library_Fri_Nov_12_85_1
+random_mdps_A_Spot_Ballstructure_UTTower_Wed_Nov_10_60_4
+random_mdps_A_Spot_Fountain_Library_Mon_Nov_8_26_1
+random_mdps_D_Spot_PerformingArts_Lbj_Sat_Nov_13_98_2
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_63_1
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_27
+random_mdps_A_Spot_AHG_Library_Wed_Nov_10_46_1
+random_mdps_A_Spot_Fountain_Library_Fri_Nov_12_85_4
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_66
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_13
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_14
+random_mdps_B_Spot_RLM_RLM_Wed_Nov_10_45_0
+random_mdps_B_Spot_Fountain_Library_Mon_Nov_8_31_0
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_28_1
+random_mdps_C_Spot_Rec_Bass_Fri_Nov_26_127_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_34
+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_1
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_4
+random_mdps_B_Spot_GDC_Library_Tue_Nov_9_42_3
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_12
+random_mdps_B_Spot_Library_Jester_Thu_Nov_11_75_3
+random_mdps_A_Spot_Ballstructure_UTTower_Wed_Nov_10_60_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_64
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_15
+random_mdps_B_Spot_Speedway_SAC_Wed_Nov_10_48_8
+random_mdps_A_Jackal_Brackenridge_Greg_Sat_Nov_13_94_0
+random_mdps_A_Jackal_Fountain_Library_Fri_Oct_29_9_0
+random_mdps_A_Spot_NHB_Jester_Wed_Nov_10_62_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_44
+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_5
+random_mdps_A_Spot_Library_Fountain_Tue_Nov_9_35_6
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_7
+random_mdps_C_Spot_Tent_AHG_Fri_Nov_26_130_0
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_2
+random_mdps_B_Spot_Library_MCCombs_Wed_Nov_10_47_9
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_17
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_0
+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_4
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_18
+random_mdps_A_Spot_NHB_Jester_Wed_Nov_10_62_0
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_68
+random_mdps_C_Spot_Rec_Bass_Fri_Nov_26_127_1
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_6
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_19
+random_mdps_A_Spot_Library_Fountain_Mon_Nov_8_30_3
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_31
+random_mdps_A_Spot_Library_Fountain_Tue_Nov_9_35_3
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_23
+random_mdps_C_Spot_SanJac_Bass_Fri_Nov_26_125_1
+random_mdps_A_Spot_Stadium_PerformingArtsCenter_Sat_Nov_13_106_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_61
+random_mdps_A_Spot_Butler_Foodtruck_Fri_Nov_26_132_9
+random_mdps_C_Spot_Rec_Tent_Fri_Nov_26_129_3
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_10
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_37
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_81
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_28_0
+random_mdps_B_Spot_Ahg_Library_Thu_Nov_11_68_3
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_33
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_64_0
+random_mdps_B_Jackal_Library_Sanjac_Sat_Nov_13_88_0
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_21
+random_mdps_A_Jackal_Sanjac_Rec_Sat_Nov_13_91_0
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_12
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_22
+random_mdps_B_Spot_Ahg_Library_Thu_Nov_11_68_0
+random_mdps_B_Spot_Welch_Welch_Thu_Nov_11_70_1
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_128_15
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_38
+random_mdps_C_Spot_Speedway_Butler_Fri_Nov_26_131_8
+random_mdps_B_Spot_Speedway_SAC_Wed_Nov_10_48_10
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_10
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_128_11
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_5
+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_9
+random_mdps_A_Spot_Library_AHG_Fri_Nov_12_86_2
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_22
+random_mdps_A_Spot_Stadium_PerformingArtsCenter_Sat_Nov_13_106_8
+random_mdps_B_Spot_Library_MCCombs_Wed_Nov_10_47_5
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_17
+random_mdps_B_Spot_Speedway_SAC_Wed_Nov_10_48_4
+random_mdps_A_Jackal_Library_AHG_Fri_Oct_29_8_0
+random_mdps_B_Spot_Speedway_SAC_Wed_Nov_10_48_0
+random_mdps_B_Spot_Library_MCCombs_Wed_Nov_10_47_8
+random_mdps_C_Spot_Rec_Tent_Fri_Nov_26_129_7
+random_mdps_A_Spot_Library_Fountain_Tue_Nov_9_35_1
+random_mdps_B_Spot_Speedway_SAC_Wed_Nov_10_48_11
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_7
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_12
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_64_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_11
+random_mdps_A_Spot_Library_AHG_Tue_Nov_9_43_1
+random_mdps_A_Jackal_SAC_GDC_Fri_Oct_29_14_0
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_3
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_12
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_3
+random_mdps_A_Spot_Library_AHG_Thu_Nov_4_22_0
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_60
+random_mdps_A_Spot_Library_Fountain_Tue_Nov_9_35_8
+random_mdps_A_Spot_AHG_Cola_Sat_Nov_13_96_0
+random_mdps_C_Spot_Rec_Tent_Fri_Nov_26_129_4
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_15
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_67
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_53
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_20
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_13
+random_mdps_B_Spot_Fountain_Library_Mon_Nov_8_31_3
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_22
+random_mdps_A_Spot_Welch_Union_Thu_Nov_11_71_2
+random_mdps_A_Spot_Parlin_Parlin_Wed_Nov_10_51_4
+random_mdps_B_Spot_GDC_Library_Tue_Nov_9_42_10
+random_mdps_B_Spot_Union_Dobie_Thu_Nov_11_72_10
+random_mdps_B_Spot_Library_Greg_Thu_Nov_11_77_0
+random_mdps_B_Spot_AHG_Union_Mon_Nov_15_111_4
+random_mdps_A_Spot_JCL_JCL_Wed_Nov_10_65_1
+random_mdps_B_Spot_Union_SAC_Mon_Nov_15_112_3
+random_mdps_A_Spot_Ballstructure_UTTower_Wed_Nov_10_60_7
+random_mdps_C_Spot_Speedway_Butler_Fri_Nov_26_131_3
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_86
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_47
+random_mdps_C_Spot_Speedway_Butler_Fri_Nov_26_131_1
+random_mdps_A_Spot_EER_OsCafe_Tue_Nov_9_39_1
+random_mdps_B_Spot_Welch_Welch_Thu_Nov_11_70_0
+random_mdps_A_Spot_AHG_Library_Wed_Nov_10_46_2
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_11
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_32
+random_mdps_A_Spot_Library_AHG_Thu_Nov_4_22_2
+random_mdps_A_Spot_Stadium_PerformingArtsCenter_Sat_Nov_13_106_4
+random_mdps_A_Spot_AHG_Library_Fri_Nov_5_21_2
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+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_35
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_11
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+random_mdps_B_Spot_Ahg_Library_Thu_Nov_11_68_5
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_7
+random_mdps_B_Spot_AHG_Library_Mon_Nov_15_107_0
+random_mdps_A_Spot_Library_AHG_Tue_Nov_9_43_0
+random_mdps_A_Jackal_REC_Abandon_Sat_Nov_13_92_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_13
+random_mdps_B_Spot_GDC_Library_Tue_Nov_9_42_5
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_20
+random_mdps_A_Spot_AHG_Library_Mon_Nov_8_24_0
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+random_mdps_A_Spot_LBJ_Stadium_Sat_Nov_13_105_0
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_84
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_21
+random_mdps_A_Spot_Security_NHB_Wed_Nov_10_54_0
+random_mdps_B_Spot_Fountain_Union_Tue_Nov_9_36_0
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_36
+random_mdps_B_Spot_AHG_Union_Mon_Nov_15_111_0
+random_mdps_A_Spot_SAC_GeorgeWashStatue_Wed_Nov_10_50_0
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_44
+random_mdps_A_Spot_Butler_Foodtruck_Fri_Nov_26_132_8
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_26
+random_mdps_B_Spot_GDC_Library_Tue_Nov_9_42_6
+random_mdps_C_Spot_Rec_Tent_Fri_Nov_26_129_6
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_72
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+random_mdps_B_Spot_Fountain_Library_Mon_Nov_8_31_1
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_11
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_80
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_71
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_0
+random_mdps_B_Spot_Ahg_Library_Thu_Nov_11_68_4
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_2
+random_mdps_A_Spot_Library_Fountain_Mon_Nov_8_30_2
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+random_mdps_B_Spot_Dobie_Library_Thu_Nov_11_74_1
+random_mdps_A_Jackal_Library_Fountain_Thu_Oct_28_5_0
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_43
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_64_7
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_20
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_57
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_10
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_83
+random_mdps_B_Spot_RLM_OsCafe_Mon_Nov_15_117_0
+random_mdps_D_Spot_PerformingArts_Lbj_Sat_Nov_13_98_0
+random_mdps_A_Spot_Welch_Union_Thu_Nov_11_71_3
+random_mdps_B_Spot_NHB_Union_Mon_Nov_15_114_1
+random_mdps_B_Spot_SAC_NHB_Mon_Nov_15_113_0
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_8
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_65
+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_6
+random_mdps_A_Spot_AHG_Cola_Sat_Nov_13_96_1
+random_mdps_A_Spot_Butler_Foodtruck_Fri_Nov_26_132_2
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_63_3
+random_mdps_B_Spot_Fountain_Library_Mon_Nov_8_31_6
+random_mdps_C_Spot_Rec_Tent_Fri_Nov_26_129_2
+random_mdps_A_Spot_Welch_Union_Thu_Nov_11_71_1
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_4
+random_mdps_A_Spot_Library_MLK_Thu_Nov_18_122_0
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_12
+random_mdps_A_Spot_Welch_Union_Thu_Nov_11_71_4
+random_mdps_A_Spot_Stadium_PerformingArtsCenter_Sat_Nov_13_106_1
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_11
+random_mdps_A_Spot_Dobie_Fountain_Fri_Nov_12_84_0
+random_mdps_A_Spot_AHG_Cola_Sat_Nov_13_96_2
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_126_2
+random_mdps_A_Spot_Union_Union_Wed_Nov_10_67_0
+Bags_A_Jackal_Brackenridge_Greg_Sat_Nov_13_94_0
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_6
+random_mdps_A_Jackal_SAC_SACindoor_Fri_Oct_29_15_0
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_4
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_28
+random_mdps_A_Spot_Fountain_Dobie_Fri_Nov_12_83_0
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_63_4
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_14
+random_mdps_B_Spot_Union_Dobie_Thu_Nov_11_72_8
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_5
+random_mdps_B_Spot_RLM_RLM_Wed_Nov_10_45_1
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_34
+random_mdps_A_Spot_AHG_Library_Fri_Nov_5_21_0
+random_mdps_B_Spot_JCL_JCL_Thu_Nov_11_76_6
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_6
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_77
+random_mdps_A_Spot_Stadium_PerformingArtsCenter_Sat_Nov_13_106_5
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_128_9
+random_mdps_C_Spot_Rec_Tent_Fri_Nov_26_129_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_40
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_2
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_25
+random_mdps_B_Spot_Hogg_GDC_Mon_Nov_15_115_0
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_1
+random_mdps_A_Spot_Ballstructure_UTTower_Wed_Nov_10_60_5
+random_mdps_C_Spot_Rec_Tent_Fri_Nov_26_129_5
+random_mdps_A_Spot_Library_Fountain_Tue_Nov_9_35_4
+random_mdps_A_Spot_JCL_JCL_Wed_Nov_10_65_0
+random_mdps_B_Spot_Parlin_NHB_Wed_Nov_10_52_0
+random_mdps_A_Spot_GDC_AHG_Thu_Nov_18_124_0
+random_mdps_A_Spot_Library_Fountain_Tue_Nov_9_35_0
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_28_2
+random_mdps_C_Spot_Tent_AHG_Fri_Nov_26_130_4
+random_mdps_A_Spot_Ballstructure_UTTower_Wed_Nov_10_60_6
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_26
+random_mdps_A_Spot_Stadium_PerformingArtsCenter_Sat_Nov_13_106_3
+random_mdps_A_Spot_GeorgeWashington_Mccombs_Wed_Nov_10_59_0
+random_mdps_B_Spot_GDC_Library_Tue_Nov_9_42_9
+random_mdps_B_Spot_Dobie_Library_Thu_Nov_11_74_3
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_15
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_38
+random_mdps_A_Spot_Library_Fountain_Mon_Nov_8_25_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_24
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_126_3
+random_mdps_B_Spot_GDC_Library_Tue_Nov_9_42_7
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_128_10
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_126_1
+random_mdps_A_Spot_Boatbuilding_AHG_Thu_Nov_4_23_0
+random_mdps_A_Spot_Butler_LBJ_Sat_Nov_13_104_1
+random_mdps_B_Spot_GDC_Library_Tue_Nov_9_42_2
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_4
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_8
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_17
+random_mdps_A_Spot_Parlin_Parlin_Wed_Nov_10_51_1
+random_mdps_B_Spot_Union_Dobie_Thu_Nov_11_72_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_17
+random_mdps_C_Spot_Speedway_Butler_Fri_Nov_26_131_0
+random_mdps_A_Spot_Jester_Jester_Wed_Nov_10_64_2
+random_mdps_A_Spot_Ballstructure_UTTower_Wed_Nov_10_60_1
+random_mdps_B_Spot_LBJ_Thompson_Sat_Nov_13_101_1
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_48
+random_mdps_A_Spot_Parlin_Parlin_Wed_Nov_10_51_3
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_5
+random_mdps_B_Spot_Library_WagnerHall_Mon_Nov_8_29_11
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_2
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_39
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_13
+random_mdps_A_Spot_Dobie_Littlefield_Wed_Nov_10_58_0
+random_mdps_A_Spot_JCL_JCL_Wed_Nov_10_66_0
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_6
+random_mdps_C_Spot_Speedway_Butler_Fri_Nov_26_131_7
+random_mdps_A_Spot_GDC_Jester_Thu_Nov_18_121_0
+random_mdps_A_Spot_Library_Fountain_Tue_Nov_9_35_5
+random_mdps_C_Spot_Tent_AHG_Fri_Nov_26_130_1
+random_mdps_A_Spot_Library_MLK_Thu_Nov_18_122_2
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_1
+random_mdps_B_Spot_Library_SAC_Thu_Nov_11_69_9
+random_mdps_B_Spot_Library_EER_Tue_Nov_9_38_2
+random_mdps_A_Spot_EER_AHG_Tue_Nov_9_40_0
+random_mdps_A_Jackal_AHG_Library_Thu_Oct_28_2_0
+random_mdps_A_Spot_Ahg_EERC_Thu_Nov_11_79_0
+random_mdps_A_Spot_Lbj_Thompson_Sat_Nov_13_99_2
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_10
+random_mdps_A_Jackal_Library_AHG_Thu_Oct_28_3_0
+random_mdps_B_Spot_AHG_Union_Mon_Nov_15_111_2
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_41
+random_mdps_B_Spot_Library_EER_Tue_Nov_9_38_1
+random_mdps_A_Spot_Bass_Rec_Fri_Nov_26_128_7
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_27
+random_mdps_D_Spot_PerformingArts_Lbj_Sat_Nov_13_98_1
+random_mdps_A_Spot_AHG_Library_Fri_Nov_12_81_0
+random_mdps_A_Spot_AHG_AHG_Mon_Nov_8_27_8
+random_mdps_B_Spot_JCL_JCL_Mon_Nov_15_108_19
+random_mdps_B_Spot_SAC_NHB_Mon_Nov_15_113_3
+random_mdps_D_Spot_PerformingArts_Lbj_Sat_Nov_13_98_3
+random_mdps_A_Spot_AHG_GDC_Tue_Nov_9_41_9
+random_mdps_A_Spot_Library_AHG_Mon_Nov_8_33_26
+random_mdps_C_Spot_Rec_Tent_Fri_Nov_26_129_0
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diff --git a/diffusion/__init__.py b/diffusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..66b3b17b38d6cea2f3d72cfad4de3f51f815f969
--- /dev/null
+++ b/diffusion/__init__.py
@@ -0,0 +1,52 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Modified from OpenAI's diffusion repos
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+from . import gaussian_diffusion as gd
+from .respace import SpacedDiffusion, space_timesteps
+
+
+def create_diffusion(
+    timestep_respacing,
+    noise_schedule="linear", 
+    use_kl=False,
+    sigma_small=False,
+    predict_xstart=False,
+    learn_sigma=True,
+    rescale_learned_sigmas=False,
+    diffusion_steps=1000
+):
+    betas = gd.get_named_beta_schedule(noise_schedule, diffusion_steps)
+    if use_kl:
+        loss_type = gd.LossType.RESCALED_KL
+    elif rescale_learned_sigmas:
+        loss_type = gd.LossType.RESCALED_MSE
+    else:
+        loss_type = gd.LossType.MSE
+    if timestep_respacing is None or timestep_respacing == "":
+        timestep_respacing = [diffusion_steps]
+    return SpacedDiffusion(
+        use_timesteps=space_timesteps(diffusion_steps, timestep_respacing),
+        betas=betas,
+        model_mean_type=(
+            gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
+        ),
+        model_var_type=(
+            (
+                gd.ModelVarType.FIXED_LARGE
+                if not sigma_small
+                else gd.ModelVarType.FIXED_SMALL
+            )
+            if not learn_sigma
+            else gd.ModelVarType.LEARNED_RANGE
+        ),
+        loss_type=loss_type
+        # rescale_timesteps=rescale_timesteps,
+    )
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diff --git a/diffusion/diffusion_utils.py b/diffusion/diffusion_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..78c483c6cc3f9116153e5c85506b5b8c076356b7
--- /dev/null
+++ b/diffusion/diffusion_utils.py
@@ -0,0 +1,94 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Modified from OpenAI's diffusion repos
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+import torch as th
+import numpy as np
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, th.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for th.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + th.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
+    )
+
+
+def approx_standard_normal_cdf(x):
+    """
+    A fast approximation of the cumulative distribution function of the
+    standard normal.
+    """
+    return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
+
+
+def continuous_gaussian_log_likelihood(x, *, means, log_scales):
+    """
+    Compute the log-likelihood of a continuous Gaussian distribution.
+    :param x: the targets
+    :param means: the Gaussian mean Tensor.
+    :param log_scales: the Gaussian log stddev Tensor.
+    :return: a tensor like x of log probabilities (in nats).
+    """
+    centered_x = x - means
+    inv_stdv = th.exp(-log_scales)
+    normalized_x = centered_x * inv_stdv
+    log_probs = th.distributions.Normal(th.zeros_like(x), th.ones_like(x)).log_prob(normalized_x)
+    return log_probs
+
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    """
+    Compute the log-likelihood of a Gaussian distribution discretizing to a
+    given image.
+    :param x: the target images. It is assumed that this was uint8 values,
+              rescaled to the range [-1, 1].
+    :param means: the Gaussian mean Tensor.
+    :param log_scales: the Gaussian log stddev Tensor.
+    :return: a tensor like x of log probabilities (in nats).
+    """
+    assert x.shape == means.shape == log_scales.shape
+    centered_x = x - means
+    inv_stdv = th.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
+    cdf_plus = approx_standard_normal_cdf(plus_in)
+    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
+    cdf_min = approx_standard_normal_cdf(min_in)
+    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
+    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
+    cdf_delta = cdf_plus - cdf_min
+    log_probs = th.where(
+        x < -0.999,
+        log_cdf_plus,
+        th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
+    )
+    assert log_probs.shape == x.shape
+    return log_probs
diff --git a/diffusion/gaussian_diffusion.py b/diffusion/gaussian_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..347d2c2ebf9ef55254d07485f0ba05f72a690e1b
--- /dev/null
+++ b/diffusion/gaussian_diffusion.py
@@ -0,0 +1,879 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Modified from OpenAI's diffusion repos
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+
+import math
+
+import numpy as np
+import torch as th
+import enum
+
+from .diffusion_utils import discretized_gaussian_log_likelihood, normal_kl
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+class ModelMeanType(enum.Enum):
+    """
+    Which type of output the model predicts.
+    """
+
+    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
+    START_X = enum.auto()  # the model predicts x_0
+    EPSILON = enum.auto()  # the model predicts epsilon
+
+
+class ModelVarType(enum.Enum):
+    """
+    What is used as the model's output variance.
+    The LEARNED_RANGE option has been added to allow the model to predict
+    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
+    """
+
+    LEARNED = enum.auto()
+    FIXED_SMALL = enum.auto()
+    FIXED_LARGE = enum.auto()
+    LEARNED_RANGE = enum.auto()
+
+
+class LossType(enum.Enum):
+    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
+    RESCALED_MSE = (
+        enum.auto()
+    )  # use raw MSE loss (with RESCALED_KL when learning variances)
+    KL = enum.auto()  # use the variational lower-bound
+    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB
+
+    def is_vb(self):
+        return self == LossType.KL or self == LossType.RESCALED_KL
+
+
+def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
+    betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    warmup_time = int(num_diffusion_timesteps * warmup_frac)
+    betas[:warmup_time] = np.linspace(beta_start, beta_end, warmup_time, dtype=np.float64)
+    return betas
+
+
+def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
+    """
+    This is the deprecated API for creating beta schedules.
+    See get_named_beta_schedule() for the new library of schedules.
+    """
+    if beta_schedule == "quad":
+        betas = (
+            np.linspace(
+                beta_start ** 0.5,
+                beta_end ** 0.5,
+                num_diffusion_timesteps,
+                dtype=np.float64,
+            )
+            ** 2
+        )
+    elif beta_schedule == "linear":
+        betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
+    elif beta_schedule == "warmup10":
+        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1)
+    elif beta_schedule == "warmup50":
+        betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5)
+    elif beta_schedule == "const":
+        betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
+    elif beta_schedule == "jsd":  # 1/T, 1/(T-1), 1/(T-2), ..., 1
+        betas = 1.0 / np.linspace(
+            num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
+        )
+    else:
+        raise NotImplementedError(beta_schedule)
+    assert betas.shape == (num_diffusion_timesteps,)
+    return betas
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        return get_beta_schedule(
+            "linear",
+            beta_start=scale * 0.0001,
+            beta_end=scale * 0.02,
+            num_diffusion_timesteps=num_diffusion_timesteps,
+        )
+    elif schedule_name == "squaredcos_cap_v2":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+        )
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+class GaussianDiffusion:
+    """
+    Utilities for training and sampling diffusion models.
+    Original ported from this codebase:
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+    :param betas: a 1-D numpy array of betas for each diffusion timestep,
+                  starting at T and going to 1.
+    """
+
+    def __init__(
+        self,
+        *,
+        betas,
+        model_mean_type,
+        model_var_type,
+        loss_type
+    ):
+
+        self.model_mean_type = model_mean_type
+        self.model_var_type = model_var_type
+        self.loss_type = loss_type
+
+        # Use float64 for accuracy.
+        betas = np.array(betas, dtype=np.float64)
+        self.betas = betas
+        assert len(betas.shape) == 1, "betas must be 1-D"
+        assert (betas > 0).all() and (betas <= 1).all()
+
+        self.num_timesteps = int(betas.shape[0])
+
+        alphas = 1.0 - betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
+        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
+        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
+        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
+        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        self.posterior_variance = (
+            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.posterior_log_variance_clipped = np.log(
+            np.append(self.posterior_variance[1], self.posterior_variance[1:])
+        ) if len(self.posterior_variance) > 1 else np.array([])
+
+        self.posterior_mean_coef1 = (
+            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        self.posterior_mean_coef2 = (
+            (1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod)
+        )
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data for a given number of diffusion steps.
+        In other words, sample from q(x_t | x_0).
+        :param x_start: the initial data batch.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :param noise: if specified, the split-out normal noise.
+        :return: A noisy version of x_start.
+        """
+        if noise is None:
+            noise = th.randn_like(x_start)
+        assert noise.shape == x_start.shape
+        return (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior:
+            q(x_{t-1} | x_t, x_0)
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.posterior_log_variance_clipped, t, x_t.shape
+        )
+        assert (
+            posterior_mean.shape[0]
+            == posterior_variance.shape[0]
+            == posterior_log_variance_clipped.shape[0]
+            == x_start.shape[0]
+        )
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None):
+        """
+        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+        the initial x, x_0.
+        :param model: the model, which takes a signal and a batch of timesteps
+                      as input.
+        :param x: the [N x C x ...] tensor at time t.
+        :param t: a 1-D Tensor of timesteps.
+        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample. Applies before
+            clip_denoised.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict with the following keys:
+                 - 'mean': the model mean output.
+                 - 'variance': the model variance output.
+                 - 'log_variance': the log of 'variance'.
+                 - 'pred_xstart': the prediction for x_0.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        B, C = x.shape[:2]
+        assert t.shape == (B,)
+        model_output = model(x, t, **model_kwargs)
+        if isinstance(model_output, tuple):
+            model_output, extra = model_output
+        else:
+            extra = None
+
+        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
+            assert model_output.shape == (B, C * 2, *x.shape[2:])
+            model_output, model_var_values = th.split(model_output, C, dim=1)
+            min_log = _extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape)
+            max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
+            # The model_var_values is [-1, 1] for [min_var, max_var].
+            frac = (model_var_values + 1) / 2
+            model_log_variance = frac * max_log + (1 - frac) * min_log
+            model_variance = th.exp(model_log_variance)
+        else:
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so
+                # to get a better decoder log likelihood.
+                ModelVarType.FIXED_LARGE: (
+                    np.append(self.posterior_variance[1], self.betas[1:]),
+                    np.log(np.append(self.posterior_variance[1], self.betas[1:])),
+                ),
+                ModelVarType.FIXED_SMALL: (
+                    self.posterior_variance,
+                    self.posterior_log_variance_clipped,
+                ),
+            }[self.model_var_type]
+            model_variance = _extract_into_tensor(model_variance, t, x.shape)
+            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
+
+        def process_xstart(x):
+            if denoised_fn is not None:
+                x = denoised_fn(x)
+            if clip_denoised:
+                return x.clamp(-1, 1)
+            return x
+
+        if self.model_mean_type == ModelMeanType.START_X:
+            pred_xstart = process_xstart(model_output)
+        else:
+            pred_xstart = process_xstart(
+                self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
+            )
+        model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)
+
+        assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
+        return {
+            "mean": model_mean,
+            "variance": model_variance,
+            "log_variance": model_log_variance,
+            "pred_xstart": pred_xstart,
+            "extra": extra,
+        }
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
+        )
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute the mean for the previous step, given a function cond_fn that
+        computes the gradient of a conditional log probability with respect to
+        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+        condition on y.
+        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+        """
+        gradient = cond_fn(x, t, **model_kwargs)
+        new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
+        return new_mean
+
+    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute what the p_mean_variance output would have been, should the
+        model's score function be conditioned by cond_fn.
+        See condition_mean() for details on cond_fn.
+        Unlike condition_mean(), this instead uses the conditioning strategy
+        from Song et al (2020).
+        """
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
+
+        out = p_mean_var.copy()
+        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
+        out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t)
+        return out
+
+    def p_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        noise = th.randn_like(x)
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        if cond_fn is not None:
+            out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs)
+        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def p_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model.
+        :param model: the model module.
+        :param shape: the shape of the samples, (N, C, H, W).
+        :param noise: if specified, the noise from the encoder to sample.
+                      Should be of the same shape as `shape`.
+        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param device: if specified, the device to create the samples on.
+                       If not specified, use a model parameter's device.
+        :param progress: if True, show a tqdm progress bar.
+        :return: a non-differentiable batch of samples.
+        """
+        final = None
+        for sample in self.p_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+        ):
+            final = sample
+        return final["sample"]
+
+    def p_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model and yield intermediate samples from
+        each timestep of diffusion.
+        Arguments are the same as p_sample_loop().
+        Returns a generator over dicts, where each dict is the return value of
+        p_sample().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.p_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                )
+                yield out
+                img = out["sample"]
+
+    def ddim_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t-1} from the model using DDIM.
+        Same usage as p_sample().
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
+        sigma = (
+            eta
+            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
+            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
+        )
+        # Equation 12.
+        noise = th.randn_like(x)
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
+        )
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        sample = mean_pred + nonzero_mask * sigma * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_reverse_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t+1} from the model using DDIM reverse ODE.
+        """
+        assert eta == 0.0, "Reverse ODE only for deterministic path"
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
+            - out["pred_xstart"]
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
+
+        # Equation 12. reversed
+        mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_next) + th.sqrt(1 - alpha_bar_next) * eps
+
+        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Generate samples from the model using DDIM.
+        Same usage as p_sample_loop().
+        """
+        final = None
+        for sample in self.ddim_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+            eta=eta,
+        ):
+            final = sample
+        return final["sample"]
+
+    def ddim_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Use DDIM to sample from the model and yield intermediate samples from
+        each timestep of DDIM.
+        Same usage as p_sample_loop_progressive().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.ddim_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                    eta=eta,
+                )
+                yield out
+                img = out["sample"]
+
+    def _vb_terms_bpd(
+            self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
+    ):
+        """
+        Get a term for the variational lower-bound.
+        The resulting units are bits (rather than nats, as one might expect).
+        This allows for comparison to other papers.
+        :return: a dict with the following keys:
+                 - 'output': a shape [N] tensor of NLLs or KLs.
+                 - 'pred_xstart': the x_0 predictions.
+        """
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+            x_start=x_start, x_t=x_t, t=t
+        )
+        out = self.p_mean_variance(
+            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
+        )
+        kl = normal_kl(
+            true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
+        )
+        kl = mean_flat(kl) / np.log(2.0)
+
+        decoder_nll = -discretized_gaussian_log_likelihood(
+            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
+        )
+        assert decoder_nll.shape == x_start.shape
+        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
+
+        # At the first timestep return the decoder NLL,
+        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
+        output = th.where((t == 0), decoder_nll, kl)
+        return {"output": output, "pred_xstart": out["pred_xstart"]}
+
+    def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
+        """
+        Compute training losses for a single timestep.
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param t: a batch of timestep indices.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param noise: if specified, the specific Gaussian noise to try to remove.
+        :return: a dict with the key "loss" containing a tensor of shape [N].
+                 Some mean or variance settings may also have other keys.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+        if noise is None:
+            noise = th.randn_like(x_start)
+        x_t = self.q_sample(x_start, t, noise=noise)
+
+        terms = {}
+
+        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
+            terms["loss"] = self._vb_terms_bpd(
+                model=model,
+                x_start=x_start,
+                x_t=x_t,
+                t=t,
+                clip_denoised=False,
+                model_kwargs=model_kwargs,
+            )["output"]
+            if self.loss_type == LossType.RESCALED_KL:
+                terms["loss"] *= self.num_timesteps
+        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
+            model_output = model(x_t, t, **model_kwargs)
+
+            if self.model_var_type in [
+                ModelVarType.LEARNED,
+                ModelVarType.LEARNED_RANGE,
+            ]:
+                B, C = x_t.shape[:2]
+                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
+                model_output, model_var_values = th.split(model_output, C, dim=1)
+                # Learn the variance using the variational bound, but don't let
+                # it affect our mean prediction.
+                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
+                terms["vb"] = self._vb_terms_bpd(
+                    model=lambda *args, r=frozen_out: r,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t,
+                    clip_denoised=False,
+                )["output"]
+                if self.loss_type == LossType.RESCALED_MSE:
+                    # Divide by 1000 for equivalence with initial implementation.
+                    # Without a factor of 1/1000, the VB term hurts the MSE term.
+                    terms["vb"] *= self.num_timesteps / 1000.0
+
+            target = {
+                ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
+                    x_start=x_start, x_t=x_t, t=t
+                )[0],
+                ModelMeanType.START_X: x_start,
+                ModelMeanType.EPSILON: noise,
+            }[self.model_mean_type]
+            assert model_output.shape == target.shape == x_start.shape
+            terms["mse"] = mean_flat((target - model_output) ** 2)
+            if "vb" in terms:
+                terms["loss"] = terms["mse"] + terms["vb"]
+            else:
+                terms["loss"] = terms["mse"]
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        return terms
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(
+            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
+        )
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
+        """
+        Compute the entire variational lower-bound, measured in bits-per-dim,
+        as well as other related quantities.
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param clip_denoised: if True, clip denoised samples.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - total_bpd: the total variational lower-bound, per batch element.
+                 - prior_bpd: the prior term in the lower-bound.
+                 - vb: an [N x T] tensor of terms in the lower-bound.
+                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
+                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
+        """
+        device = x_start.device
+        batch_size = x_start.shape[0]
+
+        vb = []
+        xstart_mse = []
+        mse = []
+        for t in list(range(self.num_timesteps))[::-1]:
+            t_batch = th.tensor([t] * batch_size, device=device)
+            noise = th.randn_like(x_start)
+            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
+            # Calculate VLB term at the current timestep
+            with th.no_grad():
+                out = self._vb_terms_bpd(
+                    model,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t_batch,
+                    clip_denoised=clip_denoised,
+                    model_kwargs=model_kwargs,
+                )
+            vb.append(out["output"])
+            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
+            eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
+            mse.append(mean_flat((eps - noise) ** 2))
+
+        vb = th.stack(vb, dim=1)
+        xstart_mse = th.stack(xstart_mse, dim=1)
+        mse = th.stack(mse, dim=1)
+
+        prior_bpd = self._prior_bpd(x_start)
+        total_bpd = vb.sum(dim=1) + prior_bpd
+        return {
+            "total_bpd": total_bpd,
+            "prior_bpd": prior_bpd,
+            "vb": vb,
+            "xstart_mse": xstart_mse,
+            "mse": mse,
+        }
+
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+    """
+    Extract values from a 1-D numpy array for a batch of indices.
+    :param arr: the 1-D numpy array.
+    :param timesteps: a tensor of indices into the array to extract.
+    :param broadcast_shape: a larger shape of K dimensions with the batch
+                            dimension equal to the length of timesteps.
+    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+    """
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res + th.zeros(broadcast_shape, device=timesteps.device)
diff --git a/diffusion/respace.py b/diffusion/respace.py
new file mode 100644
index 0000000000000000000000000000000000000000..12cf0c896b6a0b6c8bbc5bbe9bb49a4e9f1a2d17
--- /dev/null
+++ b/diffusion/respace.py
@@ -0,0 +1,135 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Modified from OpenAI's diffusion repos
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+import numpy as np
+import torch as th
+
+from .gaussian_diffusion import GaussianDiffusion
+
+
+def space_timesteps(num_timesteps, section_counts):
+    """
+    Create a list of timesteps to use from an original diffusion process,
+    given the number of timesteps we want to take from equally-sized portions
+    of the original process.
+    For example, if there's 300 timesteps and the section counts are [10,15,20]
+    then the first 100 timesteps are strided to be 10 timesteps, the second 100
+    are strided to be 15 timesteps, and the final 100 are strided to be 20.
+    If the stride is a string starting with "ddim", then the fixed striding
+    from the DDIM paper is used, and only one section is allowed.
+    :param num_timesteps: the number of diffusion steps in the original
+                          process to divide up.
+    :param section_counts: either a list of numbers, or a string containing
+                           comma-separated numbers, indicating the step count
+                           per section. As a special case, use "ddimN" where N
+                           is a number of steps to use the striding from the
+                           DDIM paper.
+    :return: a set of diffusion steps from the original process to use.
+    """
+    if isinstance(section_counts, str):
+        if section_counts.startswith("ddim"):
+            desired_count = int(section_counts[len("ddim") :])
+            for i in range(1, num_timesteps):
+                if len(range(0, num_timesteps, i)) == desired_count:
+                    return set(range(0, num_timesteps, i))
+            raise ValueError(
+                f"cannot create exactly {num_timesteps} steps with an integer stride"
+            )
+        section_counts = [int(x) for x in section_counts.split(",")]
+    size_per = num_timesteps // len(section_counts)
+    extra = num_timesteps % len(section_counts)
+    start_idx = 0
+    all_steps = []
+    for i, section_count in enumerate(section_counts):
+        size = size_per + (1 if i < extra else 0)
+        if size < section_count:
+            raise ValueError(
+                f"cannot divide section of {size} steps into {section_count}"
+            )
+        if section_count <= 1:
+            frac_stride = 1
+        else:
+            frac_stride = (size - 1) / (section_count - 1)
+        cur_idx = 0.0
+        taken_steps = []
+        for _ in range(section_count):
+            taken_steps.append(start_idx + round(cur_idx))
+            cur_idx += frac_stride
+        all_steps += taken_steps
+        start_idx += size
+    return set(all_steps)
+
+
+class SpacedDiffusion(GaussianDiffusion):
+    """
+    A diffusion process which can skip steps in a base diffusion process.
+    :param use_timesteps: a collection (sequence or set) of timesteps from the
+                          original diffusion process to retain.
+    :param kwargs: the kwargs to create the base diffusion process.
+    """
+
+    def __init__(self, use_timesteps, **kwargs):
+        self.use_timesteps = set(use_timesteps)
+        self.timestep_map = []
+        self.original_num_steps = len(kwargs["betas"])
+
+        base_diffusion = GaussianDiffusion(**kwargs)  # pylint: disable=missing-kwoa
+        last_alpha_cumprod = 1.0
+        new_betas = []
+        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
+            if i in self.use_timesteps:
+                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
+                last_alpha_cumprod = alpha_cumprod
+                self.timestep_map.append(i)
+        kwargs["betas"] = np.array(new_betas)
+        super().__init__(**kwargs)
+
+    def p_mean_variance(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
+
+    def training_losses(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().training_losses(self._wrap_model(model), *args, **kwargs)
+
+    def condition_mean(self, cond_fn, *args, **kwargs):
+        return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def condition_score(self, cond_fn, *args, **kwargs):
+        return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def _wrap_model(self, model):
+        if isinstance(model, _WrappedModel):
+            return model
+        return _WrappedModel(
+            model, self.timestep_map, self.original_num_steps
+        )
+
+    def _scale_timesteps(self, t):
+        # Scaling is done by the wrapped model.
+        return t
+
+
+class _WrappedModel:
+    def __init__(self, model, timestep_map, original_num_steps):
+        self.model = model
+        self.timestep_map = timestep_map
+        # self.rescale_timesteps = rescale_timesteps
+        self.original_num_steps = original_num_steps
+
+    def __call__(self, x, ts, **kwargs):
+        map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
+        new_ts = map_tensor[ts]
+        # if self.rescale_timesteps:
+        #     new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
+        return self.model(x, new_ts, **kwargs)
diff --git a/diffusion/timestep_sampler.py b/diffusion/timestep_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..40accfb3a49dc7ace5a26ab5e5af854de0a5215b
--- /dev/null
+++ b/diffusion/timestep_sampler.py
@@ -0,0 +1,156 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# Modified from OpenAI's diffusion repos
+#     GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
+#     ADM:   https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
+#     IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+
+from abc import ABC, abstractmethod
+
+import numpy as np
+import torch as th
+import torch.distributed as dist
+
+
+def create_named_schedule_sampler(name, diffusion):
+    """
+    Create a ScheduleSampler from a library of pre-defined samplers.
+    :param name: the name of the sampler.
+    :param diffusion: the diffusion object to sample for.
+    """
+    if name == "uniform":
+        return UniformSampler(diffusion)
+    elif name == "loss-second-moment":
+        return LossSecondMomentResampler(diffusion)
+    else:
+        raise NotImplementedError(f"unknown schedule sampler: {name}")
+
+
+class ScheduleSampler(ABC):
+    """
+    A distribution over timesteps in the diffusion process, intended to reduce
+    variance of the objective.
+    By default, samplers perform unbiased importance sampling, in which the
+    objective's mean is unchanged.
+    However, subclasses may override sample() to change how the resampled
+    terms are reweighted, allowing for actual changes in the objective.
+    """
+
+    @abstractmethod
+    def weights(self):
+        """
+        Get a numpy array of weights, one per diffusion step.
+        The weights needn't be normalized, but must be positive.
+        """
+
+    def sample(self, batch_size, device):
+        """
+        Importance-sample timesteps for a batch.
+        :param batch_size: the number of timesteps.
+        :param device: the torch device to save to.
+        :return: a tuple (timesteps, weights):
+                 - timesteps: a tensor of timestep indices.
+                 - weights: a tensor of weights to scale the resulting losses.
+        """
+        w = self.weights()
+        p = w / np.sum(w)
+        indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
+        indices = th.from_numpy(indices_np).long().to(device)
+        weights_np = 1 / (len(p) * p[indices_np])
+        weights = th.from_numpy(weights_np).float().to(device)
+        return indices, weights
+
+
+class UniformSampler(ScheduleSampler):
+    def __init__(self, diffusion):
+        self.diffusion = diffusion
+        self._weights = np.ones([diffusion.num_timesteps])
+
+    def weights(self):
+        return self._weights
+
+
+class LossAwareSampler(ScheduleSampler):
+    def update_with_local_losses(self, local_ts, local_losses):
+        """
+        Update the reweighting using losses from a model.
+        Call this method from each rank with a batch of timesteps and the
+        corresponding losses for each of those timesteps.
+        This method will perform synchronization to make sure all of the ranks
+        maintain the exact same reweighting.
+        :param local_ts: an integer Tensor of timesteps.
+        :param local_losses: a 1D Tensor of losses.
+        """
+        batch_sizes = [
+            th.tensor([0], dtype=th.int32, device=local_ts.device)
+            for _ in range(dist.get_world_size())
+        ]
+        dist.all_gather(
+            batch_sizes,
+            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
+        )
+
+        # Pad all_gather batches to be the maximum batch size.
+        batch_sizes = [x.item() for x in batch_sizes]
+        max_bs = max(batch_sizes)
+
+        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
+        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
+        dist.all_gather(timestep_batches, local_ts)
+        dist.all_gather(loss_batches, local_losses)
+        timesteps = [
+            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
+        ]
+        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
+        self.update_with_all_losses(timesteps, losses)
+
+    @abstractmethod
+    def update_with_all_losses(self, ts, losses):
+        """
+        Update the reweighting using losses from a model.
+        Sub-classes should override this method to update the reweighting
+        using losses from the model.
+        This method directly updates the reweighting without synchronizing
+        between workers. It is called by update_with_local_losses from all
+        ranks with identical arguments. Thus, it should have deterministic
+        behavior to maintain state across workers.
+        :param ts: a list of int timesteps.
+        :param losses: a list of float losses, one per timestep.
+        """
+
+
+class LossSecondMomentResampler(LossAwareSampler):
+    def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
+        self.diffusion = diffusion
+        self.history_per_term = history_per_term
+        self.uniform_prob = uniform_prob
+        self._loss_history = np.zeros(
+            [diffusion.num_timesteps, history_per_term], dtype=np.float64
+        )
+        self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
+
+    def weights(self):
+        if not self._warmed_up():
+            return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
+        weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
+        weights /= np.sum(weights)
+        weights *= 1 - self.uniform_prob
+        weights += self.uniform_prob / len(weights)
+        return weights
+
+    def update_with_all_losses(self, ts, losses):
+        for t, loss in zip(ts, losses):
+            if self._loss_counts[t] == self.history_per_term:
+                # Shift out the oldest loss term.
+                self._loss_history[t, :-1] = self._loss_history[t, 1:]
+                self._loss_history[t, -1] = loss
+            else:
+                self._loss_history[t, self._loss_counts[t]] = loss
+                self._loss_counts[t] += 1
+
+    def _warmed_up(self):
+        return (self._loss_counts == self.history_per_term).all()
diff --git a/git-lfs_3.3.0-1+deb12u1_amd64.deb b/git-lfs_3.3.0-1+deb12u1_amd64.deb
new file mode 100644
index 0000000000000000000000000000000000000000..d671882f817429422ac705e9d4501a67f1262d76
--- /dev/null
+++ b/git-lfs_3.3.0-1+deb12u1_amd64.deb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:633a69f57a01eb3dd8e4bf8e191296793c5305e90e3d4584115fba2815081383
+size 3488932
diff --git a/project_functions.py b/project_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c8700543173ce7604a69d97d5e3cd1291984cf8
--- /dev/null
+++ b/project_functions.py
@@ -0,0 +1,429 @@
+import cv2
+import math
+from scipy.spatial.transform import Rotation as R
+from pathlib import Path
+import matplotlib.pyplot as plt
+import numpy as np
+
+class Quaternionr:
+    w_val = 0.0
+    x_val = 0.0
+    y_val = 0.0
+    z_val = 0.0
+
+    def __init__(self, x_val = 0.0, y_val = 0.0, z_val = 0.0, w_val = 1.0):
+        self.x_val = x_val
+        self.y_val = y_val
+        self.z_val = z_val
+        self.w_val = w_val
+        
+
+def to_quaternion(pitch, roll, yaw):
+    t0 = math.cos(yaw * 0.5)
+    t1 = math.sin(yaw * 0.5)
+    t2 = math.cos(roll * 0.5)
+    t3 = math.sin(roll * 0.5)
+    t4 = math.cos(pitch * 0.5)
+    t5 = math.sin(pitch * 0.5)
+
+    q = Quaternionr()
+    q.w_val = t0 * t2 * t4 + t1 * t3 * t5 #w
+    q.x_val = t0 * t3 * t4 - t1 * t2 * t5 #x
+    q.y_val = t0 * t2 * t5 + t1 * t3 * t4 #y
+    q.z_val = t1 * t2 * t4 - t0 * t3 * t5 #z
+    return q
+
+
+class CoordsConverter:
+    def __init__(self):
+        pass
+    
+    def coords_img2cam(self, p, dep):
+        pass
+    
+    def coords_cam2ego(self, P_cam):
+        pass
+    
+    def coords_ego2world(self, P_ego, ego_pose):
+        pass
+    
+    def coords_world2cam(self, P_w, ego_pose):
+        pass
+
+
+class AirsimCoordsConverter(CoordsConverter):
+    def __init__(self):
+        super().__init__()
+
+        w, h = 512, 512
+        fov = np.pi / 2
+        fx = 0.5 * w / np.tan(fov / 2)
+        fy = fx
+
+        self.K = np.array(
+            [[fx, 0, w / 2],
+             [0, fy, h / 2],
+             [0, 0, 1]]
+        )
+        self.T_ec = np.array(
+            [
+                [0.0, 0, 1, 0],
+                [1.0, 0, 0, 0],
+                [0.0, 1, 0, 0],
+                [0.0, 0, 0, 1]
+            ]
+        )
+
+    def coords_img2cam(self, p, dep):
+        # p: [u, v, 1], dep: depth of p
+        K_inv = np.linalg.inv(self.K)
+        P_cam = K_inv.dot(p * dep)
+
+        return P_cam
+
+    def coords_cam2ego(self, P_cam):
+        # p: [x, y, z, 1]
+        P_ego = self.T_ec.dot(P_cam)
+        return P_ego
+
+    def coords_ego2world(self, P_ego, ego_pose):
+        # P_ego: [x, y, z, 1], ego_pose: [x, y, z, p, r, y]
+        rot_quat = to_quaternion(*ego_pose[3:])
+        rot_mat = R.from_quat([rot_quat.x_val, rot_quat.y_val, rot_quat.z_val, rot_quat.w_val]).as_matrix()
+        shift_mat = np.array(ego_pose[:3])
+
+        trans_mat = np.eye(4)
+        trans_mat[:3, :3] = rot_mat
+        trans_mat[:3, 3] = shift_mat
+
+        P_world = trans_mat.dot(P_ego)
+
+        return P_world
+
+    def coords_world2cam(self, P_w, ego_pose):
+        # P_ego: [x, y, z, 1], ego_pose: [x, y, z, p, r, y]
+        rot_quat = to_quaternion(*ego_pose[3:])
+        rot_mat = R.from_quat([rot_quat.x_val, rot_quat.y_val, rot_quat.z_val, rot_quat.w_val]).as_matrix()
+        shift_mat = np.array(ego_pose[:3])
+
+        T_we = np.eye(4)
+        T_we[:3, :3] = rot_mat
+        T_we[:3, 3] = shift_mat
+
+        T_wc = T_we.dot(self.T_ec)
+        T_cw = np.linalg.inv(T_wc)
+
+        P_cam = T_cw.dot(P_w)
+
+        return P_cam
+
+    def coords_cam2img(self, P_cam):
+        # P_cam: [X, Y, Z, 1]
+        p_img = self.K.dot(P_cam[:3]) / P_cam[2]
+        
+        return p_img
+    
+    def trans_world2cam(self, ego_pose):
+        rot_quat = to_quaternion(*ego_pose[3:])
+        
+        rot_mat = R.from_quat([rot_quat.x_val, rot_quat.y_val, rot_quat.z_val, rot_quat.w_val]).as_matrix()
+        shift_mat = np.array(ego_pose[:3])
+
+        T_we = np.eye(4)
+        T_we[:3, :3] = rot_mat
+        T_we[:3, 3] = shift_mat
+
+        T_wc = T_we.dot(self.T_ec)
+        return T_wc
+
+    def trans_cam2world(self, ego_pose):
+        rot_quat = to_quaternion(*ego_pose[3:])
+        
+        rot_mat = R.from_quat([rot_quat.x_val, rot_quat.y_val, rot_quat.z_val, rot_quat.w_val]).as_matrix()
+        shift_mat = np.array(ego_pose[:3])
+
+        T_we = np.eye(4)
+        T_we[:3, :3] = rot_mat
+        T_we[:3, 3] = shift_mat
+
+        T_wc = T_we.dot(self.T_ec)
+        T_cw = np.linalg.inv(T_wc)
+        
+        return T_cw
+
+    def trans_world2ego(self, ego_pose):
+        rot_quat = to_quaternion(*ego_pose[3:])
+        
+        rot_mat = R.from_quat([rot_quat.x_val, rot_quat.y_val, rot_quat.z_val, rot_quat.w_val]).as_matrix()
+        shift_mat = np.array(ego_pose[:3])
+
+        T_we = np.eye(4)
+        T_we[:3, :3] = rot_mat
+        T_we[:3, 3] = shift_mat
+
+        return T_we
+
+    def trans_ego2world(self, ego_pose):
+        rot_quat = to_quaternion(*ego_pose[3:])
+        
+        rot_mat = R.from_quat([rot_quat.x_val, rot_quat.y_val, rot_quat.z_val, rot_quat.w_val]).as_matrix()
+        shift_mat = np.array(ego_pose[:3])
+
+        T_we = np.eye(4)
+        T_we[:3, :3] = rot_mat
+        T_we[:3, 3] = shift_mat
+
+        T_ew = np.linalg.inv(T_we)
+        return T_ew
+
+
+def reproject_depth_to_other_pose(K, depth_map, rgb_img, pose_src, pose_dst):
+    """
+    输入：
+        K: 内参矩阵 (3, 3)
+        depth_map: 深度图 (H, W)
+        rgb_img: RGB 图像 (H, W, 3)
+        pose_src: 源相机位姿 (4, 4)
+        pose_dst: 目标相机位姿 (4, 4)
+
+    输出：
+        points_3d_dst: (N, 3) 变换后的 3D 点
+        colors: (N, 3) RGB 值
+    """
+    # 打印旋转矩阵
+    # print("[DEBUG] pose_src rotation matrix:", pose_src)
+    # print("[DEBUG] pose_dst rotation matrix:", pose_dst)
+
+    # 提取并打印源和目标的 yaw 角度差异
+    # euler_src = R.from_matrix(pose_src[:3, :3]).as_euler('zyx', degrees=True)
+    # euler_dst = R.from_matrix(pose_dst[:3, :3]).as_euler('zyx', degrees=True)
+    # print("[DEBUG] pose_src euler (yaw, pitch, roll):", euler_src)
+    # print("[DEBUG] pose_dst euler (yaw, pitch, roll):", euler_dst)
+    fx, fy = K[0, 0], K[1, 1]
+    cx, cy = K[0, 2], K[1, 2]
+    H, W = depth_map.shape
+
+    u, v = np.meshgrid(np.arange(W), np.arange(H))
+    u = u.reshape(-1)
+    v = v.reshape(-1)
+    z = depth_map.reshape(-1)
+    valid = z > 0
+
+    u = u[valid]
+    v = v[valid]
+    z = z[valid]
+
+    x = (u - cx) * z / fx
+    y = (v - cy) * z / fy
+    points_cam = np.stack([x, y, z], axis=1)  # shape: (N, 3)
+
+    # 转换到齐次坐标 (N, 4)
+    ones = np.ones((points_cam.shape[0], 1))
+    points_cam_hom = np.concatenate([points_cam, ones], axis=1)
+
+    # 从源位姿 -> 世界坐标
+    points_world = (pose_src @ points_cam_hom.T).T  # shape: (N, 4)
+
+    # 从世界坐标 -> 目标相机坐标
+    points_dst_cam = (np.linalg.inv(pose_dst) @ points_world.T).T  # shape: (N, 4)
+
+    # 去除齐次项
+    points_3d_dst = points_dst_cam[:, :3]
+
+    # 对应颜色
+    colors = rgb_img.reshape(-1, 3)[valid]
+
+    return points_3d_dst, colors
+
+def reproject_depth_to_other_pose_2seq(K, depth_maps, rgb_imgs, poses_src, poses_dst):
+    """
+    批量处理多个视角的重投影
+    
+    参数:
+        K: (3, 3)
+        depth_maps: (H, W)
+        rgb_imgs: (H, W, 3)
+        poses_src: (4, 4)
+        poses_dst: (goal_time_seq_len, 4, 4)
+    """
+    B = poses_dst.shape[0]
+    points_3d_all = []
+    colors_all = []
+
+    for i in range(B):
+        points_3d, colors = reproject_depth_to_other_pose(
+            K,
+            depth_maps,
+            rgb_imgs,
+            poses_src,
+            poses_dst[i]
+        )
+        points_3d_all.append(points_3d)
+        colors_all.append(colors)
+
+    return points_3d_all, colors_all
+
+def project_to_2d_image(K, points_3d, colors, image_size):
+    fx, fy = K[0, 0], K[1, 1]
+    cx, cy = K[0, 2], K[1, 2]
+    H, W = image_size
+
+    # 取出 3D 点
+    x, y, z = points_3d[:, 0], points_3d[:, 1], points_3d[:, 2]
+
+    # 下限限制z
+    z = np.maximum(z, 1e-6) 
+
+    # 计算投影后的像素坐标
+    u = (x * fx / z + cx)
+    v = (y * fy / z + cy)
+    # print(u, v)
+
+    # 过滤在图像边界外的点
+    valid = (u >= 0) & (u < W) & (v >= 0) & (v < H)
+    u = u[valid].astype(np.int32)
+    v = v[valid].astype(np.int32)
+    colors_valid = colors[valid]
+
+    # 创建空白图像
+    image = np.zeros((H, W, 3), dtype=np.uint8)
+    image[v, u] = colors_valid
+
+    return image
+
+def project_to_2d_image_2seq(K, points_3d, colors, image_size):
+    B = len(points_3d)
+    images = []
+
+    for i in range(B):
+        image = project_to_2d_image(K, points_3d[i], colors[i], image_size)
+        images.append(image)
+
+    return np.stack(images, axis=0)  # shape: (B, H, W, 3)
+
+def euler_to_quaternion(yaw, pitch, roll):
+    r = R.from_euler('zyx', [yaw, pitch, roll])
+    return r.as_quat()
+
+def create_pose_matrix(translation, quaternion):
+    rot = R.from_quat(quaternion)
+    rot_mat = rot.as_matrix()  # 3x3
+
+    pose = np.eye(4)
+    pose[:3, :3] = rot_mat
+    pose[:3, 3] = translation
+    return pose
+
+def create_relative_pose(pose_src, delta_translation=(0, 0, 0.1), delta_angle_deg=5.0, axis='y'):
+    """
+    基于 pose_src 创建变换后的 pose_dst
+    """
+    # 旋转矩阵（绕 Y 轴旋转）
+    if axis == 'y':
+        rot = R.from_euler('y', delta_angle_deg, degrees=True).as_matrix()
+    else:
+        raise NotImplementedError("Only Y axis implemented")
+
+    # 构造相对变换矩阵
+    delta_pose = np.eye(4)
+    delta_pose[:3, :3] = rot
+    delta_pose[:3, 3] = np.array(delta_translation)
+
+    # 将相对变换应用到原始位姿上
+    pose_dst = pose_src @ delta_pose
+    return pose_dst
+
+def resize_image_half(rgb_img):
+    original_height, original_width = rgb_img.shape[:2]
+    resized_width = original_width // 2
+    resized_height = original_height // 2
+    resized_img = cv2.resize(rgb_img, (resized_width, resized_height))
+    return resized_img
+
+# seq2seq
+def reproject_depth_to_other_pose_seq2(K, depth_seq, rgb_seq, poses_src_seq, pose_dst):
+    """
+    多帧 → 一张图：复用你已有的单帧函数 reproject_depth_to_other_pose，再用上面的 z-buffer 合成。
+    depth_seq: (T,H,W), rgb_seq: (T,H,W,3), poses_src_seq: (T,4,4), pose_dst: (4,4)
+    """
+    T, H, W = depth_seq.shape
+    pts_list, col_list = [], []
+    for i in range(T):
+        pts_i, col_i = reproject_depth_to_other_pose(K, depth_seq[i], rgb_seq[i], poses_src_seq[i], pose_dst)
+        pts_list.append(pts_i)     # 在目标相机系下的 (Ni,3)
+        col_list.append(col_i)     # (Ni,3)
+
+    points_cam = np.concatenate(pts_list, axis=0)
+    colors     = np.concatenate(col_list, axis=0)
+    return points_cam, colors
+
+def project_to_2d_image_seq2(K, points_cam, colors, image_size):
+    """
+    将目标相机坐标系下的点云 (N,3) 用 z-buffer 投影到一张 (H,W,3) 图像
+    K: (3,3), points_cam: (N,3) [X,Y,Z], colors: (N,3) uint8, image_size: (H,W)
+    """
+    fx, fy = K[0,0], K[1,1]
+    cx, cy = K[0,2], K[1,2]
+    H, W = image_size
+
+    X, Y, Z = points_cam[:,0], points_cam[:,1], points_cam[:,2]
+    front = Z > 1e-6
+    X, Y, Z, colors = X[front], Y[front], Z[front], colors[front]
+
+    u = (X * fx / Z + cx)
+    v = (Y * fy / Z + cy)
+    in_img = (u >= 0) & (u < W) & (v >= 0) & (v < H)
+    u = u[in_img].astype(np.int32)
+    v = v[in_img].astype(np.int32)
+    z = Z[in_img]
+    colors = colors[in_img].astype(np.uint8)
+
+    # z-buffer：同像素保留最近深度
+    lin = v * W + u
+    order = np.argsort(z)           # 近 -> 远
+    lin_sorted = lin[order]
+    _, keep = np.unique(lin_sorted, return_index=True)
+    sel = order[keep]
+
+    img = np.zeros((H, W, 3), dtype=np.uint8)
+    img[v[sel], u[sel]] = colors[sel]
+    return img
+
+
+def reproject_depth_to_other_pose_seq2seq(K, depth_maps, rgb_imgs, poses_src, poses_dst):
+    """
+    批量处理多个视角的重投影
+    
+    参数:
+        K: (3, 3)
+        depth_maps: (H, W)
+        rgb_imgs: (H, W, 3)
+        poses_src: (4, 4)
+        poses_dst: (goal_time_seq_len, 4, 4)
+    """
+    B = poses_dst.shape[0]
+    points_3d_all = []
+    colors_all = []
+
+    for i in range(B):
+        points_3d, colors = reproject_depth_to_other_pose_seq2(
+            K,
+            depth_maps,
+            rgb_imgs,
+            poses_src,
+            poses_dst[i]
+        )
+        points_3d_all.append(points_3d)
+        colors_all.append(colors)
+
+    return points_3d_all, colors_all
+
+def project_to_2d_image_seq2seq(K, points_3d, colors, image_size):
+    B = len(points_3d)
+    images = []
+
+    for i in range(B):
+        image = project_to_2d_image_seq2(K, points_3d[i], colors[i], image_size)
+        images.append(image)
+
+    return np.stack(images, axis=0)  # shape: (B, H, W, 3)
\ No newline at end of file
diff --git a/read_pkl.py b/read_pkl.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce33e8f78c3dc6975ba3c29707ef42e48502fc4f
--- /dev/null
+++ b/read_pkl.py
@@ -0,0 +1,44 @@
+# import pickle
+
+# pkl_path = "/data1/tpz/nwm-main/data_splits/airvln_16/test/rollout_2d.pkl"
+# target = "3JTPR5MT00AIKEFWO8VP6O4YVG35KU_processed"
+
+# with open(pkl_path, "rb") as f:
+#     data = pickle.load(f)
+
+# # 遍历查找
+# found_index = None
+# for i, item in enumerate(data):
+#     if isinstance(item, tuple) and len(item) > 0 and item[0] == target:
+#         found_index = i
+#         break
+
+# if found_index is not None:
+#     print(f"✅ 找到匹配元素：索引 = {found_index}")
+#     print("对应内容:", data[found_index])
+# else:
+#     print("❌ 没找到匹配项:", target)
+import pickle
+
+pkl_path = "/data1/tpz/nwm-main/data_splits/airvln_16/test/navigation_eval_16_2d.pkl"
+
+with open(pkl_path, "rb") as f:
+    data = pickle.load(f)
+
+print("✅ 成功读取:", pkl_path)
+print("数据类型:", type(data))
+
+# 如果是 dict，就打印键
+if isinstance(data, dict):
+    print("\n字典的键:")
+    for key in data.keys():
+        print("  ", key)
+    print("\n示例键对应的值:\n", data[next(iter(data))])
+elif isinstance(data, list):
+    print("\n列表长度:", len(data))
+    print("第一个元素类型:", type(data[0]))
+    for i, item in enumerate(data):
+        print(f"\n第 {i} 个元素内容:\n", item)
+    # print("第一个元素内容示例:\n", data[0])
+else:
+    print("\n内容示例:\n", data)
diff --git a/read_pkl_traj.py b/read_pkl_traj.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9ad3c60e2db6bde51ec972e00382809bac059dd
--- /dev/null
+++ b/read_pkl_traj.py
@@ -0,0 +1,47 @@
+# import pickle
+
+# pkl_path = "/data1/tpz/nwm-main/data_splits/airvln_16/test/rollout_2d.pkl"
+# target = "3JTPR5MT00AIKEFWO8VP6O4YVG35KU_processed"
+
+# with open(pkl_path, "rb") as f:
+#     data = pickle.load(f)
+
+# # 遍历查找
+# found_index = None
+# for i, item in enumerate(data):
+#     if isinstance(item, tuple) and len(item) > 0 and item[0] == target:
+#         found_index = i
+#         break
+
+# if found_index is not None:
+#     print(f"✅ 找到匹配元素：索引 = {found_index}")
+#     print("对应内容:", data[found_index])
+# else:
+#     print("❌ 没找到匹配项:", target)
+import pickle
+import numpy as np
+
+pkl_path = "/data1/tpz/nwm-main/data_splits/airvln_16/test/airvln_16_4_trajectories_2d.pkl"
+
+with open(pkl_path, "rb") as f:
+    data = pickle.load(f)
+
+print("✅ 成功读取:", pkl_path)
+print("数据类型:", type(data))
+
+# 如果是 dict，就打印键
+if isinstance(data, dict):
+    print("\n字典的键:")
+    for key in data.keys():
+        print("  ", key)
+    for key, value in list(data.items()):
+        print(f"value shape: {np.array(value).shape}")
+        print(f"键: {key}\n对应的值:\n", value)
+elif isinstance(data, list):
+    print("\n列表长度:", len(data))
+    print("第一个元素类型:", type(data[0]))
+    for i, item in enumerate(data[:5]):
+        print(f"\n第 {i} 个元素内容:\n", item)
+    # print("第一个元素内容示例:\n", data[0])
+else:
+    print("\n内容示例:\n", data)
diff --git a/read_pkl_v2.py b/read_pkl_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..91471f83259df85a6758d45cd77cd9e6edebfb1f
--- /dev/null
+++ b/read_pkl_v2.py
@@ -0,0 +1,47 @@
+# import pickle
+
+# pkl_path = "/data1/tpz/nwm-main/data_splits/airvln_16/test/rollout_2d.pkl"
+# target = "3JTPR5MT00AIKEFWO8VP6O4YVG35KU_processed"
+
+# with open(pkl_path, "rb") as f:
+#     data = pickle.load(f)
+
+# # 遍历查找
+# found_index = None
+# for i, item in enumerate(data):
+#     if isinstance(item, tuple) and len(item) > 0 and item[0] == target:
+#         found_index = i
+#         break
+
+# if found_index is not None:
+#     print(f"✅ 找到匹配元素：索引 = {found_index}")
+#     print("对应内容:", data[found_index])
+# else:
+#     print("❌ 没找到匹配项:", target)
+import pickle
+import numpy as np
+
+pkl_path = "/data1/tpz/nwm-main/data/airvln_16/3A3KKYU7QBF7NLUJ5LVEB19F5RQMW4_processed/traj_data.pkl"
+
+with open(pkl_path, "rb") as f:
+    data = pickle.load(f)
+
+print("✅ 成功读取:", pkl_path)
+print("数据类型:", type(data))
+
+# 如果是 dict，就打印键
+if isinstance(data, dict):
+    print("\n字典的键:")
+    for key in data.keys():
+        print("  ", key)
+    for key, value in list(data.items()):
+        print(f"value shape: {np.array(value).shape}")
+        print(f"键: {key}\n对应的值:\n", value)
+elif isinstance(data, list):
+    print("\n列表长度:", len(data))
+    print("第一个元素类型:", type(data[0]))
+    for i, item in enumerate(data[:75]):
+        print(f"\n第 {i} 个元素内容:\n", item)
+    # print("第一个元素内容示例:\n", data[0])
+else:
+    print("\n内容示例:\n", data)
diff --git a/recon_v0.log b/recon_v0.log
new file mode 100644
index 0000000000000000000000000000000000000000..a89d850cf319554e67b3e7d82fe3e3c6dfa0beed
--- /dev/null
+++ b/recon_v0.log
@@ -0,0 +1,2753 @@
+[2025-06-05 05:03:09,451] torch.distributed.run: [WARNING] 
+[2025-06-05 05:03:09,451] torch.distributed.run: [WARNING] *****************************************
+[2025-06-05 05:03:09,451] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+[2025-06-05 05:03:09,451] torch.distributed.run: [WARNING] *****************************************
+[2025-06-05 05:03:12] Experiment directory created at logs/nwm_cdit_recon
+[2025-06-05 05:03:24] CDiT Parameters: 1,014,614,816
+[2025-06-05 05:03:25] Dataset contains 132,929 images
+[2025-06-05 05:03:25] Training for 300 epochs...
+[2025-06-05 05:03:25] Beginning epoch 0...
+[2025-06-05 05:04:28] (step=0000100) Train Loss: 0.3123, Train Steps/Sec: 1.60, Samples/Sec: 76.61
+[2025-06-05 05:05:29] (step=0000200) Train Loss: 0.1959, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 05:06:30] (step=0000300) Train Loss: 0.1962, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 05:07:31] (step=0000400) Train Loss: 0.1799, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 05:08:32] (step=0000500) Train Loss: 0.1841, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 05:09:33] (step=0000600) Train Loss: 0.1847, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 05:10:34] (step=0000700) Train Loss: 0.1777, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 05:11:35] (step=0000800) Train Loss: 0.1724, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 05:12:36] (step=0000900) Train Loss: 0.1767, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 05:13:37] (step=0001000) Train Loss: 0.1808, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 05:14:38] (step=0001100) Train Loss: 0.1823, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 05:15:39] (step=0001200) Train Loss: 0.1691, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 05:16:40] (step=0001300) Train Loss: 0.1652, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 05:17:41] (step=0001400) Train Loss: 0.1636, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 05:18:42] (step=0001500) Train Loss: 0.1732, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 05:19:43] (step=0001600) Train Loss: 0.1647, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 05:20:44] (step=0001700) Train Loss: 0.1685, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 05:21:45] (step=0001800) Train Loss: 0.1741, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 05:22:46] (step=0001900) Train Loss: 0.1701, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 05:23:47] (step=0002000) Train Loss: 0.1606, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 05:24:02] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 05:25:03] (step=0002100) Train Loss: 0.1617, Train Steps/Sec: 1.32, Samples/Sec: 63.32
+[2025-06-05 05:26:04] (step=0002200) Train Loss: 0.1680, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 05:27:05] (step=0002300) Train Loss: 0.1779, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 05:28:06] (step=0002400) Train Loss: 0.1591, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 05:29:07] (step=0002500) Train Loss: 0.1554, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 05:30:08] (step=0002600) Train Loss: 0.1643, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 05:31:09] (step=0002700) Train Loss: 0.1684, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 05:32:10] (step=0002800) Train Loss: 0.1681, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 05:33:11] (step=0002900) Train Loss: 0.1629, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 05:34:12] (step=0003000) Train Loss: 0.1586, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 05:35:13] (step=0003100) Train Loss: 0.1638, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 05:36:14] (step=0003200) Train Loss: 0.1600, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 05:37:15] (step=0003300) Train Loss: 0.1558, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 05:38:17] (step=0003400) Train Loss: 0.1570, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 05:39:18] (step=0003500) Train Loss: 0.1618, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 05:40:19] (step=0003600) Train Loss: 0.1601, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 05:41:20] (step=0003700) Train Loss: 0.1518, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 05:42:21] (step=0003800) Train Loss: 0.1565, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 05:43:22] (step=0003900) Train Loss: 0.1591, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 05:44:23] (step=0004000) Train Loss: 0.1554, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 05:44:39] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 05:45:40] (step=0004100) Train Loss: 0.1534, Train Steps/Sec: 1.30, Samples/Sec: 62.17
+[2025-06-05 05:46:41] (step=0004200) Train Loss: 0.1592, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 05:47:42] (step=0004300) Train Loss: 0.1700, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 05:48:43] (step=0004400) Train Loss: 0.1535, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 05:49:44] (step=0004500) Train Loss: 0.1556, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 05:50:45] (step=0004600) Train Loss: 0.1610, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 05:51:46] (step=0004700) Train Loss: 0.1502, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 05:52:47] (step=0004800) Train Loss: 0.1483, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 05:53:48] (step=0004900) Train Loss: 0.1535, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 05:54:49] (step=0005000) Train Loss: 0.1492, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 05:55:50] (step=0005100) Train Loss: 0.1517, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 05:56:51] (step=0005200) Train Loss: 0.1617, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 05:57:53] (step=0005300) Train Loss: 0.1467, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 05:58:54] (step=0005400) Train Loss: 0.1586, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 05:59:55] (step=0005500) Train Loss: 0.1461, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 06:00:56] (step=0005600) Train Loss: 0.1505, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 06:01:57] (step=0005700) Train Loss: 0.1462, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 06:02:58] (step=0005800) Train Loss: 0.1540, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 06:03:59] (step=0005900) Train Loss: 0.1530, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 06:05:00] (step=0006000) Train Loss: 0.1502, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 06:05:16] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 06:06:17] (step=0006100) Train Loss: 0.1467, Train Steps/Sec: 1.30, Samples/Sec: 62.29
+[2025-06-05 06:07:18] (step=0006200) Train Loss: 0.1445, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 06:08:19] (step=0006300) Train Loss: 0.1487, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 06:09:21] (step=0006400) Train Loss: 0.1514, Train Steps/Sec: 1.62, Samples/Sec: 77.96
+[2025-06-05 06:10:21] (step=0006500) Train Loss: 0.1494, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-05 06:11:22] (step=0006600) Train Loss: 0.1555, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-05 06:12:24] (step=0006700) Train Loss: 0.1440, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 06:13:25] (step=0006800) Train Loss: 0.1441, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 06:14:26] (step=0006900) Train Loss: 0.1419, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 06:15:27] (step=0007000) Train Loss: 0.1399, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 06:16:28] (step=0007100) Train Loss: 0.1446, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 06:17:29] (step=0007200) Train Loss: 0.1539, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 06:18:30] (step=0007300) Train Loss: 0.1457, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 06:19:31] (step=0007400) Train Loss: 0.1521, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 06:20:32] (step=0007500) Train Loss: 0.1410, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 06:21:34] (step=0007600) Train Loss: 0.1445, Train Steps/Sec: 1.61, Samples/Sec: 77.14
+[2025-06-05 06:22:35] (step=0007700) Train Loss: 0.1473, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 06:23:36] (step=0007800) Train Loss: 0.1509, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 06:24:37] (step=0007900) Train Loss: 0.1474, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 06:25:38] (step=0008000) Train Loss: 0.1366, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 06:25:54] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 06:26:55] (step=0008100) Train Loss: 0.1462, Train Steps/Sec: 1.30, Samples/Sec: 62.22
+[2025-06-05 06:27:56] (step=0008200) Train Loss: 0.1438, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 06:28:57] (step=0008300) Train Loss: 0.1486, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 06:29:58] (step=0008400) Train Loss: 0.1455, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 06:30:59] (step=0008500) Train Loss: 0.1463, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-05 06:32:00] (step=0008600) Train Loss: 0.1452, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 06:33:01] (step=0008700) Train Loss: 0.1460, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 06:34:02] (step=0008800) Train Loss: 0.1464, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 06:35:03] (step=0008900) Train Loss: 0.1438, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 06:36:04] (step=0009000) Train Loss: 0.1474, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-05 06:37:05] (step=0009100) Train Loss: 0.1452, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 06:38:06] (step=0009200) Train Loss: 0.1445, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 06:39:07] (step=0009300) Train Loss: 0.1448, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-05 06:40:08] (step=0009400) Train Loss: 0.1457, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 06:41:09] (step=0009500) Train Loss: 0.1438, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 06:42:10] (step=0009600) Train Loss: 0.1394, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-05 06:43:11] (step=0009700) Train Loss: 0.1356, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 06:44:12] (step=0009800) Train Loss: 0.1417, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 06:45:13] (step=0009900) Train Loss: 0.1386, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-05 06:46:14] (step=0010000) Train Loss: 0.1453, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+[2025-06-05 06:46:31] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+[2025-06-05 06:46:35] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 06:48:54] (step=0010000) Perceptual Loss: 0.8338, Eval Time: 143.00
+[2025-06-05 06:49:55] (step=0010100) Train Loss: 0.1429, Train Steps/Sec: 0.45, Samples/Sec: 21.73
+[2025-06-05 06:50:56] (step=0010200) Train Loss: 0.1415, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 06:51:58] (step=0010300) Train Loss: 0.1452, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 06:52:59] (step=0010400) Train Loss: 0.1437, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 06:54:00] (step=0010500) Train Loss: 0.1459, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 06:55:01] (step=0010600) Train Loss: 0.1498, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 06:56:02] (step=0010700) Train Loss: 0.1431, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 06:57:03] (step=0010800) Train Loss: 0.1415, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 06:58:04] (step=0010900) Train Loss: 0.1383, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 06:59:05] (step=0011000) Train Loss: 0.1367, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 06:59:52] Beginning epoch 1...
+[2025-06-05 07:00:07] (step=0011100) Train Loss: 0.1376, Train Steps/Sec: 1.62, Samples/Sec: 77.94
+[2025-06-05 07:01:08] (step=0011200) Train Loss: 0.1417, Train Steps/Sec: 1.63, Samples/Sec: 78.34
+[2025-06-05 07:02:09] (step=0011300) Train Loss: 0.1380, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 07:03:10] (step=0011400) Train Loss: 0.1424, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 07:04:12] (step=0011500) Train Loss: 0.1397, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-05 07:05:13] (step=0011600) Train Loss: 0.1383, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 07:06:14] (step=0011700) Train Loss: 0.1411, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 07:07:15] (step=0011800) Train Loss: 0.1453, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 07:08:16] (step=0011900) Train Loss: 0.1374, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 07:09:17] (step=0012000) Train Loss: 0.1397, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 07:09:31] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 07:10:32] (step=0012100) Train Loss: 0.1406, Train Steps/Sec: 1.35, Samples/Sec: 64.57
+[2025-06-05 07:11:33] (step=0012200) Train Loss: 0.1382, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 07:12:34] (step=0012300) Train Loss: 0.1416, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 07:13:35] (step=0012400) Train Loss: 0.1402, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-05 07:14:36] (step=0012500) Train Loss: 0.1442, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 07:15:38] (step=0012600) Train Loss: 0.1391, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 07:16:39] (step=0012700) Train Loss: 0.1325, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 07:17:40] (step=0012800) Train Loss: 0.1361, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 07:18:41] (step=0012900) Train Loss: 0.1360, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 07:19:42] (step=0013000) Train Loss: 0.1444, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 07:20:43] (step=0013100) Train Loss: 0.1379, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 07:21:44] (step=0013200) Train Loss: 0.1430, Train Steps/Sec: 1.63, Samples/Sec: 78.32
+[2025-06-05 07:22:45] (step=0013300) Train Loss: 0.1436, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 07:23:46] (step=0013400) Train Loss: 0.1373, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 07:24:48] (step=0013500) Train Loss: 0.1440, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 07:25:49] (step=0013600) Train Loss: 0.1357, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 07:26:50] (step=0013700) Train Loss: 0.1396, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 07:27:51] (step=0013800) Train Loss: 0.1421, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 07:28:52] (step=0013900) Train Loss: 0.1410, Train Steps/Sec: 1.63, Samples/Sec: 78.30
+[2025-06-05 07:29:53] (step=0014000) Train Loss: 0.1437, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 07:30:08] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 07:31:09] (step=0014100) Train Loss: 0.1378, Train Steps/Sec: 1.33, Samples/Sec: 63.67
+[2025-06-05 07:32:10] (step=0014200) Train Loss: 0.1367, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 07:33:11] (step=0014300) Train Loss: 0.1412, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 07:34:12] (step=0014400) Train Loss: 0.1398, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 07:35:13] (step=0014500) Train Loss: 0.1394, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 07:36:14] (step=0014600) Train Loss: 0.1460, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 07:37:15] (step=0014700) Train Loss: 0.1370, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 07:38:16] (step=0014800) Train Loss: 0.1399, Train Steps/Sec: 1.64, Samples/Sec: 78.87
+[2025-06-05 07:39:17] (step=0014900) Train Loss: 0.1370, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 07:40:18] (step=0015000) Train Loss: 0.1405, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 07:41:20] (step=0015100) Train Loss: 0.1439, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 07:42:21] (step=0015200) Train Loss: 0.1335, Train Steps/Sec: 1.63, Samples/Sec: 78.30
+[2025-06-05 07:43:22] (step=0015300) Train Loss: 0.1411, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 07:44:23] (step=0015400) Train Loss: 0.1401, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 07:45:24] (step=0015500) Train Loss: 0.1324, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 07:46:25] (step=0015600) Train Loss: 0.1309, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 07:47:27] (step=0015700) Train Loss: 0.1447, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 07:48:28] (step=0015800) Train Loss: 0.1374, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 07:49:29] (step=0015900) Train Loss: 0.1363, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 07:50:30] (step=0016000) Train Loss: 0.1373, Train Steps/Sec: 1.63, Samples/Sec: 78.36
+[2025-06-05 07:50:45] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 07:51:46] (step=0016100) Train Loss: 0.1386, Train Steps/Sec: 1.31, Samples/Sec: 63.02
+[2025-06-05 07:52:47] (step=0016200) Train Loss: 0.1337, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 07:53:49] (step=0016300) Train Loss: 0.1385, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 07:54:50] (step=0016400) Train Loss: 0.1338, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 07:55:51] (step=0016500) Train Loss: 0.1369, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 07:56:52] (step=0016600) Train Loss: 0.1416, Train Steps/Sec: 1.63, Samples/Sec: 78.28
+[2025-06-05 07:57:53] (step=0016700) Train Loss: 0.1378, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 07:58:55] (step=0016800) Train Loss: 0.1326, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 07:59:56] (step=0016900) Train Loss: 0.1324, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 08:00:57] (step=0017000) Train Loss: 0.1373, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 08:01:58] (step=0017100) Train Loss: 0.1350, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 08:02:59] (step=0017200) Train Loss: 0.1351, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 08:04:00] (step=0017300) Train Loss: 0.1349, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 08:05:01] (step=0017400) Train Loss: 0.1390, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 08:06:03] (step=0017500) Train Loss: 0.1302, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 08:07:04] (step=0017600) Train Loss: 0.1289, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 08:08:05] (step=0017700) Train Loss: 0.1283, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 08:09:06] (step=0017800) Train Loss: 0.1356, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 08:10:07] (step=0017900) Train Loss: 0.1275, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 08:11:08] (step=0018000) Train Loss: 0.1300, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 08:11:22] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 08:12:23] (step=0018100) Train Loss: 0.1291, Train Steps/Sec: 1.33, Samples/Sec: 63.77
+[2025-06-05 08:13:24] (step=0018200) Train Loss: 0.1344, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 08:14:26] (step=0018300) Train Loss: 0.1355, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 08:15:27] (step=0018400) Train Loss: 0.1291, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 08:16:28] (step=0018500) Train Loss: 0.1334, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 08:17:29] (step=0018600) Train Loss: 0.1381, Train Steps/Sec: 1.63, Samples/Sec: 78.36
+[2025-06-05 08:18:30] (step=0018700) Train Loss: 0.1296, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 08:19:31] (step=0018800) Train Loss: 0.1347, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 08:20:32] (step=0018900) Train Loss: 0.1337, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 08:21:33] (step=0019000) Train Loss: 0.1390, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 08:22:34] (step=0019100) Train Loss: 0.1283, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 08:23:36] (step=0019200) Train Loss: 0.1356, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 08:24:37] (step=0019300) Train Loss: 0.1367, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 08:25:38] (step=0019400) Train Loss: 0.1361, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 08:26:39] (step=0019500) Train Loss: 0.1414, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 08:27:40] (step=0019600) Train Loss: 0.1368, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 08:28:41] (step=0019700) Train Loss: 0.1282, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 08:29:42] (step=0019800) Train Loss: 0.1348, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 08:30:43] (step=0019900) Train Loss: 0.1392, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 08:31:44] (step=0020000) Train Loss: 0.1337, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 08:32:15] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0020000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 08:32:18] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 08:34:37] (step=0020000) Perceptual Loss: 0.6465, Eval Time: 142.29
+[2025-06-05 08:35:38] (step=0020100) Train Loss: 0.1371, Train Steps/Sec: 0.43, Samples/Sec: 20.53
+[2025-06-05 08:36:39] (step=0020200) Train Loss: 0.1315, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 08:37:40] (step=0020300) Train Loss: 0.1267, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 08:38:42] (step=0020400) Train Loss: 0.1346, Train Steps/Sec: 1.63, Samples/Sec: 78.40
+[2025-06-05 08:39:43] (step=0020500) Train Loss: 0.1283, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-05 08:40:44] (step=0020600) Train Loss: 0.1373, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 08:41:45] (step=0020700) Train Loss: 0.1308, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 08:42:46] (step=0020800) Train Loss: 0.1326, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 08:43:47] (step=0020900) Train Loss: 0.1381, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 08:44:49] (step=0021000) Train Loss: 0.1352, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 08:45:50] (step=0021100) Train Loss: 0.1292, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 08:46:51] (step=0021200) Train Loss: 0.1362, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 08:47:52] (step=0021300) Train Loss: 0.1354, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 08:48:53] (step=0021400) Train Loss: 0.1311, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 08:49:54] (step=0021500) Train Loss: 0.1339, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 08:50:55] (step=0021600) Train Loss: 0.1341, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 08:51:56] (step=0021700) Train Loss: 0.1332, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-05 08:52:58] (step=0021800) Train Loss: 0.1273, Train Steps/Sec: 1.63, Samples/Sec: 78.23
+[2025-06-05 08:53:59] (step=0021900) Train Loss: 0.1314, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 08:55:00] (step=0022000) Train Loss: 0.1330, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 08:55:15] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 08:56:16] (step=0022100) Train Loss: 0.1339, Train Steps/Sec: 1.33, Samples/Sec: 63.71
+[2025-06-05 08:56:49] Beginning epoch 2...
+[2025-06-05 08:57:17] (step=0022200) Train Loss: 0.1278, Train Steps/Sec: 1.63, Samples/Sec: 78.18
+[2025-06-05 08:58:18] (step=0022300) Train Loss: 0.1329, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 08:59:19] (step=0022400) Train Loss: 0.1329, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 09:00:20] (step=0022500) Train Loss: 0.1338, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 09:01:22] (step=0022600) Train Loss: 0.1321, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 09:02:23] (step=0022700) Train Loss: 0.1306, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 09:03:24] (step=0022800) Train Loss: 0.1267, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 09:04:25] (step=0022900) Train Loss: 0.1273, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 09:05:27] (step=0023000) Train Loss: 0.1309, Train Steps/Sec: 1.60, Samples/Sec: 76.72
+[2025-06-05 09:06:28] (step=0023100) Train Loss: 0.1286, Train Steps/Sec: 1.64, Samples/Sec: 78.87
+[2025-06-05 09:07:30] (step=0023200) Train Loss: 0.1362, Train Steps/Sec: 1.63, Samples/Sec: 78.06
+[2025-06-05 09:08:31] (step=0023300) Train Loss: 0.1316, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 09:09:32] (step=0023400) Train Loss: 0.1352, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 09:10:33] (step=0023500) Train Loss: 0.1254, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 09:11:34] (step=0023600) Train Loss: 0.1317, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 09:12:35] (step=0023700) Train Loss: 0.1321, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 09:13:36] (step=0023800) Train Loss: 0.1396, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 09:14:38] (step=0023900) Train Loss: 0.1278, Train Steps/Sec: 1.63, Samples/Sec: 78.13
+[2025-06-05 09:15:39] (step=0024000) Train Loss: 0.1334, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 09:15:53] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 09:16:54] (step=0024100) Train Loss: 0.1331, Train Steps/Sec: 1.33, Samples/Sec: 63.87
+[2025-06-05 09:17:55] (step=0024200) Train Loss: 0.1306, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 09:18:56] (step=0024300) Train Loss: 0.1279, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 09:19:57] (step=0024400) Train Loss: 0.1300, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 09:20:59] (step=0024500) Train Loss: 0.1298, Train Steps/Sec: 1.63, Samples/Sec: 78.28
+[2025-06-05 09:22:00] (step=0024600) Train Loss: 0.1344, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 09:23:01] (step=0024700) Train Loss: 0.1282, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 09:24:02] (step=0024800) Train Loss: 0.1339, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 09:25:03] (step=0024900) Train Loss: 0.1290, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 09:26:04] (step=0025000) Train Loss: 0.1310, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 09:27:05] (step=0025100) Train Loss: 0.1278, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 09:28:10] (step=0025200) Train Loss: 0.1274, Train Steps/Sec: 1.56, Samples/Sec: 74.71
+[2025-06-05 09:29:11] (step=0025300) Train Loss: 0.1309, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 09:30:12] (step=0025400) Train Loss: 0.1286, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 09:31:13] (step=0025500) Train Loss: 0.1311, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 09:32:14] (step=0025600) Train Loss: 0.1325, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 09:33:15] (step=0025700) Train Loss: 0.1292, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 09:34:16] (step=0025800) Train Loss: 0.1263, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 09:35:17] (step=0025900) Train Loss: 0.1271, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 09:36:18] (step=0026000) Train Loss: 0.1288, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 09:36:33] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 09:37:34] (step=0026100) Train Loss: 0.1305, Train Steps/Sec: 1.31, Samples/Sec: 62.76
+[2025-06-05 09:38:35] (step=0026200) Train Loss: 0.1290, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 09:39:36] (step=0026300) Train Loss: 0.1299, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 09:40:37] (step=0026400) Train Loss: 0.1322, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 09:41:39] (step=0026500) Train Loss: 0.1381, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 09:42:40] (step=0026600) Train Loss: 0.1291, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 09:43:41] (step=0026700) Train Loss: 0.1254, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 09:44:42] (step=0026800) Train Loss: 0.1287, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 09:45:43] (step=0026900) Train Loss: 0.1273, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 09:46:44] (step=0027000) Train Loss: 0.1278, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 09:47:45] (step=0027100) Train Loss: 0.1297, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 09:48:46] (step=0027200) Train Loss: 0.1314, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 09:49:47] (step=0027300) Train Loss: 0.1322, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-05 09:50:48] (step=0027400) Train Loss: 0.1280, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-05 09:51:49] (step=0027500) Train Loss: 0.1276, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 09:52:50] (step=0027600) Train Loss: 0.1294, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 09:53:53] (step=0027700) Train Loss: 0.1270, Train Steps/Sec: 1.59, Samples/Sec: 76.22
+[2025-06-05 09:54:54] (step=0027800) Train Loss: 0.1270, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 09:55:55] (step=0027900) Train Loss: 0.1273, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-05 09:56:56] (step=0028000) Train Loss: 0.1257, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 09:57:10] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 09:58:11] (step=0028100) Train Loss: 0.1271, Train Steps/Sec: 1.34, Samples/Sec: 64.16
+[2025-06-05 09:59:12] (step=0028200) Train Loss: 0.1297, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 10:00:13] (step=0028300) Train Loss: 0.1301, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 10:01:14] (step=0028400) Train Loss: 0.1255, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 10:02:15] (step=0028500) Train Loss: 0.1271, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 10:03:16] (step=0028600) Train Loss: 0.1271, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 10:04:17] (step=0028700) Train Loss: 0.1348, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 10:05:18] (step=0028800) Train Loss: 0.1262, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 10:06:19] (step=0028900) Train Loss: 0.1242, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 10:07:20] (step=0029000) Train Loss: 0.1257, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 10:08:21] (step=0029100) Train Loss: 0.1301, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 10:09:22] (step=0029200) Train Loss: 0.1223, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 10:10:23] (step=0029300) Train Loss: 0.1297, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 10:11:24] (step=0029400) Train Loss: 0.1219, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 10:12:25] (step=0029500) Train Loss: 0.1301, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 10:13:26] (step=0029600) Train Loss: 0.1283, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 10:14:27] (step=0029700) Train Loss: 0.1278, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 10:15:28] (step=0029800) Train Loss: 0.1270, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 10:16:29] (step=0029900) Train Loss: 0.1266, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 10:17:30] (step=0030000) Train Loss: 0.1238, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 10:17:44] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 10:17:47] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 10:20:06] (step=0030000) Perceptual Loss: 0.3424, Eval Time: 142.30
+[2025-06-05 10:21:07] (step=0030100) Train Loss: 0.1326, Train Steps/Sec: 0.46, Samples/Sec: 22.14
+[2025-06-05 10:22:08] (step=0030200) Train Loss: 0.1337, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 10:23:09] (step=0030300) Train Loss: 0.1287, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-05 10:24:10] (step=0030400) Train Loss: 0.1274, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 10:25:11] (step=0030500) Train Loss: 0.1275, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 10:26:12] (step=0030600) Train Loss: 0.1293, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 10:27:13] (step=0030700) Train Loss: 0.1299, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 10:28:14] (step=0030800) Train Loss: 0.1240, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 10:29:15] (step=0030900) Train Loss: 0.1295, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 10:30:16] (step=0031000) Train Loss: 0.1308, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 10:31:18] (step=0031100) Train Loss: 0.1303, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 10:32:21] (step=0031200) Train Loss: 0.1333, Train Steps/Sec: 1.57, Samples/Sec: 75.38
+[2025-06-05 10:33:22] (step=0031300) Train Loss: 0.1297, Train Steps/Sec: 1.65, Samples/Sec: 78.99
+[2025-06-05 10:34:23] (step=0031400) Train Loss: 0.1232, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-05 10:35:24] (step=0031500) Train Loss: 0.1245, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 10:36:25] (step=0031600) Train Loss: 0.1244, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 10:37:26] (step=0031700) Train Loss: 0.1238, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 10:38:28] (step=0031800) Train Loss: 0.1271, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 10:39:29] (step=0031900) Train Loss: 0.1293, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 10:40:30] (step=0032000) Train Loss: 0.1295, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 10:40:44] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 10:41:45] (step=0032100) Train Loss: 0.1316, Train Steps/Sec: 1.33, Samples/Sec: 63.85
+[2025-06-05 10:42:46] (step=0032200) Train Loss: 0.1297, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 10:43:47] (step=0032300) Train Loss: 0.1323, Train Steps/Sec: 1.63, Samples/Sec: 78.40
+[2025-06-05 10:44:48] (step=0032400) Train Loss: 0.1246, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 10:45:50] (step=0032500) Train Loss: 0.1217, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 10:46:51] (step=0032600) Train Loss: 0.1245, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 10:47:52] (step=0032700) Train Loss: 0.1322, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 10:48:53] (step=0032800) Train Loss: 0.1253, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 10:49:54] (step=0032900) Train Loss: 0.1256, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 10:50:55] (step=0033000) Train Loss: 0.1241, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 10:51:56] (step=0033100) Train Loss: 0.1310, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 10:52:58] (step=0033200) Train Loss: 0.1277, Train Steps/Sec: 1.63, Samples/Sec: 78.31
+[2025-06-05 10:53:17] Beginning epoch 3...
+[2025-06-05 10:53:59] (step=0033300) Train Loss: 0.1301, Train Steps/Sec: 1.63, Samples/Sec: 78.18
+[2025-06-05 10:55:00] (step=0033400) Train Loss: 0.1289, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 10:56:01] (step=0033500) Train Loss: 0.1285, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 10:57:02] (step=0033600) Train Loss: 0.1318, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 10:58:03] (step=0033700) Train Loss: 0.1222, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 10:59:05] (step=0033800) Train Loss: 0.1295, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 11:00:06] (step=0033900) Train Loss: 0.1206, Train Steps/Sec: 1.63, Samples/Sec: 78.26
+[2025-06-05 11:01:07] (step=0034000) Train Loss: 0.1295, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 11:01:23] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 11:02:24] (step=0034100) Train Loss: 0.1168, Train Steps/Sec: 1.30, Samples/Sec: 62.38
+[2025-06-05 11:03:25] (step=0034200) Train Loss: 0.1303, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 11:04:26] (step=0034300) Train Loss: 0.1231, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 11:05:27] (step=0034400) Train Loss: 0.1247, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 11:06:29] (step=0034500) Train Loss: 0.1223, Train Steps/Sec: 1.63, Samples/Sec: 78.25
+[2025-06-05 11:07:30] (step=0034600) Train Loss: 0.1252, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 11:08:31] (step=0034700) Train Loss: 0.1266, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 11:09:32] (step=0034800) Train Loss: 0.1289, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 11:10:33] (step=0034900) Train Loss: 0.1247, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 11:11:34] (step=0035000) Train Loss: 0.1243, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 11:12:36] (step=0035100) Train Loss: 0.1239, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 11:13:37] (step=0035200) Train Loss: 0.1178, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 11:14:38] (step=0035300) Train Loss: 0.1256, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 11:15:39] (step=0035400) Train Loss: 0.1211, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 11:16:40] (step=0035500) Train Loss: 0.1319, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 11:17:41] (step=0035600) Train Loss: 0.1276, Train Steps/Sec: 1.63, Samples/Sec: 78.40
+[2025-06-05 11:18:42] (step=0035700) Train Loss: 0.1244, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 11:19:43] (step=0035800) Train Loss: 0.1221, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 11:20:45] (step=0035900) Train Loss: 0.1183, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 11:21:46] (step=0036000) Train Loss: 0.1215, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 11:22:00] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 11:23:01] (step=0036100) Train Loss: 0.1319, Train Steps/Sec: 1.33, Samples/Sec: 63.92
+[2025-06-05 11:24:02] (step=0036200) Train Loss: 0.1171, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 11:25:03] (step=0036300) Train Loss: 0.1332, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 11:26:04] (step=0036400) Train Loss: 0.1208, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 11:27:05] (step=0036500) Train Loss: 0.1216, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 11:28:07] (step=0036600) Train Loss: 0.1298, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 11:29:08] (step=0036700) Train Loss: 0.1261, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 11:30:09] (step=0036800) Train Loss: 0.1229, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 11:31:10] (step=0036900) Train Loss: 0.1317, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 11:32:11] (step=0037000) Train Loss: 0.1261, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 11:33:12] (step=0037100) Train Loss: 0.1253, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 11:34:13] (step=0037200) Train Loss: 0.1278, Train Steps/Sec: 1.63, Samples/Sec: 78.40
+[2025-06-05 11:35:14] (step=0037300) Train Loss: 0.1279, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 11:36:15] (step=0037400) Train Loss: 0.1258, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 11:37:16] (step=0037500) Train Loss: 0.1279, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 11:38:18] (step=0037600) Train Loss: 0.1269, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-05 11:39:19] (step=0037700) Train Loss: 0.1273, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 11:40:20] (step=0037800) Train Loss: 0.1230, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 11:41:21] (step=0037900) Train Loss: 0.1298, Train Steps/Sec: 1.63, Samples/Sec: 78.21
+[2025-06-05 11:42:22] (step=0038000) Train Loss: 0.1228, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 11:42:40] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 11:43:41] (step=0038100) Train Loss: 0.1241, Train Steps/Sec: 1.26, Samples/Sec: 60.63
+[2025-06-05 11:44:43] (step=0038200) Train Loss: 0.1237, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-05 11:45:44] (step=0038300) Train Loss: 0.1274, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 11:46:45] (step=0038400) Train Loss: 0.1214, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 11:47:46] (step=0038500) Train Loss: 0.1256, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 11:48:47] (step=0038600) Train Loss: 0.1313, Train Steps/Sec: 1.63, Samples/Sec: 78.13
+[2025-06-05 11:49:49] (step=0038700) Train Loss: 0.1279, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 11:50:50] (step=0038800) Train Loss: 0.1241, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 11:51:51] (step=0038900) Train Loss: 0.1236, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-05 11:52:52] (step=0039000) Train Loss: 0.1263, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 11:53:53] (step=0039100) Train Loss: 0.1267, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 11:54:54] (step=0039200) Train Loss: 0.1230, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 11:55:55] (step=0039300) Train Loss: 0.1267, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 11:56:56] (step=0039400) Train Loss: 0.1226, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 11:57:58] (step=0039500) Train Loss: 0.1241, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 11:58:59] (step=0039600) Train Loss: 0.1243, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 12:00:00] (step=0039700) Train Loss: 0.1235, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-05 12:01:00] (step=0039800) Train Loss: 0.1207, Train Steps/Sec: 1.64, Samples/Sec: 78.93
+[2025-06-05 12:02:01] (step=0039900) Train Loss: 0.1271, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 12:03:03] (step=0040000) Train Loss: 0.1302, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 12:03:29] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0040000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 12:03:32] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 12:05:51] (step=0040000) Perceptual Loss: 0.2461, Eval Time: 142.22
+[2025-06-05 12:06:52] (step=0040100) Train Loss: 0.1241, Train Steps/Sec: 0.44, Samples/Sec: 20.90
+[2025-06-05 12:07:53] (step=0040200) Train Loss: 0.1245, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 12:08:54] (step=0040300) Train Loss: 0.1267, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 12:09:55] (step=0040400) Train Loss: 0.1292, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 12:10:57] (step=0040500) Train Loss: 0.1272, Train Steps/Sec: 1.63, Samples/Sec: 78.36
+[2025-06-05 12:11:58] (step=0040600) Train Loss: 0.1320, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 12:12:59] (step=0040700) Train Loss: 0.1257, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 12:14:00] (step=0040800) Train Loss: 0.1218, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 12:15:01] (step=0040900) Train Loss: 0.1249, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 12:16:02] (step=0041000) Train Loss: 0.1296, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 12:17:03] (step=0041100) Train Loss: 0.1255, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 12:18:04] (step=0041200) Train Loss: 0.1233, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 12:19:05] (step=0041300) Train Loss: 0.1167, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 12:20:07] (step=0041400) Train Loss: 0.1290, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 12:21:08] (step=0041500) Train Loss: 0.1249, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 12:22:09] (step=0041600) Train Loss: 0.1261, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 12:23:10] (step=0041700) Train Loss: 0.1244, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 12:24:11] (step=0041800) Train Loss: 0.1249, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 12:25:12] (step=0041900) Train Loss: 0.1290, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 12:26:13] (step=0042000) Train Loss: 0.1276, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 12:26:28] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 12:27:29] (step=0042100) Train Loss: 0.1253, Train Steps/Sec: 1.32, Samples/Sec: 63.33
+[2025-06-05 12:28:30] (step=0042200) Train Loss: 0.1262, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 12:29:31] (step=0042300) Train Loss: 0.1254, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 12:30:32] (step=0042400) Train Loss: 0.1229, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 12:31:34] (step=0042500) Train Loss: 0.1234, Train Steps/Sec: 1.63, Samples/Sec: 78.29
+[2025-06-05 12:32:35] (step=0042600) Train Loss: 0.1259, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 12:33:36] (step=0042700) Train Loss: 0.1284, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 12:34:37] (step=0042800) Train Loss: 0.1238, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 12:35:38] (step=0042900) Train Loss: 0.1230, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 12:36:39] (step=0043000) Train Loss: 0.1245, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 12:37:40] (step=0043100) Train Loss: 0.1247, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 12:38:42] (step=0043200) Train Loss: 0.1239, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 12:39:43] (step=0043300) Train Loss: 0.1245, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 12:40:44] (step=0043400) Train Loss: 0.1200, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 12:41:45] (step=0043500) Train Loss: 0.1264, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 12:42:46] (step=0043600) Train Loss: 0.1209, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 12:43:47] (step=0043700) Train Loss: 0.1245, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 12:44:48] (step=0043800) Train Loss: 0.1219, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 12:45:50] (step=0043900) Train Loss: 0.1269, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 12:46:51] (step=0044000) Train Loss: 0.1212, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 12:47:04] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 12:48:06] (step=0044100) Train Loss: 0.1221, Train Steps/Sec: 1.34, Samples/Sec: 64.17
+[2025-06-05 12:49:07] (step=0044200) Train Loss: 0.1235, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 12:50:08] (step=0044300) Train Loss: 0.1202, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 12:50:13] Beginning epoch 4...
+[2025-06-05 12:51:09] (step=0044400) Train Loss: 0.1221, Train Steps/Sec: 1.63, Samples/Sec: 78.17
+[2025-06-05 12:52:10] (step=0044500) Train Loss: 0.1261, Train Steps/Sec: 1.63, Samples/Sec: 78.26
+[2025-06-05 12:53:12] (step=0044600) Train Loss: 0.1238, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-05 12:54:13] (step=0044700) Train Loss: 0.1263, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 12:55:14] (step=0044800) Train Loss: 0.1268, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 12:56:15] (step=0044900) Train Loss: 0.1203, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 12:57:16] (step=0045000) Train Loss: 0.1228, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 12:58:17] (step=0045100) Train Loss: 0.1217, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 12:59:18] (step=0045200) Train Loss: 0.1208, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-05 13:00:20] (step=0045300) Train Loss: 0.1207, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 13:01:21] (step=0045400) Train Loss: 0.1216, Train Steps/Sec: 1.63, Samples/Sec: 78.32
+[2025-06-05 13:02:22] (step=0045500) Train Loss: 0.1175, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 13:03:23] (step=0045600) Train Loss: 0.1184, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 13:04:24] (step=0045700) Train Loss: 0.1195, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 13:05:25] (step=0045800) Train Loss: 0.1271, Train Steps/Sec: 1.63, Samples/Sec: 78.30
+[2025-06-05 13:06:27] (step=0045900) Train Loss: 0.1227, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 13:07:28] (step=0046000) Train Loss: 0.1253, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 13:07:42] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 13:08:43] (step=0046100) Train Loss: 0.1241, Train Steps/Sec: 1.32, Samples/Sec: 63.50
+[2025-06-05 13:09:44] (step=0046200) Train Loss: 0.1197, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 13:10:46] (step=0046300) Train Loss: 0.1223, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 13:11:47] (step=0046400) Train Loss: 0.1242, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 13:12:48] (step=0046500) Train Loss: 0.1254, Train Steps/Sec: 1.63, Samples/Sec: 78.32
+[2025-06-05 13:13:49] (step=0046600) Train Loss: 0.1174, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 13:14:50] (step=0046700) Train Loss: 0.1157, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 13:15:51] (step=0046800) Train Loss: 0.1248, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 13:16:52] (step=0046900) Train Loss: 0.1259, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 13:17:53] (step=0047000) Train Loss: 0.1253, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 13:18:54] (step=0047100) Train Loss: 0.1213, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 13:19:56] (step=0047200) Train Loss: 0.1233, Train Steps/Sec: 1.63, Samples/Sec: 78.29
+[2025-06-05 13:20:57] (step=0047300) Train Loss: 0.1238, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 13:21:58] (step=0047400) Train Loss: 0.1195, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 13:22:59] (step=0047500) Train Loss: 0.1263, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 13:24:00] (step=0047600) Train Loss: 0.1220, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 13:25:01] (step=0047700) Train Loss: 0.1210, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 13:26:02] (step=0047800) Train Loss: 0.1212, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 13:27:04] (step=0047900) Train Loss: 0.1202, Train Steps/Sec: 1.63, Samples/Sec: 78.15
+[2025-06-05 13:28:04] (step=0048000) Train Loss: 0.1250, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-05 13:28:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 13:29:18] (step=0048100) Train Loss: 0.1247, Train Steps/Sec: 1.36, Samples/Sec: 65.17
+[2025-06-05 13:30:19] (step=0048200) Train Loss: 0.1188, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 13:31:20] (step=0048300) Train Loss: 0.1175, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 13:32:22] (step=0048400) Train Loss: 0.1200, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 13:33:23] (step=0048500) Train Loss: 0.1254, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-05 13:34:24] (step=0048600) Train Loss: 0.1251, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 13:35:25] (step=0048700) Train Loss: 0.1196, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 13:36:26] (step=0048800) Train Loss: 0.1266, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 13:37:27] (step=0048900) Train Loss: 0.1206, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 13:38:28] (step=0049000) Train Loss: 0.1222, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 13:39:29] (step=0049100) Train Loss: 0.1152, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 13:40:31] (step=0049200) Train Loss: 0.1283, Train Steps/Sec: 1.63, Samples/Sec: 78.30
+[2025-06-05 13:41:32] (step=0049300) Train Loss: 0.1219, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 13:42:33] (step=0049400) Train Loss: 0.1200, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 13:43:34] (step=0049500) Train Loss: 0.1283, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 13:44:35] (step=0049600) Train Loss: 0.1199, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 13:45:36] (step=0049700) Train Loss: 0.1212, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 13:46:37] (step=0049800) Train Loss: 0.1190, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 13:47:38] (step=0049900) Train Loss: 0.1223, Train Steps/Sec: 1.63, Samples/Sec: 78.25
+[2025-06-05 13:48:40] (step=0050000) Train Loss: 0.1177, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 13:48:54] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 13:48:57] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 13:51:17] (step=0050000) Perceptual Loss: 0.2002, Eval Time: 142.84
+[2025-06-05 13:52:18] (step=0050100) Train Loss: 0.1233, Train Steps/Sec: 0.46, Samples/Sec: 22.01
+[2025-06-05 13:53:19] (step=0050200) Train Loss: 0.1263, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 13:54:20] (step=0050300) Train Loss: 0.1238, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 13:55:21] (step=0050400) Train Loss: 0.1225, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 13:56:22] (step=0050500) Train Loss: 0.1191, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 13:57:23] (step=0050600) Train Loss: 0.1198, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 13:58:24] (step=0050700) Train Loss: 0.1199, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 13:59:25] (step=0050800) Train Loss: 0.1178, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 14:00:26] (step=0050900) Train Loss: 0.1156, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 14:01:28] (step=0051000) Train Loss: 0.1212, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 14:02:29] (step=0051100) Train Loss: 0.1152, Train Steps/Sec: 1.63, Samples/Sec: 78.28
+[2025-06-05 14:03:30] (step=0051200) Train Loss: 0.1257, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 14:04:31] (step=0051300) Train Loss: 0.1218, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 14:05:32] (step=0051400) Train Loss: 0.1227, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 14:06:33] (step=0051500) Train Loss: 0.1202, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 14:07:34] (step=0051600) Train Loss: 0.1168, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 14:08:35] (step=0051700) Train Loss: 0.1209, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 14:09:37] (step=0051800) Train Loss: 0.1123, Train Steps/Sec: 1.63, Samples/Sec: 78.24
+[2025-06-05 14:10:38] (step=0051900) Train Loss: 0.1242, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 14:11:39] (step=0052000) Train Loss: 0.1190, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 14:11:53] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 14:12:54] (step=0052100) Train Loss: 0.1181, Train Steps/Sec: 1.32, Samples/Sec: 63.46
+[2025-06-05 14:13:55] (step=0052200) Train Loss: 0.1224, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 14:14:57] (step=0052300) Train Loss: 0.1252, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 14:15:58] (step=0052400) Train Loss: 0.1191, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 14:16:59] (step=0052500) Train Loss: 0.1183, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 14:18:00] (step=0052600) Train Loss: 0.1281, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 14:19:01] (step=0052700) Train Loss: 0.1215, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 14:20:02] (step=0052800) Train Loss: 0.1172, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 14:21:03] (step=0052900) Train Loss: 0.1244, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 14:22:04] (step=0053000) Train Loss: 0.1249, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 14:23:06] (step=0053100) Train Loss: 0.1215, Train Steps/Sec: 1.63, Samples/Sec: 78.31
+[2025-06-05 14:24:07] (step=0053200) Train Loss: 0.1189, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 14:25:08] (step=0053300) Train Loss: 0.1221, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 14:26:09] (step=0053400) Train Loss: 0.1205, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 14:27:10] (step=0053500) Train Loss: 0.1250, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 14:28:11] (step=0053600) Train Loss: 0.1185, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 14:29:12] (step=0053700) Train Loss: 0.1174, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 14:30:14] (step=0053800) Train Loss: 0.1219, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 14:31:15] (step=0053900) Train Loss: 0.1217, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 14:32:16] (step=0054000) Train Loss: 0.1210, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 14:32:30] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 14:33:31] (step=0054100) Train Loss: 0.1124, Train Steps/Sec: 1.34, Samples/Sec: 64.18
+[2025-06-05 14:34:32] (step=0054200) Train Loss: 0.1247, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 14:35:33] (step=0054300) Train Loss: 0.1201, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 14:36:34] (step=0054400) Train Loss: 0.1187, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 14:37:35] (step=0054500) Train Loss: 0.1216, Train Steps/Sec: 1.63, Samples/Sec: 78.40
+[2025-06-05 14:38:36] (step=0054600) Train Loss: 0.1206, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 14:39:37] (step=0054700) Train Loss: 0.1239, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 14:40:39] (step=0054800) Train Loss: 0.1191, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 14:41:40] (step=0054900) Train Loss: 0.1152, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 14:42:41] (step=0055000) Train Loss: 0.1207, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 14:43:42] (step=0055100) Train Loss: 0.1213, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 14:44:43] (step=0055200) Train Loss: 0.1163, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 14:45:44] (step=0055300) Train Loss: 0.1234, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 14:46:36] Beginning epoch 5...
+[2025-06-05 14:46:46] (step=0055400) Train Loss: 0.1209, Train Steps/Sec: 1.63, Samples/Sec: 78.22
+[2025-06-05 14:47:47] (step=0055500) Train Loss: 0.1246, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 14:48:48] (step=0055600) Train Loss: 0.1216, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 14:49:49] (step=0055700) Train Loss: 0.1194, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 14:50:50] (step=0055800) Train Loss: 0.1217, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 14:51:51] (step=0055900) Train Loss: 0.1156, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 14:52:52] (step=0056000) Train Loss: 0.1196, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 14:53:06] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 14:54:07] (step=0056100) Train Loss: 0.1150, Train Steps/Sec: 1.34, Samples/Sec: 64.13
+[2025-06-05 14:55:08] (step=0056200) Train Loss: 0.1197, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-05 14:56:09] (step=0056300) Train Loss: 0.1257, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-05 14:57:10] (step=0056400) Train Loss: 0.1219, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-05 14:58:11] (step=0056500) Train Loss: 0.1227, Train Steps/Sec: 1.63, Samples/Sec: 78.28
+[2025-06-05 14:59:12] (step=0056600) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 15:00:13] (step=0056700) Train Loss: 0.1253, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 15:01:15] (step=0056800) Train Loss: 0.1200, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 15:02:16] (step=0056900) Train Loss: 0.1197, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 15:03:17] (step=0057000) Train Loss: 0.1198, Train Steps/Sec: 1.63, Samples/Sec: 78.34
+[2025-06-05 15:04:18] (step=0057100) Train Loss: 0.1176, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 15:05:19] (step=0057200) Train Loss: 0.1192, Train Steps/Sec: 1.63, Samples/Sec: 78.29
+[2025-06-05 15:06:20] (step=0057300) Train Loss: 0.1156, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 15:07:22] (step=0057400) Train Loss: 0.1218, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 15:08:23] (step=0057500) Train Loss: 0.1179, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 15:09:24] (step=0057600) Train Loss: 0.1194, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 15:10:25] (step=0057700) Train Loss: 0.1201, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 15:11:26] (step=0057800) Train Loss: 0.1199, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 15:12:27] (step=0057900) Train Loss: 0.1160, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 15:13:28] (step=0058000) Train Loss: 0.1212, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 15:13:43] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 15:14:44] (step=0058100) Train Loss: 0.1178, Train Steps/Sec: 1.32, Samples/Sec: 63.26
+[2025-06-05 15:15:45] (step=0058200) Train Loss: 0.1212, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 15:16:46] (step=0058300) Train Loss: 0.1242, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 15:17:47] (step=0058400) Train Loss: 0.1181, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 15:18:49] (step=0058500) Train Loss: 0.1198, Train Steps/Sec: 1.63, Samples/Sec: 78.29
+[2025-06-05 15:19:50] (step=0058600) Train Loss: 0.1164, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 15:20:51] (step=0058700) Train Loss: 0.1220, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 15:21:52] (step=0058800) Train Loss: 0.1171, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 15:22:53] (step=0058900) Train Loss: 0.1202, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 15:23:54] (step=0059000) Train Loss: 0.1212, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 15:24:55] (step=0059100) Train Loss: 0.1204, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 15:25:57] (step=0059200) Train Loss: 0.1166, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 15:26:58] (step=0059300) Train Loss: 0.1197, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 15:27:59] (step=0059400) Train Loss: 0.1214, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 15:29:00] (step=0059500) Train Loss: 0.1257, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 15:30:01] (step=0059600) Train Loss: 0.1236, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 15:31:02] (step=0059700) Train Loss: 0.1219, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 15:32:03] (step=0059800) Train Loss: 0.1147, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 15:33:04] (step=0059900) Train Loss: 0.1188, Train Steps/Sec: 1.63, Samples/Sec: 78.26
+[2025-06-05 15:34:05] (step=0060000) Train Loss: 0.1183, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 15:34:32] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0060000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 15:34:35] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 15:36:55] (step=0060000) Perceptual Loss: 0.1831, Eval Time: 142.94
+[2025-06-05 15:37:56] (step=0060100) Train Loss: 0.1190, Train Steps/Sec: 0.43, Samples/Sec: 20.83
+[2025-06-05 15:38:57] (step=0060200) Train Loss: 0.1193, Train Steps/Sec: 1.63, Samples/Sec: 78.36
+[2025-06-05 15:39:58] (step=0060300) Train Loss: 0.1194, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 15:40:59] (step=0060400) Train Loss: 0.1158, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 15:42:01] (step=0060500) Train Loss: 0.1160, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 15:43:02] (step=0060600) Train Loss: 0.1220, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 15:44:03] (step=0060700) Train Loss: 0.1254, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 15:45:04] (step=0060800) Train Loss: 0.1185, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 15:46:05] (step=0060900) Train Loss: 0.1200, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 15:47:06] (step=0061000) Train Loss: 0.1212, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 15:48:07] (step=0061100) Train Loss: 0.1175, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-05 15:49:08] (step=0061200) Train Loss: 0.1234, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-05 15:50:09] (step=0061300) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 15:51:10] (step=0061400) Train Loss: 0.1154, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 15:52:11] (step=0061500) Train Loss: 0.1143, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 15:53:12] (step=0061600) Train Loss: 0.1194, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 15:54:14] (step=0061700) Train Loss: 0.1189, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 15:55:15] (step=0061800) Train Loss: 0.1210, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 15:56:16] (step=0061900) Train Loss: 0.1194, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 15:57:17] (step=0062000) Train Loss: 0.1243, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 15:57:31] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 15:58:32] (step=0062100) Train Loss: 0.1176, Train Steps/Sec: 1.34, Samples/Sec: 64.12
+[2025-06-05 15:59:33] (step=0062200) Train Loss: 0.1152, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 16:00:34] (step=0062300) Train Loss: 0.1220, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 16:01:35] (step=0062400) Train Loss: 0.1244, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 16:02:36] (step=0062500) Train Loss: 0.1156, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-05 16:03:37] (step=0062600) Train Loss: 0.1210, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 16:04:38] (step=0062700) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 16:05:39] (step=0062800) Train Loss: 0.1187, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 16:06:40] (step=0062900) Train Loss: 0.1211, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 16:07:42] (step=0063000) Train Loss: 0.1181, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 16:08:43] (step=0063100) Train Loss: 0.1191, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-05 16:09:44] (step=0063200) Train Loss: 0.1240, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 16:10:45] (step=0063300) Train Loss: 0.1172, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 16:11:46] (step=0063400) Train Loss: 0.1251, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 16:12:47] (step=0063500) Train Loss: 0.1227, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 16:13:48] (step=0063600) Train Loss: 0.1174, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 16:14:49] (step=0063700) Train Loss: 0.1210, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 16:15:50] (step=0063800) Train Loss: 0.1204, Train Steps/Sec: 1.63, Samples/Sec: 78.41
+[2025-06-05 16:16:51] (step=0063900) Train Loss: 0.1199, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 16:17:52] (step=0064000) Train Loss: 0.1197, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 16:18:07] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 16:19:08] (step=0064100) Train Loss: 0.1240, Train Steps/Sec: 1.32, Samples/Sec: 63.38
+[2025-06-05 16:20:09] (step=0064200) Train Loss: 0.1223, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 16:21:10] (step=0064300) Train Loss: 0.1159, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 16:22:11] (step=0064400) Train Loss: 0.1213, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 16:23:12] (step=0064500) Train Loss: 0.1205, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 16:24:13] (step=0064600) Train Loss: 0.1131, Train Steps/Sec: 1.65, Samples/Sec: 79.02
+[2025-06-05 16:25:14] (step=0064700) Train Loss: 0.1247, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 16:26:15] (step=0064800) Train Loss: 0.1157, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 16:27:16] (step=0064900) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 16:28:18] (step=0065000) Train Loss: 0.1167, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 16:29:19] (step=0065100) Train Loss: 0.1191, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 16:30:20] (step=0065200) Train Loss: 0.1234, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 16:31:21] (step=0065300) Train Loss: 0.1230, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 16:32:22] (step=0065400) Train Loss: 0.1137, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 16:33:23] (step=0065500) Train Loss: 0.1163, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 16:34:24] (step=0065600) Train Loss: 0.1165, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 16:35:25] (step=0065700) Train Loss: 0.1184, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 16:36:26] (step=0065800) Train Loss: 0.1113, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 16:37:27] (step=0065900) Train Loss: 0.1191, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 16:38:29] (step=0066000) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 16:38:43] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 16:39:44] (step=0066100) Train Loss: 0.1212, Train Steps/Sec: 1.33, Samples/Sec: 63.68
+[2025-06-05 16:40:45] (step=0066200) Train Loss: 0.1196, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 16:41:46] (step=0066300) Train Loss: 0.1221, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 16:42:47] (step=0066400) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 16:43:25] Beginning epoch 6...
+[2025-06-05 16:43:49] (step=0066500) Train Loss: 0.1158, Train Steps/Sec: 1.62, Samples/Sec: 77.76
+[2025-06-05 16:44:50] (step=0066600) Train Loss: 0.1193, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 16:45:51] (step=0066700) Train Loss: 0.1212, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 16:46:52] (step=0066800) Train Loss: 0.1161, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 16:47:53] (step=0066900) Train Loss: 0.1168, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 16:48:54] (step=0067000) Train Loss: 0.1137, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 16:49:55] (step=0067100) Train Loss: 0.1144, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 16:50:57] (step=0067200) Train Loss: 0.1161, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 16:51:58] (step=0067300) Train Loss: 0.1156, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 16:52:59] (step=0067400) Train Loss: 0.1163, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 16:54:00] (step=0067500) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 16:55:01] (step=0067600) Train Loss: 0.1255, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 16:56:02] (step=0067700) Train Loss: 0.1181, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 16:57:03] (step=0067800) Train Loss: 0.1195, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-05 16:58:04] (step=0067900) Train Loss: 0.1214, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 16:59:05] (step=0068000) Train Loss: 0.1162, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 16:59:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 17:00:21] (step=0068100) Train Loss: 0.1160, Train Steps/Sec: 1.32, Samples/Sec: 63.18
+[2025-06-05 17:01:22] (step=0068200) Train Loss: 0.1192, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 17:02:23] (step=0068300) Train Loss: 0.1206, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 17:03:25] (step=0068400) Train Loss: 0.1199, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 17:04:26] (step=0068500) Train Loss: 0.1218, Train Steps/Sec: 1.63, Samples/Sec: 78.32
+[2025-06-05 17:05:27] (step=0068600) Train Loss: 0.1225, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 17:06:28] (step=0068700) Train Loss: 0.1213, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 17:07:29] (step=0068800) Train Loss: 0.1180, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 17:08:30] (step=0068900) Train Loss: 0.1259, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 17:09:31] (step=0069000) Train Loss: 0.1156, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 17:10:32] (step=0069100) Train Loss: 0.1163, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 17:11:34] (step=0069200) Train Loss: 0.1243, Train Steps/Sec: 1.63, Samples/Sec: 78.32
+[2025-06-05 17:12:35] (step=0069300) Train Loss: 0.1152, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 17:13:36] (step=0069400) Train Loss: 0.1236, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 17:14:37] (step=0069500) Train Loss: 0.1164, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 17:15:38] (step=0069600) Train Loss: 0.1173, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 17:16:39] (step=0069700) Train Loss: 0.1206, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 17:17:40] (step=0069800) Train Loss: 0.1224, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 17:18:41] (step=0069900) Train Loss: 0.1147, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 17:19:42] (step=0070000) Train Loss: 0.1189, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 17:19:57] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 17:20:00] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 17:22:20] (step=0070000) Perceptual Loss: 0.1678, Eval Time: 142.68
+[2025-06-05 17:23:21] (step=0070100) Train Loss: 0.1142, Train Steps/Sec: 0.46, Samples/Sec: 21.96
+[2025-06-05 17:24:22] (step=0070200) Train Loss: 0.1171, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 17:25:23] (step=0070300) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 17:26:24] (step=0070400) Train Loss: 0.1163, Train Steps/Sec: 1.63, Samples/Sec: 78.25
+[2025-06-05 17:27:25] (step=0070500) Train Loss: 0.1204, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 17:28:26] (step=0070600) Train Loss: 0.1182, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 17:29:27] (step=0070700) Train Loss: 0.1162, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 17:30:29] (step=0070800) Train Loss: 0.1171, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 17:31:30] (step=0070900) Train Loss: 0.1186, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 17:32:31] (step=0071000) Train Loss: 0.1217, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 17:33:32] (step=0071100) Train Loss: 0.1194, Train Steps/Sec: 1.63, Samples/Sec: 78.32
+[2025-06-05 17:34:33] (step=0071200) Train Loss: 0.1182, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 17:35:34] (step=0071300) Train Loss: 0.1235, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 17:36:35] (step=0071400) Train Loss: 0.1183, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 17:37:37] (step=0071500) Train Loss: 0.1202, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 17:38:38] (step=0071600) Train Loss: 0.1173, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 17:39:39] (step=0071700) Train Loss: 0.1217, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 17:40:40] (step=0071800) Train Loss: 0.1146, Train Steps/Sec: 1.63, Samples/Sec: 78.36
+[2025-06-05 17:41:41] (step=0071900) Train Loss: 0.1223, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 17:42:42] (step=0072000) Train Loss: 0.1220, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 17:42:56] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 17:43:57] (step=0072100) Train Loss: 0.1178, Train Steps/Sec: 1.33, Samples/Sec: 63.87
+[2025-06-05 17:44:58] (step=0072200) Train Loss: 0.1187, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 17:45:59] (step=0072300) Train Loss: 0.1168, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 17:47:01] (step=0072400) Train Loss: 0.1185, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-05 17:48:02] (step=0072500) Train Loss: 0.1167, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 17:49:03] (step=0072600) Train Loss: 0.1131, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 17:50:04] (step=0072700) Train Loss: 0.1173, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 17:51:05] (step=0072800) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.94
+[2025-06-05 17:52:05] (step=0072900) Train Loss: 0.1131, Train Steps/Sec: 1.64, Samples/Sec: 78.92
+[2025-06-05 17:53:07] (step=0073000) Train Loss: 0.1224, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 17:54:08] (step=0073100) Train Loss: 0.1152, Train Steps/Sec: 1.63, Samples/Sec: 78.23
+[2025-06-05 17:55:09] (step=0073200) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 17:56:10] (step=0073300) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-05 17:57:11] (step=0073400) Train Loss: 0.1143, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-05 17:58:12] (step=0073500) Train Loss: 0.1166, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 17:59:13] (step=0073600) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 18:00:15] (step=0073700) Train Loss: 0.1228, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 18:01:16] (step=0073800) Train Loss: 0.1177, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 18:02:17] (step=0073900) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 18:03:18] (step=0074000) Train Loss: 0.1148, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 18:03:33] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 18:04:34] (step=0074100) Train Loss: 0.1186, Train Steps/Sec: 1.32, Samples/Sec: 63.29
+[2025-06-05 18:05:35] (step=0074200) Train Loss: 0.1200, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 18:06:36] (step=0074300) Train Loss: 0.1173, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 18:07:37] (step=0074400) Train Loss: 0.1193, Train Steps/Sec: 1.63, Samples/Sec: 78.41
+[2025-06-05 18:08:38] (step=0074500) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 18:09:39] (step=0074600) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 18:10:40] (step=0074700) Train Loss: 0.1195, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 18:11:41] (step=0074800) Train Loss: 0.1139, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 18:12:43] (step=0074900) Train Loss: 0.1167, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 18:13:44] (step=0075000) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 18:14:45] (step=0075100) Train Loss: 0.1186, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 18:15:46] (step=0075200) Train Loss: 0.1180, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 18:16:47] (step=0075300) Train Loss: 0.1186, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 18:17:48] (step=0075400) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 18:18:49] (step=0075500) Train Loss: 0.1201, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 18:19:50] (step=0075600) Train Loss: 0.1203, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 18:20:51] (step=0075700) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 18:21:52] (step=0075800) Train Loss: 0.1206, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 18:22:54] (step=0075900) Train Loss: 0.1183, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 18:23:55] (step=0076000) Train Loss: 0.1253, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 18:24:08] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 18:25:10] (step=0076100) Train Loss: 0.1157, Train Steps/Sec: 1.33, Samples/Sec: 64.02
+[2025-06-05 18:26:11] (step=0076200) Train Loss: 0.1142, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 18:27:12] (step=0076300) Train Loss: 0.1157, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 18:28:13] (step=0076400) Train Loss: 0.1206, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 18:29:14] (step=0076500) Train Loss: 0.1186, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 18:30:15] (step=0076600) Train Loss: 0.1217, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 18:31:16] (step=0076700) Train Loss: 0.1151, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 18:32:17] (step=0076800) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 18:33:18] (step=0076900) Train Loss: 0.1198, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 18:34:19] (step=0077000) Train Loss: 0.1142, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 18:35:21] (step=0077100) Train Loss: 0.1102, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 18:36:22] (step=0077200) Train Loss: 0.1181, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 18:37:23] (step=0077300) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 18:38:24] (step=0077400) Train Loss: 0.1155, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 18:39:25] (step=0077500) Train Loss: 0.1165, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 18:39:49] Beginning epoch 7...
+[2025-06-05 18:40:26] (step=0077600) Train Loss: 0.1177, Train Steps/Sec: 1.63, Samples/Sec: 78.21
+[2025-06-05 18:41:27] (step=0077700) Train Loss: 0.1189, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 18:42:28] (step=0077800) Train Loss: 0.1162, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 18:43:29] (step=0077900) Train Loss: 0.1207, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 18:44:30] (step=0078000) Train Loss: 0.1144, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 18:44:45] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 18:45:46] (step=0078100) Train Loss: 0.1228, Train Steps/Sec: 1.32, Samples/Sec: 63.20
+[2025-06-05 18:46:48] (step=0078200) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 18:47:49] (step=0078300) Train Loss: 0.1216, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 18:48:50] (step=0078400) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 18:49:51] (step=0078500) Train Loss: 0.1124, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 18:50:52] (step=0078600) Train Loss: 0.1179, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-05 18:51:53] (step=0078700) Train Loss: 0.1178, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 18:52:54] (step=0078800) Train Loss: 0.1186, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 18:53:55] (step=0078900) Train Loss: 0.1179, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 18:54:56] (step=0079000) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 18:55:57] (step=0079100) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 18:56:59] (step=0079200) Train Loss: 0.1147, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 18:58:00] (step=0079300) Train Loss: 0.1184, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 18:59:01] (step=0079400) Train Loss: 0.1165, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 19:00:02] (step=0079500) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 19:01:03] (step=0079600) Train Loss: 0.1199, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 19:02:04] (step=0079700) Train Loss: 0.1152, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 19:03:05] (step=0079800) Train Loss: 0.1150, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 19:04:06] (step=0079900) Train Loss: 0.1158, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 19:05:08] (step=0080000) Train Loss: 0.1137, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 19:05:34] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0080000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 19:05:36] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 19:07:56] (step=0080000) Perceptual Loss: 0.1639, Eval Time: 142.61
+[2025-06-05 19:08:57] (step=0080100) Train Loss: 0.1144, Train Steps/Sec: 0.44, Samples/Sec: 20.91
+[2025-06-05 19:09:58] (step=0080200) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-05 19:10:59] (step=0080300) Train Loss: 0.1193, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 19:12:00] (step=0080400) Train Loss: 0.1183, Train Steps/Sec: 1.63, Samples/Sec: 78.41
+[2025-06-05 19:13:01] (step=0080500) Train Loss: 0.1190, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 19:14:02] (step=0080600) Train Loss: 0.1170, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 19:15:04] (step=0080700) Train Loss: 0.1222, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 19:16:05] (step=0080800) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 19:17:06] (step=0080900) Train Loss: 0.1123, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 19:18:07] (step=0081000) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-05 19:19:07] (step=0081100) Train Loss: 0.1221, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-05 19:20:08] (step=0081200) Train Loss: 0.1180, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 19:21:09] (step=0081300) Train Loss: 0.1192, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 19:22:11] (step=0081400) Train Loss: 0.1169, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 19:23:12] (step=0081500) Train Loss: 0.1216, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 19:24:13] (step=0081600) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 19:25:14] (step=0081700) Train Loss: 0.1142, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 19:26:15] (step=0081800) Train Loss: 0.1172, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 19:27:16] (step=0081900) Train Loss: 0.1147, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 19:28:17] (step=0082000) Train Loss: 0.1158, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 19:28:31] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 19:29:32] (step=0082100) Train Loss: 0.1200, Train Steps/Sec: 1.34, Samples/Sec: 64.19
+[2025-06-05 19:30:33] (step=0082200) Train Loss: 0.1166, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 19:31:34] (step=0082300) Train Loss: 0.1163, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 19:32:35] (step=0082400) Train Loss: 0.1122, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 19:33:36] (step=0082500) Train Loss: 0.1147, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 19:34:37] (step=0082600) Train Loss: 0.1191, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 19:35:38] (step=0082700) Train Loss: 0.1185, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 19:36:39] (step=0082800) Train Loss: 0.1163, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 19:37:40] (step=0082900) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 19:38:41] (step=0083000) Train Loss: 0.1184, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 19:39:43] (step=0083100) Train Loss: 0.1167, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 19:40:44] (step=0083200) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 19:41:45] (step=0083300) Train Loss: 0.1195, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 19:42:46] (step=0083400) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 19:43:47] (step=0083500) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 19:44:48] (step=0083600) Train Loss: 0.1191, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 19:45:49] (step=0083700) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 19:46:50] (step=0083800) Train Loss: 0.1153, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 19:47:51] (step=0083900) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 19:48:53] (step=0084000) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 19:49:06] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 19:50:07] (step=0084100) Train Loss: 0.1119, Train Steps/Sec: 1.34, Samples/Sec: 64.32
+[2025-06-05 19:51:08] (step=0084200) Train Loss: 0.1205, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 19:52:09] (step=0084300) Train Loss: 0.1180, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 19:53:11] (step=0084400) Train Loss: 0.1185, Train Steps/Sec: 1.63, Samples/Sec: 78.31
+[2025-06-05 19:54:12] (step=0084500) Train Loss: 0.1186, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 19:55:13] (step=0084600) Train Loss: 0.1158, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 19:56:14] (step=0084700) Train Loss: 0.1180, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 19:57:15] (step=0084800) Train Loss: 0.1184, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 19:58:16] (step=0084900) Train Loss: 0.1232, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 19:59:17] (step=0085000) Train Loss: 0.1207, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 20:00:18] (step=0085100) Train Loss: 0.1158, Train Steps/Sec: 1.63, Samples/Sec: 78.32
+[2025-06-05 20:01:19] (step=0085200) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 20:02:20] (step=0085300) Train Loss: 0.1198, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 20:03:21] (step=0085400) Train Loss: 0.1151, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 20:04:22] (step=0085500) Train Loss: 0.1134, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 20:05:23] (step=0085600) Train Loss: 0.1171, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 20:06:24] (step=0085700) Train Loss: 0.1123, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 20:07:25] (step=0085800) Train Loss: 0.1153, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-05 20:08:26] (step=0085900) Train Loss: 0.1231, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 20:09:27] (step=0086000) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 20:09:42] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 20:10:43] (step=0086100) Train Loss: 0.1204, Train Steps/Sec: 1.33, Samples/Sec: 63.81
+[2025-06-05 20:11:44] (step=0086200) Train Loss: 0.1191, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 20:12:45] (step=0086300) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 20:13:46] (step=0086400) Train Loss: 0.1176, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 20:14:47] (step=0086500) Train Loss: 0.1142, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-05 20:15:48] (step=0086600) Train Loss: 0.1168, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 20:16:49] (step=0086700) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 20:17:50] (step=0086800) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 20:18:51] (step=0086900) Train Loss: 0.1151, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 20:19:53] (step=0087000) Train Loss: 0.1129, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 20:20:54] (step=0087100) Train Loss: 0.1159, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-05 20:21:55] (step=0087200) Train Loss: 0.1149, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 20:22:56] (step=0087300) Train Loss: 0.1219, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 20:23:57] (step=0087400) Train Loss: 0.1127, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 20:24:58] (step=0087500) Train Loss: 0.1180, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 20:25:59] (step=0087600) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 20:27:00] (step=0087700) Train Loss: 0.1173, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 20:28:01] (step=0087800) Train Loss: 0.1158, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 20:29:02] (step=0087900) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 20:30:03] (step=0088000) Train Loss: 0.1161, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 20:30:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 20:31:18] (step=0088100) Train Loss: 0.1176, Train Steps/Sec: 1.34, Samples/Sec: 64.16
+[2025-06-05 20:32:19] (step=0088200) Train Loss: 0.1201, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 20:33:21] (step=0088300) Train Loss: 0.1191, Train Steps/Sec: 1.63, Samples/Sec: 78.28
+[2025-06-05 20:34:22] (step=0088400) Train Loss: 0.1198, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 20:35:23] (step=0088500) Train Loss: 0.1124, Train Steps/Sec: 1.63, Samples/Sec: 78.14
+[2025-06-05 20:36:24] (step=0088600) Train Loss: 0.1129, Train Steps/Sec: 1.63, Samples/Sec: 78.34
+[2025-06-05 20:36:34] Beginning epoch 8...
+[2025-06-05 20:37:26] (step=0088700) Train Loss: 0.1111, Train Steps/Sec: 1.62, Samples/Sec: 77.85
+[2025-06-05 20:38:27] (step=0088800) Train Loss: 0.1123, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 20:39:29] (step=0088900) Train Loss: 0.1175, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 20:40:30] (step=0089000) Train Loss: 0.1134, Train Steps/Sec: 1.63, Samples/Sec: 78.36
+[2025-06-05 20:41:31] (step=0089100) Train Loss: 0.1087, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-05 20:42:33] (step=0089200) Train Loss: 0.1118, Train Steps/Sec: 1.63, Samples/Sec: 78.22
+[2025-06-05 20:43:34] (step=0089300) Train Loss: 0.1128, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 20:44:35] (step=0089400) Train Loss: 0.1180, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-05 20:45:41] (step=0089500) Train Loss: 0.1098, Train Steps/Sec: 1.51, Samples/Sec: 72.67
+[2025-06-05 20:46:43] (step=0089600) Train Loss: 0.1198, Train Steps/Sec: 1.63, Samples/Sec: 78.04
+[2025-06-05 20:47:44] (step=0089700) Train Loss: 0.1181, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 20:48:45] (step=0089800) Train Loss: 0.1181, Train Steps/Sec: 1.63, Samples/Sec: 78.25
+[2025-06-05 20:49:46] (step=0089900) Train Loss: 0.1189, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 20:50:47] (step=0090000) Train Loss: 0.1220, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 20:51:03] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 20:51:05] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 20:53:25] (step=0090000) Perceptual Loss: 0.1481, Eval Time: 142.82
+[2025-06-05 20:54:26] (step=0090100) Train Loss: 0.1090, Train Steps/Sec: 0.46, Samples/Sec: 21.89
+[2025-06-05 20:55:27] (step=0090200) Train Loss: 0.1154, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 20:56:29] (step=0090300) Train Loss: 0.1148, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-05 20:57:30] (step=0090400) Train Loss: 0.1204, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 20:58:31] (step=0090500) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 20:59:32] (step=0090600) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 21:00:33] (step=0090700) Train Loss: 0.1179, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 21:01:34] (step=0090800) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 21:02:35] (step=0090900) Train Loss: 0.1155, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 21:03:36] (step=0091000) Train Loss: 0.1165, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-05 21:04:38] (step=0091100) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 21:05:39] (step=0091200) Train Loss: 0.1126, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 21:06:40] (step=0091300) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 21:07:41] (step=0091400) Train Loss: 0.1154, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 21:08:42] (step=0091500) Train Loss: 0.1185, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 21:09:43] (step=0091600) Train Loss: 0.1197, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 21:10:44] (step=0091700) Train Loss: 0.1152, Train Steps/Sec: 1.63, Samples/Sec: 78.40
+[2025-06-05 21:11:45] (step=0091800) Train Loss: 0.1139, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 21:12:46] (step=0091900) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 21:13:47] (step=0092000) Train Loss: 0.1189, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 21:14:03] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 21:15:04] (step=0092100) Train Loss: 0.1123, Train Steps/Sec: 1.30, Samples/Sec: 62.20
+[2025-06-05 21:16:06] (step=0092200) Train Loss: 0.1096, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 21:17:07] (step=0092300) Train Loss: 0.1165, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 21:18:08] (step=0092400) Train Loss: 0.1183, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 21:19:09] (step=0092500) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 21:20:10] (step=0092600) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 21:21:11] (step=0092700) Train Loss: 0.1198, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 21:22:12] (step=0092800) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 21:23:13] (step=0092900) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 21:24:14] (step=0093000) Train Loss: 0.1123, Train Steps/Sec: 1.63, Samples/Sec: 78.29
+[2025-06-05 21:25:16] (step=0093100) Train Loss: 0.1131, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 21:26:17] (step=0093200) Train Loss: 0.1208, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 21:27:18] (step=0093300) Train Loss: 0.1134, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 21:28:19] (step=0093400) Train Loss: 0.1189, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 21:29:20] (step=0093500) Train Loss: 0.1142, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 21:30:21] (step=0093600) Train Loss: 0.1213, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 21:31:22] (step=0093700) Train Loss: 0.1191, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 21:32:23] (step=0093800) Train Loss: 0.1131, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 21:33:24] (step=0093900) Train Loss: 0.1179, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 21:34:25] (step=0094000) Train Loss: 0.1139, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 21:34:40] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 21:35:41] (step=0094100) Train Loss: 0.1129, Train Steps/Sec: 1.33, Samples/Sec: 63.62
+[2025-06-05 21:36:42] (step=0094200) Train Loss: 0.1191, Train Steps/Sec: 1.63, Samples/Sec: 78.41
+[2025-06-05 21:37:43] (step=0094300) Train Loss: 0.1188, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 21:38:44] (step=0094400) Train Loss: 0.1120, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-05 21:39:45] (step=0094500) Train Loss: 0.1148, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 21:40:46] (step=0094600) Train Loss: 0.1182, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 21:41:48] (step=0094700) Train Loss: 0.1146, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 21:42:49] (step=0094800) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 21:43:50] (step=0094900) Train Loss: 0.1180, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 21:44:51] (step=0095000) Train Loss: 0.1182, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 21:45:52] (step=0095100) Train Loss: 0.1162, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-05 21:46:53] (step=0095200) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 21:47:54] (step=0095300) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 21:48:55] (step=0095400) Train Loss: 0.1180, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 21:49:56] (step=0095500) Train Loss: 0.1177, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 21:50:57] (step=0095600) Train Loss: 0.1144, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 21:51:59] (step=0095700) Train Loss: 0.1122, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 21:53:00] (step=0095800) Train Loss: 0.1136, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 21:54:01] (step=0095900) Train Loss: 0.1139, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 21:55:02] (step=0096000) Train Loss: 0.1198, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 21:55:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 21:56:18] (step=0096100) Train Loss: 0.1088, Train Steps/Sec: 1.30, Samples/Sec: 62.64
+[2025-06-05 21:57:20] (step=0096200) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 21:58:21] (step=0096300) Train Loss: 0.1157, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 21:59:22] (step=0096400) Train Loss: 0.1145, Train Steps/Sec: 1.63, Samples/Sec: 78.11
+[2025-06-05 22:00:23] (step=0096500) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 22:01:24] (step=0096600) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 22:02:25] (step=0096700) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 22:03:26] (step=0096800) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 22:04:27] (step=0096900) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 22:05:28] (step=0097000) Train Loss: 0.1185, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 22:06:30] (step=0097100) Train Loss: 0.1122, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-05 22:07:31] (step=0097200) Train Loss: 0.1195, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 22:08:32] (step=0097300) Train Loss: 0.1151, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 22:09:33] (step=0097400) Train Loss: 0.1196, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 22:10:34] (step=0097500) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-05 22:11:35] (step=0097600) Train Loss: 0.1172, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 22:12:36] (step=0097700) Train Loss: 0.1137, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 22:13:37] (step=0097800) Train Loss: 0.1176, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 22:14:38] (step=0097900) Train Loss: 0.1167, Train Steps/Sec: 1.65, Samples/Sec: 79.02
+[2025-06-05 22:15:39] (step=0098000) Train Loss: 0.1195, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-05 22:15:54] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 22:16:55] (step=0098100) Train Loss: 0.1159, Train Steps/Sec: 1.32, Samples/Sec: 63.22
+[2025-06-05 22:17:56] (step=0098200) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 22:18:57] (step=0098300) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 22:19:58] (step=0098400) Train Loss: 0.1148, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 22:20:59] (step=0098500) Train Loss: 0.1117, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-05 22:22:00] (step=0098600) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 22:23:02] (step=0098700) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 22:24:03] (step=0098800) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 22:25:04] (step=0098900) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-05 22:26:05] (step=0099000) Train Loss: 0.1156, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 22:27:06] (step=0099100) Train Loss: 0.1156, Train Steps/Sec: 1.63, Samples/Sec: 78.32
+[2025-06-05 22:28:07] (step=0099200) Train Loss: 0.1169, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 22:29:08] (step=0099300) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 22:30:09] (step=0099400) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 22:31:10] (step=0099500) Train Loss: 0.1114, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 22:32:11] (step=0099600) Train Loss: 0.1176, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 22:33:08] Beginning epoch 9...
+[2025-06-05 22:33:13] (step=0099700) Train Loss: 0.1157, Train Steps/Sec: 1.63, Samples/Sec: 78.18
+[2025-06-05 22:34:14] (step=0099800) Train Loss: 0.1156, Train Steps/Sec: 1.63, Samples/Sec: 78.36
+[2025-06-05 22:35:15] (step=0099900) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 22:36:16] (step=0100000) Train Loss: 0.1163, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 22:36:43] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0100000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-05 22:36:46] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-05 22:39:06] (step=0100000) Perceptual Loss: 0.1472, Eval Time: 142.61
+[2025-06-05 22:40:07] (step=0100100) Train Loss: 0.1115, Train Steps/Sec: 0.43, Samples/Sec: 20.79
+[2025-06-05 22:41:08] (step=0100200) Train Loss: 0.1187, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-05 22:42:09] (step=0100300) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 22:43:10] (step=0100400) Train Loss: 0.1163, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 22:44:11] (step=0100500) Train Loss: 0.1131, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 22:45:12] (step=0100600) Train Loss: 0.1182, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 22:46:13] (step=0100700) Train Loss: 0.1143, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-05 22:47:14] (step=0100800) Train Loss: 0.1177, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 22:48:15] (step=0100900) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 22:49:17] (step=0101000) Train Loss: 0.1126, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 22:50:18] (step=0101100) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 22:51:19] (step=0101200) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 22:52:20] (step=0101300) Train Loss: 0.1166, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 22:53:21] (step=0101400) Train Loss: 0.1076, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-05 22:54:22] (step=0101500) Train Loss: 0.1168, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 22:55:23] (step=0101600) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 22:56:24] (step=0101700) Train Loss: 0.1123, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-05 22:57:25] (step=0101800) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 22:58:26] (step=0101900) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 22:59:28] (step=0102000) Train Loss: 0.1103, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-05 22:59:42] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 23:00:43] (step=0102100) Train Loss: 0.1148, Train Steps/Sec: 1.34, Samples/Sec: 64.10
+[2025-06-05 23:01:44] (step=0102200) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 23:02:45] (step=0102300) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-05 23:03:46] (step=0102400) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 23:04:47] (step=0102500) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 23:05:48] (step=0102600) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 23:06:49] (step=0102700) Train Loss: 0.1134, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-05 23:07:50] (step=0102800) Train Loss: 0.1091, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 23:08:51] (step=0102900) Train Loss: 0.1194, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 23:09:52] (step=0103000) Train Loss: 0.1150, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-05 23:10:53] (step=0103100) Train Loss: 0.1164, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 23:11:54] (step=0103200) Train Loss: 0.1144, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 23:12:55] (step=0103300) Train Loss: 0.1127, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 23:13:56] (step=0103400) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 23:14:57] (step=0103500) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 23:15:58] (step=0103600) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 23:16:59] (step=0103700) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 23:18:00] (step=0103800) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-05 23:19:01] (step=0103900) Train Loss: 0.1185, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 23:20:02] (step=0104000) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-05 23:20:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 23:21:18] (step=0104100) Train Loss: 0.1159, Train Steps/Sec: 1.32, Samples/Sec: 63.43
+[2025-06-05 23:22:19] (step=0104200) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 23:23:20] (step=0104300) Train Loss: 0.1183, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 23:24:21] (step=0104400) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-05 23:25:22] (step=0104500) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 23:26:23] (step=0104600) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-05 23:27:24] (step=0104700) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-05 23:28:25] (step=0104800) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 23:29:26] (step=0104900) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 23:30:27] (step=0105000) Train Loss: 0.1145, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-05 23:31:28] (step=0105100) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 23:32:29] (step=0105200) Train Loss: 0.1186, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-05 23:33:30] (step=0105300) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 23:34:31] (step=0105400) Train Loss: 0.1208, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 23:35:32] (step=0105500) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 23:36:33] (step=0105600) Train Loss: 0.1213, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-05 23:37:34] (step=0105700) Train Loss: 0.1122, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-05 23:38:35] (step=0105800) Train Loss: 0.1173, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 23:39:36] (step=0105900) Train Loss: 0.1142, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-05 23:40:37] (step=0106000) Train Loss: 0.1127, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-05 23:40:56] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-05 23:41:57] (step=0106100) Train Loss: 0.1127, Train Steps/Sec: 1.26, Samples/Sec: 60.60
+[2025-06-05 23:42:57] (step=0106200) Train Loss: 0.1081, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-05 23:43:58] (step=0106300) Train Loss: 0.1186, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-05 23:45:00] (step=0106400) Train Loss: 0.1143, Train Steps/Sec: 1.63, Samples/Sec: 78.41
+[2025-06-05 23:46:01] (step=0106500) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-05 23:47:02] (step=0106600) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-05 23:48:03] (step=0106700) Train Loss: 0.1169, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 23:49:04] (step=0106800) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-05 23:50:05] (step=0106900) Train Loss: 0.1122, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-05 23:51:06] (step=0107000) Train Loss: 0.1148, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 23:52:07] (step=0107100) Train Loss: 0.1153, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-05 23:53:08] (step=0107200) Train Loss: 0.1163, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-05 23:54:09] (step=0107300) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-05 23:55:10] (step=0107400) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-05 23:56:11] (step=0107500) Train Loss: 0.1127, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 23:57:12] (step=0107600) Train Loss: 0.1165, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-05 23:58:13] (step=0107700) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-05 23:59:14] (step=0107800) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 00:00:15] (step=0107900) Train Loss: 0.1160, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 00:01:16] (step=0108000) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 00:01:30] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 00:02:31] (step=0108100) Train Loss: 0.1145, Train Steps/Sec: 1.34, Samples/Sec: 64.10
+[2025-06-06 00:03:32] (step=0108200) Train Loss: 0.1158, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 00:04:33] (step=0108300) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 00:05:35] (step=0108400) Train Loss: 0.1076, Train Steps/Sec: 1.63, Samples/Sec: 78.25
+[2025-06-06 00:06:36] (step=0108500) Train Loss: 0.1165, Train Steps/Sec: 1.63, Samples/Sec: 78.41
+[2025-06-06 00:07:37] (step=0108600) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 00:08:38] (step=0108700) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 00:09:39] (step=0108800) Train Loss: 0.1134, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 00:10:40] (step=0108900) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 00:11:41] (step=0109000) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 00:12:42] (step=0109100) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 00:13:43] (step=0109200) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 00:14:44] (step=0109300) Train Loss: 0.1160, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 00:15:45] (step=0109400) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 00:16:46] (step=0109500) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 00:17:47] (step=0109600) Train Loss: 0.1158, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 00:18:48] (step=0109700) Train Loss: 0.1139, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 00:19:49] (step=0109800) Train Loss: 0.1110, Train Steps/Sec: 1.63, Samples/Sec: 78.31
+[2025-06-06 00:20:50] (step=0109900) Train Loss: 0.1143, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 00:21:51] (step=0110000) Train Loss: 0.1161, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 00:22:05] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 00:22:08] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 00:24:28] (step=0110000) Perceptual Loss: 0.1373, Eval Time: 142.38
+[2025-06-06 00:25:29] (step=0110100) Train Loss: 0.1104, Train Steps/Sec: 0.46, Samples/Sec: 22.10
+[2025-06-06 00:26:29] (step=0110200) Train Loss: 0.1152, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 00:27:31] (step=0110300) Train Loss: 0.1126, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-06 00:28:32] (step=0110400) Train Loss: 0.1154, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 00:29:33] (step=0110500) Train Loss: 0.1142, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 00:30:34] (step=0110600) Train Loss: 0.1151, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 00:31:35] (step=0110700) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 00:32:17] Beginning epoch 10...
+[2025-06-06 00:32:36] (step=0110800) Train Loss: 0.1108, Train Steps/Sec: 1.63, Samples/Sec: 78.24
+[2025-06-06 00:33:37] (step=0110900) Train Loss: 0.1086, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 00:34:38] (step=0111000) Train Loss: 0.1161, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-06 00:35:39] (step=0111100) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 00:36:40] (step=0111200) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:37:41] (step=0111300) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:38:42] (step=0111400) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:39:43] (step=0111500) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:40:44] (step=0111600) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 00:41:46] (step=0111700) Train Loss: 0.1192, Train Steps/Sec: 1.63, Samples/Sec: 78.05
+[2025-06-06 00:42:47] (step=0111800) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 00:43:48] (step=0111900) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:44:49] (step=0112000) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:45:02] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 00:46:03] (step=0112100) Train Loss: 0.1112, Train Steps/Sec: 1.34, Samples/Sec: 64.15
+[2025-06-06 00:47:04] (step=0112200) Train Loss: 0.1125, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 00:48:05] (step=0112300) Train Loss: 0.1170, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 00:49:07] (step=0112400) Train Loss: 0.1093, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 00:50:08] (step=0112500) Train Loss: 0.1188, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:51:09] (step=0112600) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:52:10] (step=0112700) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:53:10] (step=0112800) Train Loss: 0.1174, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 00:54:11] (step=0112900) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 00:55:13] (step=0113000) Train Loss: 0.1157, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 00:56:14] (step=0113100) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 00:57:15] (step=0113200) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 00:58:16] (step=0113300) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 00:59:17] (step=0113400) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 01:00:18] (step=0113500) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 01:01:19] (step=0113600) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 01:02:20] (step=0113700) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-06 01:03:21] (step=0113800) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 01:04:22] (step=0113900) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 01:05:23] (step=0114000) Train Loss: 0.1051, Train Steps/Sec: 1.63, Samples/Sec: 78.28
+[2025-06-06 01:05:37] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 01:06:38] (step=0114100) Train Loss: 0.1106, Train Steps/Sec: 1.34, Samples/Sec: 64.46
+[2025-06-06 01:07:39] (step=0114200) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 01:08:40] (step=0114300) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 01:09:41] (step=0114400) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 01:10:44] (step=0114500) Train Loss: 0.1200, Train Steps/Sec: 1.57, Samples/Sec: 75.16
+[2025-06-06 01:11:46] (step=0114600) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 01:12:53] (step=0114700) Train Loss: 0.1137, Train Steps/Sec: 1.49, Samples/Sec: 71.66
+[2025-06-06 01:13:54] (step=0114800) Train Loss: 0.1128, Train Steps/Sec: 1.63, Samples/Sec: 78.27
+[2025-06-06 01:14:55] (step=0114900) Train Loss: 0.1160, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 01:15:56] (step=0115000) Train Loss: 0.1144, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-06 01:17:02] (step=0115100) Train Loss: 0.1090, Train Steps/Sec: 1.51, Samples/Sec: 72.34
+[2025-06-06 01:18:04] (step=0115200) Train Loss: 0.1147, Train Steps/Sec: 1.63, Samples/Sec: 78.33
+[2025-06-06 01:19:05] (step=0115300) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 01:20:06] (step=0115400) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 01:21:13] (step=0115500) Train Loss: 0.1107, Train Steps/Sec: 1.48, Samples/Sec: 70.88
+[2025-06-06 01:22:15] (step=0115600) Train Loss: 0.1136, Train Steps/Sec: 1.63, Samples/Sec: 78.26
+[2025-06-06 01:23:16] (step=0115700) Train Loss: 0.1208, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-06 01:24:17] (step=0115800) Train Loss: 0.1163, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 01:25:18] (step=0115900) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 01:26:19] (step=0116000) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 01:26:32] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 01:27:33] (step=0116100) Train Loss: 0.1109, Train Steps/Sec: 1.36, Samples/Sec: 65.11
+[2025-06-06 01:28:36] (step=0116200) Train Loss: 0.1134, Train Steps/Sec: 1.59, Samples/Sec: 76.28
+[2025-06-06 01:29:37] (step=0116300) Train Loss: 0.1085, Train Steps/Sec: 1.63, Samples/Sec: 78.25
+[2025-06-06 01:30:38] (step=0116400) Train Loss: 0.1145, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-06 01:31:39] (step=0116500) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 01:32:42] (step=0116600) Train Loss: 0.1118, Train Steps/Sec: 1.58, Samples/Sec: 75.83
+[2025-06-06 01:33:44] (step=0116700) Train Loss: 0.1073, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-06 01:34:45] (step=0116800) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 01:35:47] (step=0116900) Train Loss: 0.1142, Train Steps/Sec: 1.59, Samples/Sec: 76.56
+[2025-06-06 01:36:48] (step=0117000) Train Loss: 0.1127, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-06 01:37:52] (step=0117100) Train Loss: 0.1140, Train Steps/Sec: 1.57, Samples/Sec: 75.42
+[2025-06-06 01:38:53] (step=0117200) Train Loss: 0.1126, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 01:39:57] (step=0117300) Train Loss: 0.1065, Train Steps/Sec: 1.56, Samples/Sec: 75.11
+[2025-06-06 01:40:58] (step=0117400) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 01:41:59] (step=0117500) Train Loss: 0.1163, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 01:43:00] (step=0117600) Train Loss: 0.1139, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 01:44:01] (step=0117700) Train Loss: 0.1148, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-06 01:45:02] (step=0117800) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 01:46:03] (step=0117900) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 01:47:04] (step=0118000) Train Loss: 0.1149, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 01:47:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 01:48:19] (step=0118100) Train Loss: 0.1179, Train Steps/Sec: 1.34, Samples/Sec: 64.33
+[2025-06-06 01:49:20] (step=0118200) Train Loss: 0.1137, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 01:50:21] (step=0118300) Train Loss: 0.1081, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 01:51:22] (step=0118400) Train Loss: 0.1146, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 01:52:23] (step=0118500) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 01:53:24] (step=0118600) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 01:54:25] (step=0118700) Train Loss: 0.1141, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 01:55:26] (step=0118800) Train Loss: 0.1170, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 01:56:27] (step=0118900) Train Loss: 0.1168, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 01:57:28] (step=0119000) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 01:58:29] (step=0119100) Train Loss: 0.1171, Train Steps/Sec: 1.63, Samples/Sec: 78.48
+[2025-06-06 01:59:30] (step=0119200) Train Loss: 0.1190, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 02:00:31] (step=0119300) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 02:01:32] (step=0119400) Train Loss: 0.1155, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 02:02:33] (step=0119500) Train Loss: 0.1142, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 02:03:34] (step=0119600) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 02:04:35] (step=0119700) Train Loss: 0.1208, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 02:05:36] (step=0119800) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 02:06:37] (step=0119900) Train Loss: 0.1106, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 02:07:38] (step=0120000) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 02:08:04] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0120000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 02:08:06] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 02:10:26] (step=0120000) Perceptual Loss: 0.1524, Eval Time: 142.53
+[2025-06-06 02:11:27] (step=0120100) Train Loss: 0.1123, Train Steps/Sec: 0.44, Samples/Sec: 20.97
+[2025-06-06 02:12:28] (step=0120200) Train Loss: 0.1145, Train Steps/Sec: 1.64, Samples/Sec: 78.83
+[2025-06-06 02:13:29] (step=0120300) Train Loss: 0.1076, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 02:14:30] (step=0120400) Train Loss: 0.1125, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 02:15:35] (step=0120500) Train Loss: 0.1105, Train Steps/Sec: 1.55, Samples/Sec: 74.25
+[2025-06-06 02:16:36] (step=0120600) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 02:17:37] (step=0120700) Train Loss: 0.1091, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 02:18:40] (step=0120800) Train Loss: 0.1164, Train Steps/Sec: 1.59, Samples/Sec: 76.28
+[2025-06-06 02:19:41] (step=0120900) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-06 02:20:42] (step=0121000) Train Loss: 0.1125, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-06 02:21:46] (step=0121100) Train Loss: 0.1112, Train Steps/Sec: 1.57, Samples/Sec: 75.30
+[2025-06-06 02:22:47] (step=0121200) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 02:23:50] (step=0121300) Train Loss: 0.1114, Train Steps/Sec: 1.57, Samples/Sec: 75.50
+[2025-06-06 02:24:53] (step=0121400) Train Loss: 0.1143, Train Steps/Sec: 1.59, Samples/Sec: 76.51
+[2025-06-06 02:25:56] (step=0121500) Train Loss: 0.1108, Train Steps/Sec: 1.61, Samples/Sec: 77.16
+[2025-06-06 02:26:57] (step=0121600) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 02:28:00] (step=0121700) Train Loss: 0.1096, Train Steps/Sec: 1.57, Samples/Sec: 75.50
+[2025-06-06 02:29:01] (step=0121800) Train Loss: 0.1130, Train Steps/Sec: 1.63, Samples/Sec: 78.37
+[2025-06-06 02:29:30] Beginning epoch 11...
+[2025-06-06 02:30:03] (step=0121900) Train Loss: 0.1123, Train Steps/Sec: 1.63, Samples/Sec: 78.30
+[2025-06-06 02:31:04] (step=0122000) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 02:31:16] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 02:32:17] (step=0122100) Train Loss: 0.1081, Train Steps/Sec: 1.36, Samples/Sec: 65.09
+[2025-06-06 02:33:18] (step=0122200) Train Loss: 0.1092, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 02:34:20] (step=0122300) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-06 02:35:21] (step=0122400) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 02:36:22] (step=0122500) Train Loss: 0.1122, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 02:37:23] (step=0122600) Train Loss: 0.1136, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 02:38:24] (step=0122700) Train Loss: 0.1149, Train Steps/Sec: 1.64, Samples/Sec: 78.87
+[2025-06-06 02:39:25] (step=0122800) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 02:40:30] (step=0122900) Train Loss: 0.1141, Train Steps/Sec: 1.53, Samples/Sec: 73.24
+[2025-06-06 02:41:31] (step=0123000) Train Loss: 0.1091, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-06 02:42:32] (step=0123100) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 02:43:33] (step=0123200) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 02:44:35] (step=0123300) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 02:45:36] (step=0123400) Train Loss: 0.1058, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-06 02:46:38] (step=0123500) Train Loss: 0.1158, Train Steps/Sec: 1.61, Samples/Sec: 77.48
+[2025-06-06 02:47:39] (step=0123600) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 02:48:40] (step=0123700) Train Loss: 0.1189, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-06 02:49:41] (step=0123800) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 02:50:42] (step=0123900) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 02:51:43] (step=0124000) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 02:51:56] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 02:52:59] (step=0124100) Train Loss: 0.1110, Train Steps/Sec: 1.32, Samples/Sec: 63.14
+[2025-06-06 02:54:00] (step=0124200) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 02:55:01] (step=0124300) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-06 02:56:02] (step=0124400) Train Loss: 0.1093, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 02:57:03] (step=0124500) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 02:58:04] (step=0124600) Train Loss: 0.1123, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 02:59:05] (step=0124700) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 03:00:06] (step=0124800) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 03:01:07] (step=0124900) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 03:02:08] (step=0125000) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 03:03:10] (step=0125100) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 03:04:11] (step=0125200) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 03:05:12] (step=0125300) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 03:06:13] (step=0125400) Train Loss: 0.1121, Train Steps/Sec: 1.62, Samples/Sec: 77.85
+[2025-06-06 03:07:14] (step=0125500) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 03:08:15] (step=0125600) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 03:09:17] (step=0125700) Train Loss: 0.1168, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-06 03:10:17] (step=0125800) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 03:11:18] (step=0125900) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 03:12:19] (step=0126000) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 03:12:32] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 03:13:33] (step=0126100) Train Loss: 0.1132, Train Steps/Sec: 1.35, Samples/Sec: 64.86
+[2025-06-06 03:14:34] (step=0126200) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 03:15:35] (step=0126300) Train Loss: 0.1161, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 03:16:36] (step=0126400) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 03:17:37] (step=0126500) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 03:18:38] (step=0126600) Train Loss: 0.1059, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 03:19:39] (step=0126700) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 03:20:40] (step=0126800) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 03:21:41] (step=0126900) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 03:22:42] (step=0127000) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 03:23:43] (step=0127100) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 03:24:44] (step=0127200) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 03:25:45] (step=0127300) Train Loss: 0.1157, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 03:26:46] (step=0127400) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 03:27:47] (step=0127500) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 03:28:48] (step=0127600) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 03:29:49] (step=0127700) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 03:30:51] (step=0127800) Train Loss: 0.1128, Train Steps/Sec: 1.63, Samples/Sec: 78.34
+[2025-06-06 03:31:52] (step=0127900) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 03:32:53] (step=0128000) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 03:33:06] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 03:34:07] (step=0128100) Train Loss: 0.1072, Train Steps/Sec: 1.34, Samples/Sec: 64.38
+[2025-06-06 03:35:08] (step=0128200) Train Loss: 0.1145, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 03:36:09] (step=0128300) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 03:37:10] (step=0128400) Train Loss: 0.1090, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-06 03:38:11] (step=0128500) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 03:39:12] (step=0128600) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 03:40:13] (step=0128700) Train Loss: 0.1145, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 03:41:14] (step=0128800) Train Loss: 0.1169, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 03:42:15] (step=0128900) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 03:43:16] (step=0129000) Train Loss: 0.1144, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 03:44:17] (step=0129100) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 03:45:18] (step=0129200) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 03:46:19] (step=0129300) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 03:47:20] (step=0129400) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 03:48:21] (step=0129500) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 03:49:22] (step=0129600) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 03:50:24] (step=0129700) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 03:51:25] (step=0129800) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 03:52:26] (step=0129900) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 03:53:27] (step=0130000) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 03:53:40] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 03:53:43] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 03:56:03] (step=0130000) Perceptual Loss: 0.1534, Eval Time: 142.37
+[2025-06-06 03:57:04] (step=0130100) Train Loss: 0.1053, Train Steps/Sec: 0.46, Samples/Sec: 22.12
+[2025-06-06 03:58:05] (step=0130200) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 03:59:06] (step=0130300) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-06 04:00:07] (step=0130400) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 04:01:08] (step=0130500) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 04:02:09] (step=0130600) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 04:03:10] (step=0130700) Train Loss: 0.1141, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 04:04:11] (step=0130800) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 04:05:12] (step=0130900) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 04:06:12] (step=0131000) Train Loss: 0.1157, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 04:07:13] (step=0131100) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.86
+[2025-06-06 04:08:14] (step=0131200) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 04:09:15] (step=0131300) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 04:10:17] (step=0131400) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 04:11:17] (step=0131500) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 04:12:19] (step=0131600) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 04:13:20] (step=0131700) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 04:14:21] (step=0131800) Train Loss: 0.1187, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 04:15:22] (step=0131900) Train Loss: 0.1061, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 04:16:22] (step=0132000) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 04:16:36] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 04:17:37] (step=0132100) Train Loss: 0.1079, Train Steps/Sec: 1.34, Samples/Sec: 64.33
+[2025-06-06 04:18:38] (step=0132200) Train Loss: 0.1159, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 04:19:39] (step=0132300) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 04:20:40] (step=0132400) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 04:21:41] (step=0132500) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 04:22:42] (step=0132600) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 04:23:43] (step=0132700) Train Loss: 0.1157, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 04:24:44] (step=0132800) Train Loss: 0.1172, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 04:25:45] (step=0132900) Train Loss: 0.1122, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 04:26:00] Beginning epoch 12...
+[2025-06-06 04:26:47] (step=0133000) Train Loss: 0.1106, Train Steps/Sec: 1.63, Samples/Sec: 78.09
+[2025-06-06 04:27:48] (step=0133100) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 04:28:49] (step=0133200) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 04:29:50] (step=0133300) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 04:30:51] (step=0133400) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 04:31:52] (step=0133500) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 04:32:53] (step=0133600) Train Loss: 0.1201, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 04:33:54] (step=0133700) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 04:34:55] (step=0133800) Train Loss: 0.1156, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 04:35:56] (step=0133900) Train Loss: 0.1072, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 04:36:57] (step=0134000) Train Loss: 0.1106, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 04:37:10] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 04:38:11] (step=0134100) Train Loss: 0.1145, Train Steps/Sec: 1.34, Samples/Sec: 64.48
+[2025-06-06 04:39:12] (step=0134200) Train Loss: 0.1068, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 04:40:13] (step=0134300) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 04:41:14] (step=0134400) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 04:42:15] (step=0134500) Train Loss: 0.1090, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 04:43:16] (step=0134600) Train Loss: 0.1061, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 04:44:17] (step=0134700) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 04:45:18] (step=0134800) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 04:46:19] (step=0134900) Train Loss: 0.1167, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 04:47:20] (step=0135000) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 04:48:21] (step=0135100) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 04:49:22] (step=0135200) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 04:50:23] (step=0135300) Train Loss: 0.1053, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 04:51:24] (step=0135400) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 04:52:25] (step=0135500) Train Loss: 0.1103, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 04:53:26] (step=0135600) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 04:54:27] (step=0135700) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 04:55:28] (step=0135800) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 04:56:29] (step=0135900) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 04:57:30] (step=0136000) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 04:57:44] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 04:58:44] (step=0136100) Train Loss: 0.1048, Train Steps/Sec: 1.35, Samples/Sec: 64.60
+[2025-06-06 04:59:45] (step=0136200) Train Loss: 0.1143, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 05:00:47] (step=0136300) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 05:01:48] (step=0136400) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 05:02:49] (step=0136500) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 05:03:50] (step=0136600) Train Loss: 0.1122, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 05:04:51] (step=0136700) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 05:05:52] (step=0136800) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 05:06:53] (step=0136900) Train Loss: 0.1158, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 05:07:54] (step=0137000) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 05:08:55] (step=0137100) Train Loss: 0.1171, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 05:09:56] (step=0137200) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 05:10:57] (step=0137300) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 05:11:58] (step=0137400) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 05:12:59] (step=0137500) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 05:14:00] (step=0137600) Train Loss: 0.1135, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 05:15:01] (step=0137700) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 05:16:02] (step=0137800) Train Loss: 0.1090, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 05:17:03] (step=0137900) Train Loss: 0.1053, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 05:18:04] (step=0138000) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 05:18:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 05:19:18] (step=0138100) Train Loss: 0.1134, Train Steps/Sec: 1.34, Samples/Sec: 64.41
+[2025-06-06 05:20:19] (step=0138200) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 05:21:20] (step=0138300) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 05:22:21] (step=0138400) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 05:23:22] (step=0138500) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 05:24:23] (step=0138600) Train Loss: 0.1122, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 05:25:24] (step=0138700) Train Loss: 0.1127, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 05:26:25] (step=0138800) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 05:27:26] (step=0138900) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 05:28:27] (step=0139000) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 05:29:28] (step=0139100) Train Loss: 0.1103, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 05:30:29] (step=0139200) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 05:31:30] (step=0139300) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 05:32:31] (step=0139400) Train Loss: 0.1052, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 05:33:32] (step=0139500) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.95
+[2025-06-06 05:34:33] (step=0139600) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 05:35:34] (step=0139700) Train Loss: 0.1065, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 05:36:35] (step=0139800) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-06 05:37:36] (step=0139900) Train Loss: 0.1154, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 05:38:37] (step=0140000) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 05:39:03] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0140000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 05:39:06] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 05:41:26] (step=0140000) Perceptual Loss: 0.1311, Eval Time: 142.33
+[2025-06-06 05:42:26] (step=0140100) Train Loss: 0.1152, Train Steps/Sec: 0.44, Samples/Sec: 20.96
+[2025-06-06 05:43:27] (step=0140200) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 05:44:28] (step=0140300) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 05:45:30] (step=0140400) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 05:46:30] (step=0140500) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 05:47:31] (step=0140600) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 05:48:32] (step=0140700) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 05:49:33] (step=0140800) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.83
+[2025-06-06 05:50:34] (step=0140900) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 05:51:35] (step=0141000) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 05:52:36] (step=0141100) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 05:53:37] (step=0141200) Train Loss: 0.1081, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 05:54:38] (step=0141300) Train Loss: 0.1017, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 05:55:39] (step=0141400) Train Loss: 0.1065, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 05:56:40] (step=0141500) Train Loss: 0.1122, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 05:57:41] (step=0141600) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 05:58:42] (step=0141700) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 05:59:43] (step=0141800) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 06:00:44] (step=0141900) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 06:01:45] (step=0142000) Train Loss: 0.1127, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 06:01:59] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 06:03:00] (step=0142100) Train Loss: 0.1107, Train Steps/Sec: 1.34, Samples/Sec: 64.46
+[2025-06-06 06:04:01] (step=0142200) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 06:05:02] (step=0142300) Train Loss: 0.1137, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 06:06:03] (step=0142400) Train Loss: 0.1166, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-06 06:07:04] (step=0142500) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 06:08:05] (step=0142600) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 06:09:06] (step=0142700) Train Loss: 0.1142, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 06:10:06] (step=0142800) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 06:11:07] (step=0142900) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 06:12:09] (step=0143000) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 06:13:10] (step=0143100) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 06:14:10] (step=0143200) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 06:15:11] (step=0143300) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 06:16:12] (step=0143400) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 06:17:13] (step=0143500) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 06:18:14] (step=0143600) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 06:19:15] (step=0143700) Train Loss: 0.1093, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 06:20:16] (step=0143800) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 06:21:17] (step=0143900) Train Loss: 0.1069, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 06:22:18] (step=0144000) Train Loss: 0.1125, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 06:22:32] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 06:22:32] Beginning epoch 13...
+[2025-06-06 06:23:33] (step=0144100) Train Loss: 0.1118, Train Steps/Sec: 1.34, Samples/Sec: 64.22
+[2025-06-06 06:24:34] (step=0144200) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 06:25:35] (step=0144300) Train Loss: 0.1189, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-06 06:26:36] (step=0144400) Train Loss: 0.1140, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 06:27:37] (step=0144500) Train Loss: 0.1129, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 06:28:38] (step=0144600) Train Loss: 0.1161, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 06:29:39] (step=0144700) Train Loss: 0.1086, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 06:30:40] (step=0144800) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 06:31:41] (step=0144900) Train Loss: 0.1091, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 06:32:42] (step=0145000) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 06:33:43] (step=0145100) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 06:34:44] (step=0145200) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 06:35:45] (step=0145300) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 06:36:46] (step=0145400) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 06:37:47] (step=0145500) Train Loss: 0.1070, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 06:38:48] (step=0145600) Train Loss: 0.1174, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 06:39:49] (step=0145700) Train Loss: 0.1059, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 06:40:50] (step=0145800) Train Loss: 0.1154, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 06:41:51] (step=0145900) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 06:42:52] (step=0146000) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 06:43:10] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 06:44:11] (step=0146100) Train Loss: 0.1044, Train Steps/Sec: 1.26, Samples/Sec: 60.31
+[2025-06-06 06:45:12] (step=0146200) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 06:46:13] (step=0146300) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 06:47:14] (step=0146400) Train Loss: 0.1069, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 06:48:15] (step=0146500) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 06:49:16] (step=0146600) Train Loss: 0.1059, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 06:50:17] (step=0146700) Train Loss: 0.1167, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 06:51:18] (step=0146800) Train Loss: 0.1086, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 06:52:19] (step=0146900) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 06:53:20] (step=0147000) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-06 06:54:21] (step=0147100) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 06:55:22] (step=0147200) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 06:56:23] (step=0147300) Train Loss: 0.1149, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 06:57:24] (step=0147400) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 06:58:25] (step=0147500) Train Loss: 0.1125, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 06:59:26] (step=0147600) Train Loss: 0.1147, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 07:00:27] (step=0147700) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 07:01:28] (step=0147800) Train Loss: 0.1091, Train Steps/Sec: 1.64, Samples/Sec: 78.85
+[2025-06-06 07:02:29] (step=0147900) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:03:30] (step=0148000) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 07:03:44] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 07:04:45] (step=0148100) Train Loss: 0.1113, Train Steps/Sec: 1.34, Samples/Sec: 64.35
+[2025-06-06 07:05:46] (step=0148200) Train Loss: 0.1047, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 07:06:47] (step=0148300) Train Loss: 0.1116, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-06 07:07:48] (step=0148400) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 07:08:49] (step=0148500) Train Loss: 0.1065, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:09:50] (step=0148600) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 07:10:51] (step=0148700) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:11:52] (step=0148800) Train Loss: 0.1153, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:12:53] (step=0148900) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 07:13:54] (step=0149000) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 07:14:55] (step=0149100) Train Loss: 0.1106, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 07:15:56] (step=0149200) Train Loss: 0.1126, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:16:57] (step=0149300) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:17:58] (step=0149400) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 07:18:59] (step=0149500) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:20:00] (step=0149600) Train Loss: 0.1058, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 07:21:01] (step=0149700) Train Loss: 0.1092, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 07:22:02] (step=0149800) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 07:23:03] (step=0149900) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:24:04] (step=0150000) Train Loss: 0.1125, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 07:24:18] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 07:24:20] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 07:26:40] (step=0150000) Perceptual Loss: 0.1386, Eval Time: 142.33
+[2025-06-06 07:27:41] (step=0150100) Train Loss: 0.1117, Train Steps/Sec: 0.46, Samples/Sec: 22.13
+[2025-06-06 07:28:42] (step=0150200) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 07:29:43] (step=0150300) Train Loss: 0.1144, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 07:30:44] (step=0150400) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 07:31:45] (step=0150500) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 07:32:46] (step=0150600) Train Loss: 0.1150, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 07:33:47] (step=0150700) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 07:34:48] (step=0150800) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 07:35:49] (step=0150900) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 07:36:50] (step=0151000) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 07:37:51] (step=0151100) Train Loss: 0.1069, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:38:52] (step=0151200) Train Loss: 0.1106, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 07:39:53] (step=0151300) Train Loss: 0.1123, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 07:40:54] (step=0151400) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 07:41:55] (step=0151500) Train Loss: 0.1058, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 07:42:56] (step=0151600) Train Loss: 0.1066, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-06 07:43:57] (step=0151700) Train Loss: 0.1042, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 07:44:58] (step=0151800) Train Loss: 0.1126, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 07:45:59] (step=0151900) Train Loss: 0.1049, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 07:47:00] (step=0152000) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.76
+[2025-06-06 07:47:13] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 07:48:14] (step=0152100) Train Loss: 0.1028, Train Steps/Sec: 1.34, Samples/Sec: 64.51
+[2025-06-06 07:49:15] (step=0152200) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-06 07:50:16] (step=0152300) Train Loss: 0.1136, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 07:51:17] (step=0152400) Train Loss: 0.1165, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 07:52:18] (step=0152500) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 07:53:19] (step=0152600) Train Loss: 0.1051, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 07:54:20] (step=0152700) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 07:55:21] (step=0152800) Train Loss: 0.1168, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 07:56:22] (step=0152900) Train Loss: 0.1162, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-06 07:57:23] (step=0153000) Train Loss: 0.1061, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 07:58:24] (step=0153100) Train Loss: 0.1046, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 07:59:25] (step=0153200) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 08:00:26] (step=0153300) Train Loss: 0.1058, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 08:01:27] (step=0153400) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 08:02:28] (step=0153500) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 08:03:29] (step=0153600) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-06 08:04:30] (step=0153700) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 08:05:31] (step=0153800) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 08:06:32] (step=0153900) Train Loss: 0.1138, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 08:07:33] (step=0154000) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 08:07:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 08:08:48] (step=0154100) Train Loss: 0.1120, Train Steps/Sec: 1.34, Samples/Sec: 64.21
+[2025-06-06 08:09:49] (step=0154200) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 08:10:50] (step=0154300) Train Loss: 0.1113, Train Steps/Sec: 1.63, Samples/Sec: 78.47
+[2025-06-06 08:11:51] (step=0154400) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 08:12:52] (step=0154500) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 08:13:53] (step=0154600) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 08:14:54] (step=0154700) Train Loss: 0.1103, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 08:15:55] (step=0154800) Train Loss: 0.1081, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 08:16:56] (step=0154900) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-06 08:17:57] (step=0155000) Train Loss: 0.1074, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 08:18:45] Beginning epoch 14...
+[2025-06-06 08:18:59] (step=0155100) Train Loss: 0.1069, Train Steps/Sec: 1.63, Samples/Sec: 78.34
+[2025-06-06 08:20:00] (step=0155200) Train Loss: 0.1061, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 08:21:01] (step=0155300) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 08:22:02] (step=0155400) Train Loss: 0.1061, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 08:23:03] (step=0155500) Train Loss: 0.1139, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 08:24:04] (step=0155600) Train Loss: 0.1106, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-06 08:25:05] (step=0155700) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 08:26:06] (step=0155800) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 08:27:07] (step=0155900) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 08:28:07] (step=0156000) Train Loss: 0.1080, Train Steps/Sec: 1.65, Samples/Sec: 78.97
+[2025-06-06 08:28:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 08:29:21] (step=0156100) Train Loss: 0.1073, Train Steps/Sec: 1.36, Samples/Sec: 65.37
+[2025-06-06 08:30:22] (step=0156200) Train Loss: 0.1146, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 08:31:23] (step=0156300) Train Loss: 0.1068, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 08:32:24] (step=0156400) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 08:33:25] (step=0156500) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 08:34:26] (step=0156600) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 08:35:27] (step=0156700) Train Loss: 0.1059, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 08:36:28] (step=0156800) Train Loss: 0.1147, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 08:37:29] (step=0156900) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 08:38:30] (step=0157000) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 08:39:31] (step=0157100) Train Loss: 0.1090, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 08:40:32] (step=0157200) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 08:41:33] (step=0157300) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 08:42:34] (step=0157400) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 08:43:35] (step=0157500) Train Loss: 0.1019, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 08:44:36] (step=0157600) Train Loss: 0.1086, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 08:45:37] (step=0157700) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 08:46:38] (step=0157800) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 08:47:39] (step=0157900) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 08:48:40] (step=0158000) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 08:48:53] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 08:49:54] (step=0158100) Train Loss: 0.1057, Train Steps/Sec: 1.34, Samples/Sec: 64.41
+[2025-06-06 08:50:55] (step=0158200) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 08:51:56] (step=0158300) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 08:52:57] (step=0158400) Train Loss: 0.1068, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 08:53:58] (step=0158500) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 08:54:59] (step=0158600) Train Loss: 0.1046, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 08:56:00] (step=0158700) Train Loss: 0.1046, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 08:57:01] (step=0158800) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 08:58:02] (step=0158900) Train Loss: 0.1065, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 08:59:03] (step=0159000) Train Loss: 0.1136, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 09:00:04] (step=0159100) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 09:01:05] (step=0159200) Train Loss: 0.1022, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 09:02:06] (step=0159300) Train Loss: 0.1053, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 09:03:07] (step=0159400) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 09:04:08] (step=0159500) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 09:05:09] (step=0159600) Train Loss: 0.1071, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 09:06:10] (step=0159700) Train Loss: 0.1156, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 09:07:11] (step=0159800) Train Loss: 0.1037, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 09:08:12] (step=0159900) Train Loss: 0.1033, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 09:09:13] (step=0160000) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 09:09:39] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0160000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 09:09:41] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 09:12:01] (step=0160000) Perceptual Loss: 0.1206, Eval Time: 142.37
+[2025-06-06 09:13:02] (step=0160100) Train Loss: 0.1150, Train Steps/Sec: 0.44, Samples/Sec: 20.95
+[2025-06-06 09:14:03] (step=0160200) Train Loss: 0.1037, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 09:15:04] (step=0160300) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 09:16:05] (step=0160400) Train Loss: 0.1137, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 09:17:06] (step=0160500) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 09:18:07] (step=0160600) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 09:19:08] (step=0160700) Train Loss: 0.1123, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 09:20:09] (step=0160800) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 09:21:10] (step=0160900) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 09:22:11] (step=0161000) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 09:23:12] (step=0161100) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 09:24:13] (step=0161200) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 09:25:14] (step=0161300) Train Loss: 0.1035, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 09:26:15] (step=0161400) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 09:27:16] (step=0161500) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 09:28:17] (step=0161600) Train Loss: 0.1096, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 09:29:18] (step=0161700) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 09:30:19] (step=0161800) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 09:31:20] (step=0161900) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 09:32:21] (step=0162000) Train Loss: 0.1035, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 09:32:35] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 09:33:36] (step=0162100) Train Loss: 0.1127, Train Steps/Sec: 1.34, Samples/Sec: 64.24
+[2025-06-06 09:34:37] (step=0162200) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 09:35:38] (step=0162300) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 09:36:39] (step=0162400) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 09:37:40] (step=0162500) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 09:38:41] (step=0162600) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 09:39:42] (step=0162700) Train Loss: 0.1173, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 09:40:43] (step=0162800) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 09:41:44] (step=0162900) Train Loss: 0.1131, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 09:42:45] (step=0163000) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 09:43:46] (step=0163100) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 09:44:47] (step=0163200) Train Loss: 0.1031, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 09:45:48] (step=0163300) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 09:46:49] (step=0163400) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 09:47:50] (step=0163500) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 09:48:51] (step=0163600) Train Loss: 0.1045, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 09:49:52] (step=0163700) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 09:50:53] (step=0163800) Train Loss: 0.1065, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 09:51:54] (step=0163900) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 09:52:55] (step=0164000) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 09:53:09] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 09:54:10] (step=0164100) Train Loss: 0.1031, Train Steps/Sec: 1.34, Samples/Sec: 64.43
+[2025-06-06 09:55:11] (step=0164200) Train Loss: 0.1038, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 09:56:12] (step=0164300) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 09:57:13] (step=0164400) Train Loss: 0.1156, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 09:58:14] (step=0164500) Train Loss: 0.1093, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 09:59:15] (step=0164600) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 10:00:16] (step=0164700) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 10:01:17] (step=0164800) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 10:02:18] (step=0164900) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 10:03:19] (step=0165000) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 10:04:20] (step=0165100) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 10:05:21] (step=0165200) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 10:06:22] (step=0165300) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 10:07:23] (step=0165400) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 10:08:24] (step=0165500) Train Loss: 0.1093, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 10:09:25] (step=0165600) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 10:10:26] (step=0165700) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 10:11:27] (step=0165800) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 10:12:28] (step=0165900) Train Loss: 0.1103, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 10:13:29] (step=0166000) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 10:13:42] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 10:14:43] (step=0166100) Train Loss: 0.1151, Train Steps/Sec: 1.34, Samples/Sec: 64.50
+[2025-06-06 10:15:17] Beginning epoch 15...
+[2025-06-06 10:15:45] (step=0166200) Train Loss: 0.1076, Train Steps/Sec: 1.63, Samples/Sec: 78.24
+[2025-06-06 10:16:46] (step=0166300) Train Loss: 0.1087, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-06 10:17:47] (step=0166400) Train Loss: 0.1144, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 10:18:48] (step=0166500) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 10:19:49] (step=0166600) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 10:20:50] (step=0166700) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 10:21:51] (step=0166800) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 10:22:52] (step=0166900) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 10:23:53] (step=0167000) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 10:24:54] (step=0167100) Train Loss: 0.1035, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 10:25:55] (step=0167200) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 10:26:56] (step=0167300) Train Loss: 0.1014, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 10:27:57] (step=0167400) Train Loss: 0.1014, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 10:28:58] (step=0167500) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 10:29:59] (step=0167600) Train Loss: 0.1126, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 10:31:00] (step=0167700) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 10:32:01] (step=0167800) Train Loss: 0.1093, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 10:33:02] (step=0167900) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 10:34:03] (step=0168000) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 10:34:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 10:35:18] (step=0168100) Train Loss: 0.1093, Train Steps/Sec: 1.34, Samples/Sec: 64.45
+[2025-06-06 10:36:19] (step=0168200) Train Loss: 0.1131, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 10:37:20] (step=0168300) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 10:38:21] (step=0168400) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 10:39:22] (step=0168500) Train Loss: 0.1125, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 10:40:23] (step=0168600) Train Loss: 0.1032, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 10:41:24] (step=0168700) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 10:42:25] (step=0168800) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 10:43:26] (step=0168900) Train Loss: 0.1093, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 10:44:27] (step=0169000) Train Loss: 0.1098, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-06 10:45:28] (step=0169100) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 10:46:30] (step=0169200) Train Loss: 0.1047, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 10:47:31] (step=0169300) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 10:48:32] (step=0169400) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 10:49:33] (step=0169500) Train Loss: 0.1040, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 10:50:34] (step=0169600) Train Loss: 0.1045, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 10:51:35] (step=0169700) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 10:52:36] (step=0169800) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 10:53:37] (step=0169900) Train Loss: 0.1022, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 10:54:38] (step=0170000) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 10:54:52] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 10:54:55] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 10:57:14] (step=0170000) Perceptual Loss: 0.1218, Eval Time: 142.35
+[2025-06-06 10:58:15] (step=0170100) Train Loss: 0.1092, Train Steps/Sec: 0.46, Samples/Sec: 22.10
+[2025-06-06 10:59:16] (step=0170200) Train Loss: 0.1088, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-06 11:00:17] (step=0170300) Train Loss: 0.1058, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 11:01:18] (step=0170400) Train Loss: 0.1103, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:02:19] (step=0170500) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.50
+[2025-06-06 11:03:20] (step=0170600) Train Loss: 0.1051, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:04:21] (step=0170700) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:05:22] (step=0170800) Train Loss: 0.1123, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 11:06:24] (step=0170900) Train Loss: 0.1067, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-06 11:07:25] (step=0171000) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 11:08:26] (step=0171100) Train Loss: 0.1134, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 11:09:27] (step=0171200) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 11:10:28] (step=0171300) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 11:11:29] (step=0171400) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 11:12:30] (step=0171500) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 11:13:31] (step=0171600) Train Loss: 0.1116, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 11:14:32] (step=0171700) Train Loss: 0.1064, Train Steps/Sec: 1.65, Samples/Sec: 78.98
+[2025-06-06 11:15:33] (step=0171800) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 11:16:34] (step=0171900) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-06 11:17:35] (step=0172000) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 11:17:48] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 11:18:49] (step=0172100) Train Loss: 0.1149, Train Steps/Sec: 1.34, Samples/Sec: 64.37
+[2025-06-06 11:19:50] (step=0172200) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 11:20:52] (step=0172300) Train Loss: 0.1092, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 11:21:53] (step=0172400) Train Loss: 0.1223, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 11:22:54] (step=0172500) Train Loss: 0.1035, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 11:23:55] (step=0172600) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:24:56] (step=0172700) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:25:57] (step=0172800) Train Loss: 0.1068, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 11:26:58] (step=0172900) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 11:27:59] (step=0173000) Train Loss: 0.1136, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 11:29:00] (step=0173100) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:30:01] (step=0173200) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 11:31:02] (step=0173300) Train Loss: 0.1066, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 11:32:03] (step=0173400) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 11:33:04] (step=0173500) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 11:34:05] (step=0173600) Train Loss: 0.1084, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-06 11:35:06] (step=0173700) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 11:36:07] (step=0173800) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 11:37:08] (step=0173900) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 11:38:09] (step=0174000) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:38:23] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 11:39:24] (step=0174100) Train Loss: 0.1173, Train Steps/Sec: 1.34, Samples/Sec: 64.25
+[2025-06-06 11:40:25] (step=0174200) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 11:41:26] (step=0174300) Train Loss: 0.1102, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 11:42:27] (step=0174400) Train Loss: 0.1023, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 11:43:28] (step=0174500) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:44:29] (step=0174600) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 11:45:30] (step=0174700) Train Loss: 0.1018, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 11:46:31] (step=0174800) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 11:47:32] (step=0174900) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 11:48:33] (step=0175000) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 11:49:34] (step=0175100) Train Loss: 0.1008, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 11:50:35] (step=0175200) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 11:51:37] (step=0175300) Train Loss: 0.1081, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:52:38] (step=0175400) Train Loss: 0.1119, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 11:53:38] (step=0175500) Train Loss: 0.1029, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 11:54:40] (step=0175600) Train Loss: 0.1071, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-06 11:55:41] (step=0175700) Train Loss: 0.1051, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 11:56:42] (step=0175800) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 11:57:43] (step=0175900) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 11:58:44] (step=0176000) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 11:58:57] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 11:59:58] (step=0176100) Train Loss: 0.1076, Train Steps/Sec: 1.34, Samples/Sec: 64.39
+[2025-06-06 12:00:59] (step=0176200) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 12:02:01] (step=0176300) Train Loss: 0.1075, Train Steps/Sec: 1.63, Samples/Sec: 78.44
+[2025-06-06 12:03:02] (step=0176400) Train Loss: 0.1049, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 12:04:03] (step=0176500) Train Loss: 0.1115, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 12:05:04] (step=0176600) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 12:06:05] (step=0176700) Train Loss: 0.1015, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:07:06] (step=0176800) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:08:07] (step=0176900) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 12:09:08] (step=0177000) Train Loss: 0.1076, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 12:10:09] (step=0177100) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 12:11:10] (step=0177200) Train Loss: 0.1091, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:11:29] Beginning epoch 16...
+[2025-06-06 12:12:11] (step=0177300) Train Loss: 0.1018, Train Steps/Sec: 1.63, Samples/Sec: 78.19
+[2025-06-06 12:13:12] (step=0177400) Train Loss: 0.1152, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 12:14:13] (step=0177500) Train Loss: 0.1012, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 12:15:14] (step=0177600) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 12:16:16] (step=0177700) Train Loss: 0.1033, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-06 12:17:17] (step=0177800) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 12:18:18] (step=0177900) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 12:19:19] (step=0178000) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:19:32] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 12:20:33] (step=0178100) Train Loss: 0.1074, Train Steps/Sec: 1.34, Samples/Sec: 64.26
+[2025-06-06 12:21:34] (step=0178200) Train Loss: 0.1071, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:22:36] (step=0178300) Train Loss: 0.1082, Train Steps/Sec: 1.63, Samples/Sec: 78.34
+[2025-06-06 12:23:37] (step=0178400) Train Loss: 0.1081, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:24:38] (step=0178500) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:25:39] (step=0178600) Train Loss: 0.1139, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 12:26:40] (step=0178700) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 12:27:41] (step=0178800) Train Loss: 0.1086, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 12:28:42] (step=0178900) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:29:43] (step=0179000) Train Loss: 0.1107, Train Steps/Sec: 1.63, Samples/Sec: 78.34
+[2025-06-06 12:30:44] (step=0179100) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.83
+[2025-06-06 12:31:44] (step=0179200) Train Loss: 0.1089, Train Steps/Sec: 1.65, Samples/Sec: 78.97
+[2025-06-06 12:32:45] (step=0179300) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 12:33:46] (step=0179400) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 12:34:47] (step=0179500) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:35:49] (step=0179600) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 12:36:50] (step=0179700) Train Loss: 0.1091, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-06 12:37:51] (step=0179800) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 12:38:52] (step=0179900) Train Loss: 0.1038, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 12:39:53] (step=0180000) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 12:40:19] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0180000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 12:40:21] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 12:42:41] (step=0180000) Perceptual Loss: 0.1395, Eval Time: 142.40
+[2025-06-06 12:43:42] (step=0180100) Train Loss: 0.1081, Train Steps/Sec: 0.44, Samples/Sec: 20.95
+[2025-06-06 12:44:43] (step=0180200) Train Loss: 0.1112, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 12:45:44] (step=0180300) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 12:46:45] (step=0180400) Train Loss: 0.1046, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 12:47:46] (step=0180500) Train Loss: 0.1024, Train Steps/Sec: 1.64, Samples/Sec: 78.73
+[2025-06-06 12:48:47] (step=0180600) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 12:49:48] (step=0180700) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 12:50:49] (step=0180800) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 12:51:50] (step=0180900) Train Loss: 0.1106, Train Steps/Sec: 1.63, Samples/Sec: 78.35
+[2025-06-06 12:52:51] (step=0181000) Train Loss: 0.1091, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 12:53:52] (step=0181100) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 12:54:53] (step=0181200) Train Loss: 0.1042, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:55:54] (step=0181300) Train Loss: 0.1055, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 12:56:55] (step=0181400) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 12:57:56] (step=0181500) Train Loss: 0.1130, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 12:58:57] (step=0181600) Train Loss: 0.1072, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-06 12:59:58] (step=0181700) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:00:59] (step=0181800) Train Loss: 0.1109, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:02:00] (step=0181900) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:03:01] (step=0182000) Train Loss: 0.1092, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 13:03:15] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 13:04:16] (step=0182100) Train Loss: 0.1093, Train Steps/Sec: 1.34, Samples/Sec: 64.42
+[2025-06-06 13:05:17] (step=0182200) Train Loss: 0.1070, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-06 13:06:18] (step=0182300) Train Loss: 0.1134, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 13:07:19] (step=0182400) Train Loss: 0.1025, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:08:20] (step=0182500) Train Loss: 0.1039, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:09:21] (step=0182600) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:10:22] (step=0182700) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 13:11:23] (step=0182800) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:12:24] (step=0182900) Train Loss: 0.1084, Train Steps/Sec: 1.63, Samples/Sec: 78.34
+[2025-06-06 13:13:25] (step=0183000) Train Loss: 0.1090, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 13:14:26] (step=0183100) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 13:15:27] (step=0183200) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:16:28] (step=0183300) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 13:17:29] (step=0183400) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 13:18:30] (step=0183500) Train Loss: 0.1041, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:19:32] (step=0183600) Train Loss: 0.1044, Train Steps/Sec: 1.63, Samples/Sec: 78.36
+[2025-06-06 13:20:33] (step=0183700) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:21:34] (step=0183800) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:22:35] (step=0183900) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:23:36] (step=0184000) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 13:23:50] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 13:24:51] (step=0184100) Train Loss: 0.1055, Train Steps/Sec: 1.33, Samples/Sec: 64.01
+[2025-06-06 13:25:52] (step=0184200) Train Loss: 0.1052, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 13:26:53] (step=0184300) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 13:27:54] (step=0184400) Train Loss: 0.1012, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 13:28:55] (step=0184500) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 13:29:56] (step=0184600) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 13:30:57] (step=0184700) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 13:31:58] (step=0184800) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 13:32:59] (step=0184900) Train Loss: 0.1031, Train Steps/Sec: 1.63, Samples/Sec: 78.40
+[2025-06-06 13:34:00] (step=0185000) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 13:35:01] (step=0185100) Train Loss: 0.1065, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 13:36:02] (step=0185200) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:37:03] (step=0185300) Train Loss: 0.1012, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:38:04] (step=0185400) Train Loss: 0.1070, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:39:05] (step=0185500) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:40:06] (step=0185600) Train Loss: 0.1059, Train Steps/Sec: 1.63, Samples/Sec: 78.39
+[2025-06-06 13:41:07] (step=0185700) Train Loss: 0.1028, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 13:42:08] (step=0185800) Train Loss: 0.1046, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:43:09] (step=0185900) Train Loss: 0.1086, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 13:44:10] (step=0186000) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 13:44:24] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 13:45:25] (step=0186100) Train Loss: 0.1058, Train Steps/Sec: 1.34, Samples/Sec: 64.34
+[2025-06-06 13:46:26] (step=0186200) Train Loss: 0.1034, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 13:47:27] (step=0186300) Train Loss: 0.1068, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 13:48:28] (step=0186400) Train Loss: 0.1107, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-06 13:49:29] (step=0186500) Train Loss: 0.1050, Train Steps/Sec: 1.65, Samples/Sec: 78.97
+[2025-06-06 13:50:30] (step=0186600) Train Loss: 0.1035, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 13:51:31] (step=0186700) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 13:52:32] (step=0186800) Train Loss: 0.1052, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 13:53:33] (step=0186900) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 13:54:34] (step=0187000) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 13:55:35] (step=0187100) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:56:36] (step=0187200) Train Loss: 0.0983, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 13:57:37] (step=0187300) Train Loss: 0.1065, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 13:58:38] (step=0187400) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 13:59:39] (step=0187500) Train Loss: 0.1061, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 14:00:40] (step=0187600) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.48
+[2025-06-06 14:01:41] (step=0187700) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 14:02:42] (step=0187800) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 14:03:43] (step=0187900) Train Loss: 0.1071, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 14:04:44] (step=0188000) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 14:04:58] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 14:05:59] (step=0188100) Train Loss: 0.1039, Train Steps/Sec: 1.34, Samples/Sec: 64.31
+[2025-06-06 14:07:00] (step=0188200) Train Loss: 0.1147, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 14:08:01] (step=0188300) Train Loss: 0.1019, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 14:08:07] Beginning epoch 17...
+[2025-06-06 14:09:03] (step=0188400) Train Loss: 0.1088, Train Steps/Sec: 1.63, Samples/Sec: 78.13
+[2025-06-06 14:10:04] (step=0188500) Train Loss: 0.1026, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 14:11:05] (step=0188600) Train Loss: 0.1090, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 14:12:06] (step=0188700) Train Loss: 0.1053, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 14:13:07] (step=0188800) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 14:14:08] (step=0188900) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 14:15:09] (step=0189000) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 14:16:10] (step=0189100) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 14:17:11] (step=0189200) Train Loss: 0.1026, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 14:18:12] (step=0189300) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 14:19:13] (step=0189400) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 14:20:14] (step=0189500) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 14:21:15] (step=0189600) Train Loss: 0.1067, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-06 14:22:16] (step=0189700) Train Loss: 0.1076, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 14:23:17] (step=0189800) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 14:24:18] (step=0189900) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 14:25:19] (step=0190000) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 14:25:33] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 14:25:36] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 14:27:55] (step=0190000) Perceptual Loss: 0.1319, Eval Time: 142.28
+[2025-06-06 14:28:56] (step=0190100) Train Loss: 0.1085, Train Steps/Sec: 0.46, Samples/Sec: 22.10
+[2025-06-06 14:29:57] (step=0190200) Train Loss: 0.1034, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 14:30:58] (step=0190300) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 14:31:59] (step=0190400) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 14:33:00] (step=0190500) Train Loss: 0.1101, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 14:34:01] (step=0190600) Train Loss: 0.1124, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 14:35:02] (step=0190700) Train Loss: 0.1053, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 14:36:03] (step=0190800) Train Loss: 0.1010, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 14:37:05] (step=0190900) Train Loss: 0.1103, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 14:38:06] (step=0191000) Train Loss: 0.1117, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 14:39:07] (step=0191100) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 14:40:08] (step=0191200) Train Loss: 0.1038, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 14:41:09] (step=0191300) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 14:42:10] (step=0191400) Train Loss: 0.1072, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 14:43:11] (step=0191500) Train Loss: 0.1111, Train Steps/Sec: 1.63, Samples/Sec: 78.41
+[2025-06-06 14:44:12] (step=0191600) Train Loss: 0.1075, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 14:45:13] (step=0191700) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 14:46:14] (step=0191800) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 14:47:15] (step=0191900) Train Loss: 0.1041, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 14:48:16] (step=0192000) Train Loss: 0.1068, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 14:48:31] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 14:49:32] (step=0192100) Train Loss: 0.1033, Train Steps/Sec: 1.32, Samples/Sec: 63.20
+[2025-06-06 14:50:33] (step=0192200) Train Loss: 0.1103, Train Steps/Sec: 1.63, Samples/Sec: 78.42
+[2025-06-06 14:51:34] (step=0192300) Train Loss: 0.1132, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 14:52:35] (step=0192400) Train Loss: 0.1036, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 14:53:36] (step=0192500) Train Loss: 0.1113, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 14:54:37] (step=0192600) Train Loss: 0.1035, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 14:55:38] (step=0192700) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 14:56:40] (step=0192800) Train Loss: 0.1064, Train Steps/Sec: 1.63, Samples/Sec: 78.38
+[2025-06-06 14:57:41] (step=0192900) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.52
+[2025-06-06 14:58:42] (step=0193000) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 14:59:43] (step=0193100) Train Loss: 0.1069, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 15:00:44] (step=0193200) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 15:01:45] (step=0193300) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.64
+[2025-06-06 15:02:46] (step=0193400) Train Loss: 0.1044, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 15:03:47] (step=0193500) Train Loss: 0.1081, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-06 15:04:48] (step=0193600) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.53
+[2025-06-06 15:05:49] (step=0193700) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.63
+[2025-06-06 15:06:50] (step=0193800) Train Loss: 0.1013, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 15:07:51] (step=0193900) Train Loss: 0.1051, Train Steps/Sec: 1.64, Samples/Sec: 78.87
+[2025-06-06 15:08:52] (step=0194000) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 15:09:07] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 15:10:08] (step=0194100) Train Loss: 0.1070, Train Steps/Sec: 1.32, Samples/Sec: 63.39
+[2025-06-06 15:11:09] (step=0194200) Train Loss: 0.1027, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 15:12:10] (step=0194300) Train Loss: 0.1045, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 15:13:11] (step=0194400) Train Loss: 0.1055, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 15:14:12] (step=0194500) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 15:15:13] (step=0194600) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 15:16:14] (step=0194700) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 15:17:15] (step=0194800) Train Loss: 0.1053, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-06 15:18:16] (step=0194900) Train Loss: 0.1061, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 15:19:17] (step=0195000) Train Loss: 0.1038, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 15:20:18] (step=0195100) Train Loss: 0.1068, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 15:21:19] (step=0195200) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 15:22:20] (step=0195300) Train Loss: 0.1059, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 15:23:21] (step=0195400) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 15:24:22] (step=0195500) Train Loss: 0.1118, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 15:25:23] (step=0195600) Train Loss: 0.1076, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 15:26:24] (step=0195700) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 15:27:25] (step=0195800) Train Loss: 0.1050, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 15:28:26] (step=0195900) Train Loss: 0.1021, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 15:29:27] (step=0196000) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 15:29:41] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 15:30:42] (step=0196100) Train Loss: 0.0997, Train Steps/Sec: 1.34, Samples/Sec: 64.13
+[2025-06-06 15:31:43] (step=0196200) Train Loss: 0.1111, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 15:32:44] (step=0196300) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 15:33:45] (step=0196400) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 15:34:46] (step=0196500) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 15:35:47] (step=0196600) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 15:36:48] (step=0196700) Train Loss: 0.1020, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 15:37:49] (step=0196800) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 15:38:51] (step=0196900) Train Loss: 0.1063, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-06 15:39:52] (step=0197000) Train Loss: 0.1110, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 15:40:53] (step=0197100) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 15:41:54] (step=0197200) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 15:42:55] (step=0197300) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 15:43:56] (step=0197400) Train Loss: 0.1028, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 15:44:57] (step=0197500) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.56
+[2025-06-06 15:45:58] (step=0197600) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 15:46:59] (step=0197700) Train Loss: 0.1096, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 15:48:00] (step=0197800) Train Loss: 0.1045, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 15:49:01] (step=0197900) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 15:50:02] (step=0198000) Train Loss: 0.1133, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 15:50:16] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 15:51:17] (step=0198100) Train Loss: 0.1060, Train Steps/Sec: 1.34, Samples/Sec: 64.12
+[2025-06-06 15:52:18] (step=0198200) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 15:53:19] (step=0198300) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 15:54:20] (step=0198400) Train Loss: 0.1061, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 15:55:21] (step=0198500) Train Loss: 0.1071, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 15:56:22] (step=0198600) Train Loss: 0.1042, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 15:57:23] (step=0198700) Train Loss: 0.1074, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 15:58:24] (step=0198800) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 15:59:25] (step=0198900) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 16:00:26] (step=0199000) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.59
+[2025-06-06 16:01:27] (step=0199100) Train Loss: 0.1076, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 16:02:28] (step=0199200) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 16:03:29] (step=0199300) Train Loss: 0.1022, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:04:21] Beginning epoch 18...
+[2025-06-06 16:04:30] (step=0199400) Train Loss: 0.1162, Train Steps/Sec: 1.63, Samples/Sec: 78.09
+[2025-06-06 16:05:31] (step=0199500) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 16:06:33] (step=0199600) Train Loss: 0.1062, Train Steps/Sec: 1.63, Samples/Sec: 78.45
+[2025-06-06 16:07:34] (step=0199700) Train Loss: 0.1019, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 16:08:35] (step=0199800) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.72
+[2025-06-06 16:09:36] (step=0199900) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 16:10:37] (step=0200000) Train Loss: 0.1008, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 16:11:02] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0200000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 16:11:05] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 16:13:25] (step=0200000) Perceptual Loss: 0.1379, Eval Time: 142.56
+[2025-06-06 16:14:26] (step=0200100) Train Loss: 0.1081, Train Steps/Sec: 0.44, Samples/Sec: 20.92
+[2025-06-06 16:15:27] (step=0200200) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.61
+[2025-06-06 16:16:28] (step=0200300) Train Loss: 0.1072, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:17:29] (step=0200400) Train Loss: 0.1071, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:18:30] (step=0200500) Train Loss: 0.1072, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:19:31] (step=0200600) Train Loss: 0.1086, Train Steps/Sec: 1.64, Samples/Sec: 78.54
+[2025-06-06 16:20:32] (step=0200700) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 16:21:33] (step=0200800) Train Loss: 0.1091, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-06 16:22:35] (step=0200900) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 16:23:36] (step=0201000) Train Loss: 0.0982, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:24:36] (step=0201100) Train Loss: 0.1056, Train Steps/Sec: 1.65, Samples/Sec: 78.96
+[2025-06-06 16:25:37] (step=0201200) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 16:26:38] (step=0201300) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.51
+[2025-06-06 16:27:39] (step=0201400) Train Loss: 0.1065, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 16:28:40] (step=0201500) Train Loss: 0.1024, Train Steps/Sec: 1.63, Samples/Sec: 78.46
+[2025-06-06 16:29:41] (step=0201600) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 16:30:42] (step=0201700) Train Loss: 0.1067, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 16:31:44] (step=0201800) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 16:32:45] (step=0201900) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 16:33:46] (step=0202000) Train Loss: 0.1093, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 16:34:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 16:35:02] (step=0202100) Train Loss: 0.1021, Train Steps/Sec: 1.32, Samples/Sec: 63.13
+[2025-06-06 16:36:03] (step=0202200) Train Loss: 0.1047, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 16:37:04] (step=0202300) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 16:38:05] (step=0202400) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:39:06] (step=0202500) Train Loss: 0.1069, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 16:40:07] (step=0202600) Train Loss: 0.1058, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 16:41:08] (step=0202700) Train Loss: 0.1037, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:42:09] (step=0202800) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.58
+[2025-06-06 16:43:10] (step=0202900) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.57
+[2025-06-06 16:44:11] (step=0203000) Train Loss: 0.1047, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:45:12] (step=0203100) Train Loss: 0.1046, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:46:13] (step=0203200) Train Loss: 0.0993, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 16:47:14] (step=0203300) Train Loss: 0.1037, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 16:48:15] (step=0203400) Train Loss: 0.1028, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:49:16] (step=0203500) Train Loss: 0.1065, Train Steps/Sec: 1.63, Samples/Sec: 78.43
+[2025-06-06 16:50:17] (step=0203600) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 16:51:18] (step=0203700) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 16:52:19] (step=0203800) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 16:53:20] (step=0203900) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.60
+[2025-06-06 16:54:21] (step=0204000) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 16:54:35] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 16:55:36] (step=0204100) Train Loss: 0.1032, Train Steps/Sec: 1.34, Samples/Sec: 64.20
+[2025-06-06 16:56:37] (step=0204200) Train Loss: 0.1027, Train Steps/Sec: 1.64, Samples/Sec: 78.62
+[2025-06-06 16:57:38] (step=0204300) Train Loss: 0.1074, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 16:58:39] (step=0204400) Train Loss: 0.1069, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 16:59:40] (step=0204500) Train Loss: 0.1004, Train Steps/Sec: 1.64, Samples/Sec: 78.55
+[2025-06-06 17:00:41] (step=0204600) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 78.66
+[2025-06-06 17:01:42] (step=0204700) Train Loss: 0.1048, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 17:02:43] (step=0204800) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.71
+[2025-06-06 17:03:44] (step=0204900) Train Loss: 0.1030, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 17:04:45] (step=0205000) Train Loss: 0.1018, Train Steps/Sec: 1.64, Samples/Sec: 78.85
+[2025-06-06 17:05:46] (step=0205100) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.83
+[2025-06-06 17:06:47] (step=0205200) Train Loss: 0.1120, Train Steps/Sec: 1.64, Samples/Sec: 78.74
+[2025-06-06 17:07:48] (step=0205300) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.86
+[2025-06-06 17:08:49] (step=0205400) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:09:49] (step=0205500) Train Loss: 0.1084, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 17:10:50] (step=0205600) Train Loss: 0.1100, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 17:11:51] (step=0205700) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 17:12:52] (step=0205800) Train Loss: 0.1092, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 17:13:53] (step=0205900) Train Loss: 0.1037, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 17:14:54] (step=0206000) Train Loss: 0.1052, Train Steps/Sec: 1.64, Samples/Sec: 78.87
+[2025-06-06 17:15:07] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 17:16:08] (step=0206100) Train Loss: 0.1070, Train Steps/Sec: 1.35, Samples/Sec: 65.03
+[2025-06-06 17:17:09] (step=0206200) Train Loss: 0.1078, Train Steps/Sec: 1.64, Samples/Sec: 78.67
+[2025-06-06 17:18:09] (step=0206300) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:19:10] (step=0206400) Train Loss: 0.1030, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:20:11] (step=0206500) Train Loss: 0.1091, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 17:21:12] (step=0206600) Train Loss: 0.1028, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 17:22:13] (step=0206700) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 17:23:14] (step=0206800) Train Loss: 0.1053, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 17:24:15] (step=0206900) Train Loss: 0.1009, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 17:25:16] (step=0207000) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 17:26:16] (step=0207100) Train Loss: 0.1114, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 17:27:17] (step=0207200) Train Loss: 0.1112, Train Steps/Sec: 1.64, Samples/Sec: 78.83
+[2025-06-06 17:28:18] (step=0207300) Train Loss: 0.1085, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 17:29:19] (step=0207400) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:30:20] (step=0207500) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 17:31:21] (step=0207600) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 17:32:22] (step=0207700) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:33:23] (step=0207800) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 17:34:23] (step=0207900) Train Loss: 0.1069, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:35:24] (step=0208000) Train Loss: 0.1088, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 17:35:37] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 17:36:38] (step=0208100) Train Loss: 0.1051, Train Steps/Sec: 1.36, Samples/Sec: 65.15
+[2025-06-06 17:37:39] (step=0208200) Train Loss: 0.1087, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 17:38:40] (step=0208300) Train Loss: 0.1033, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 17:39:41] (step=0208400) Train Loss: 0.1050, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 17:40:42] (step=0208500) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:41:42] (step=0208600) Train Loss: 0.1019, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 17:42:43] (step=0208700) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 17:43:44] (step=0208800) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 17:44:45] (step=0208900) Train Loss: 0.1042, Train Steps/Sec: 1.64, Samples/Sec: 78.68
+[2025-06-06 17:45:46] (step=0209000) Train Loss: 0.1053, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:46:47] (step=0209100) Train Loss: 0.1074, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 17:47:48] (step=0209200) Train Loss: 0.1091, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 17:48:49] (step=0209300) Train Loss: 0.1019, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 17:49:49] (step=0209400) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 17:50:50] (step=0209500) Train Loss: 0.1048, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 17:51:51] (step=0209600) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 17:52:52] (step=0209700) Train Loss: 0.1063, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 17:53:53] (step=0209800) Train Loss: 0.1048, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 17:54:54] (step=0209900) Train Loss: 0.1018, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 17:55:55] (step=0210000) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 17:56:08] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 17:56:10] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 17:58:29] (step=0210000) Perceptual Loss: 0.1281, Eval Time: 141.74
+[2025-06-06 17:59:30] (step=0210100) Train Loss: 0.1047, Train Steps/Sec: 0.46, Samples/Sec: 22.26
+[2025-06-06 18:00:31] (step=0210200) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 18:01:32] (step=0210300) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.87
+[2025-06-06 18:02:33] (step=0210400) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.75
+[2025-06-06 18:03:11] Beginning epoch 19...
+[2025-06-06 18:03:34] (step=0210500) Train Loss: 0.1108, Train Steps/Sec: 1.64, Samples/Sec: 78.49
+[2025-06-06 18:04:35] (step=0210600) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.86
+[2025-06-06 18:05:36] (step=0210700) Train Loss: 0.1043, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 18:06:37] (step=0210800) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 18:07:38] (step=0210900) Train Loss: 0.1016, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 18:08:39] (step=0211000) Train Loss: 0.1052, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:09:40] (step=0211100) Train Loss: 0.1092, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-06 18:10:40] (step=0211200) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:11:41] (step=0211300) Train Loss: 0.1049, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 18:12:42] (step=0211400) Train Loss: 0.1074, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-06 18:13:43] (step=0211500) Train Loss: 0.1033, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 18:14:44] (step=0211600) Train Loss: 0.1073, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 18:15:45] (step=0211700) Train Loss: 0.1125, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:16:46] (step=0211800) Train Loss: 0.1050, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 18:17:46] (step=0211900) Train Loss: 0.1030, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 18:18:47] (step=0212000) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:19:00] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 18:20:01] (step=0212100) Train Loss: 0.1044, Train Steps/Sec: 1.36, Samples/Sec: 65.25
+[2025-06-06 18:21:02] (step=0212200) Train Loss: 0.1057, Train Steps/Sec: 1.64, Samples/Sec: 78.77
+[2025-06-06 18:22:03] (step=0212300) Train Loss: 0.1095, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 18:23:04] (step=0212400) Train Loss: 0.1002, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 18:24:04] (step=0212500) Train Loss: 0.1092, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 18:25:05] (step=0212600) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:26:06] (step=0212700) Train Loss: 0.1072, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:27:07] (step=0212800) Train Loss: 0.1147, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-06 18:28:08] (step=0212900) Train Loss: 0.1020, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 18:29:09] (step=0213000) Train Loss: 0.0993, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:30:10] (step=0213100) Train Loss: 0.1042, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 18:31:10] (step=0213200) Train Loss: 0.1046, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:32:11] (step=0213300) Train Loss: 0.1038, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 18:33:12] (step=0213400) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 18:34:13] (step=0213500) Train Loss: 0.1097, Train Steps/Sec: 1.64, Samples/Sec: 78.69
+[2025-06-06 18:35:14] (step=0213600) Train Loss: 0.1094, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:36:15] (step=0213700) Train Loss: 0.1040, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 18:37:16] (step=0213800) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.83
+[2025-06-06 18:38:17] (step=0213900) Train Loss: 0.1079, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:39:17] (step=0214000) Train Loss: 0.1017, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 18:39:30] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 18:40:31] (step=0214100) Train Loss: 0.1077, Train Steps/Sec: 1.36, Samples/Sec: 65.29
+[2025-06-06 18:41:32] (step=0214200) Train Loss: 0.1128, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 18:42:33] (step=0214300) Train Loss: 0.1071, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 18:43:34] (step=0214400) Train Loss: 0.1104, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 18:44:34] (step=0214500) Train Loss: 0.1038, Train Steps/Sec: 1.64, Samples/Sec: 78.92
+[2025-06-06 18:45:35] (step=0214600) Train Loss: 0.1058, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:46:36] (step=0214700) Train Loss: 0.1050, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 18:47:37] (step=0214800) Train Loss: 0.0995, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 18:48:38] (step=0214900) Train Loss: 0.1042, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 18:49:39] (step=0215000) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:50:40] (step=0215100) Train Loss: 0.1089, Train Steps/Sec: 1.64, Samples/Sec: 78.83
+[2025-06-06 18:51:41] (step=0215200) Train Loss: 0.1106, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:52:41] (step=0215300) Train Loss: 0.1058, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 18:53:42] (step=0215400) Train Loss: 0.1060, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 18:54:43] (step=0215500) Train Loss: 0.1069, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-06 18:55:44] (step=0215600) Train Loss: 0.1105, Train Steps/Sec: 1.64, Samples/Sec: 78.81
+[2025-06-06 18:56:45] (step=0215700) Train Loss: 0.0952, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 18:57:46] (step=0215800) Train Loss: 0.1033, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 18:58:47] (step=0215900) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 18:59:47] (step=0216000) Train Loss: 0.1058, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 19:00:00] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 19:01:01] (step=0216100) Train Loss: 0.1059, Train Steps/Sec: 1.36, Samples/Sec: 65.31
+[2025-06-06 19:02:02] (step=0216200) Train Loss: 0.1082, Train Steps/Sec: 1.64, Samples/Sec: 78.65
+[2025-06-06 19:03:03] (step=0216300) Train Loss: 0.1086, Train Steps/Sec: 1.64, Samples/Sec: 78.79
+[2025-06-06 19:04:04] (step=0216400) Train Loss: 0.1071, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 19:05:05] (step=0216500) Train Loss: 0.1041, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 19:06:05] (step=0216600) Train Loss: 0.0987, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 19:07:06] (step=0216700) Train Loss: 0.1018, Train Steps/Sec: 1.64, Samples/Sec: 78.88
+[2025-06-06 19:08:07] (step=0216800) Train Loss: 0.1005, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 19:09:08] (step=0216900) Train Loss: 0.1052, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 19:10:09] (step=0217000) Train Loss: 0.1064, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 19:11:10] (step=0217100) Train Loss: 0.1080, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 19:12:11] (step=0217200) Train Loss: 0.1121, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 19:13:12] (step=0217300) Train Loss: 0.1030, Train Steps/Sec: 1.64, Samples/Sec: 78.89
+[2025-06-06 19:14:12] (step=0217400) Train Loss: 0.1054, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 19:15:13] (step=0217500) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 19:16:14] (step=0217600) Train Loss: 0.1039, Train Steps/Sec: 1.64, Samples/Sec: 78.80
+[2025-06-06 19:17:15] (step=0217700) Train Loss: 0.1074, Train Steps/Sec: 1.64, Samples/Sec: 78.82
+[2025-06-06 19:18:16] (step=0217800) Train Loss: 0.1017, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 19:19:17] (step=0217900) Train Loss: 0.1056, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 19:20:18] (step=0218000) Train Loss: 0.1040, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 19:20:30] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 19:21:31] (step=0218100) Train Loss: 0.1050, Train Steps/Sec: 1.36, Samples/Sec: 65.38
+[2025-06-06 19:22:32] (step=0218200) Train Loss: 0.1024, Train Steps/Sec: 1.64, Samples/Sec: 78.70
+[2025-06-06 19:23:33] (step=0218300) Train Loss: 0.1099, Train Steps/Sec: 1.64, Samples/Sec: 78.78
+[2025-06-06 19:24:34] (step=0218400) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.90
+[2025-06-06 19:25:35] (step=0218500) Train Loss: 0.1083, Train Steps/Sec: 1.64, Samples/Sec: 78.91
+[2025-06-06 19:26:33] (step=0218600) Train Loss: 0.1073, Train Steps/Sec: 1.71, Samples/Sec: 82.26
+[2025-06-06 19:27:28] (step=0218700) Train Loss: 0.1076, Train Steps/Sec: 1.82, Samples/Sec: 87.16
+[2025-06-06 19:28:23] (step=0218800) Train Loss: 0.1031, Train Steps/Sec: 1.81, Samples/Sec: 87.08
+[2025-06-06 19:29:18] (step=0218900) Train Loss: 0.1097, Train Steps/Sec: 1.81, Samples/Sec: 86.93
+[2025-06-06 19:30:14] (step=0219000) Train Loss: 0.1021, Train Steps/Sec: 1.81, Samples/Sec: 87.09
+[2025-06-06 19:31:09] (step=0219100) Train Loss: 0.1059, Train Steps/Sec: 1.81, Samples/Sec: 87.09
+[2025-06-06 19:32:04] (step=0219200) Train Loss: 0.1017, Train Steps/Sec: 1.82, Samples/Sec: 87.18
+[2025-06-06 19:32:59] (step=0219300) Train Loss: 0.1054, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 19:33:54] (step=0219400) Train Loss: 0.1102, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 19:34:49] (step=0219500) Train Loss: 0.1029, Train Steps/Sec: 1.81, Samples/Sec: 87.11
+[2025-06-06 19:35:44] (step=0219600) Train Loss: 0.1062, Train Steps/Sec: 1.81, Samples/Sec: 86.97
+[2025-06-06 19:36:39] (step=0219700) Train Loss: 0.1092, Train Steps/Sec: 1.82, Samples/Sec: 87.13
+[2025-06-06 19:37:34] (step=0219800) Train Loss: 0.1158, Train Steps/Sec: 1.82, Samples/Sec: 87.16
+[2025-06-06 19:38:29] (step=0219900) Train Loss: 0.1056, Train Steps/Sec: 1.82, Samples/Sec: 87.18
+[2025-06-06 19:39:24] (step=0220000) Train Loss: 0.1087, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 19:39:48] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0220000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 19:39:51] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 19:42:10] (step=0220000) Perceptual Loss: 0.1277, Eval Time: 141.73
+[2025-06-06 19:43:05] (step=0220100) Train Loss: 0.1061, Train Steps/Sec: 0.45, Samples/Sec: 21.73
+[2025-06-06 19:44:00] (step=0220200) Train Loss: 0.1011, Train Steps/Sec: 1.81, Samples/Sec: 87.02
+[2025-06-06 19:44:55] (step=0220300) Train Loss: 0.1037, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 19:45:50] (step=0220400) Train Loss: 0.1028, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:46:46] (step=0220500) Train Loss: 0.1038, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:47:41] (step=0220600) Train Loss: 0.1103, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:48:36] (step=0220700) Train Loss: 0.1044, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:49:31] (step=0220800) Train Loss: 0.1068, Train Steps/Sec: 1.81, Samples/Sec: 86.98
+[2025-06-06 19:50:26] (step=0220900) Train Loss: 0.1089, Train Steps/Sec: 1.82, Samples/Sec: 87.12
+[2025-06-06 19:51:21] (step=0221000) Train Loss: 0.1077, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:52:16] (step=0221100) Train Loss: 0.1048, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:53:11] (step=0221200) Train Loss: 0.1060, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:54:06] (step=0221300) Train Loss: 0.1071, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:55:01] (step=0221400) Train Loss: 0.1050, Train Steps/Sec: 1.82, Samples/Sec: 87.14
+[2025-06-06 19:55:56] (step=0221500) Train Loss: 0.1032, Train Steps/Sec: 1.81, Samples/Sec: 86.99
+[2025-06-06 19:56:18] Beginning epoch 20...
+[2025-06-06 19:56:52] (step=0221600) Train Loss: 0.1046, Train Steps/Sec: 1.81, Samples/Sec: 86.75
+[2025-06-06 19:57:47] (step=0221700) Train Loss: 0.1080, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 19:58:42] (step=0221800) Train Loss: 0.1100, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 19:59:37] (step=0221900) Train Loss: 0.1054, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:00:32] (step=0222000) Train Loss: 0.1101, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:00:45] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 20:01:40] (step=0222100) Train Loss: 0.1029, Train Steps/Sec: 1.47, Samples/Sec: 70.58
+[2025-06-06 20:02:35] (step=0222200) Train Loss: 0.0989, Train Steps/Sec: 1.81, Samples/Sec: 86.94
+[2025-06-06 20:03:30] (step=0222300) Train Loss: 0.1087, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:04:25] (step=0222400) Train Loss: 0.1069, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 20:05:20] (step=0222500) Train Loss: 0.1098, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 20:06:15] (step=0222600) Train Loss: 0.1080, Train Steps/Sec: 1.82, Samples/Sec: 87.18
+[2025-06-06 20:07:10] (step=0222700) Train Loss: 0.1007, Train Steps/Sec: 1.82, Samples/Sec: 87.19
+[2025-06-06 20:08:05] (step=0222800) Train Loss: 0.1073, Train Steps/Sec: 1.81, Samples/Sec: 86.96
+[2025-06-06 20:09:00] (step=0222900) Train Loss: 0.1129, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 20:09:55] (step=0223000) Train Loss: 0.1128, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 20:10:50] (step=0223100) Train Loss: 0.1121, Train Steps/Sec: 1.82, Samples/Sec: 87.19
+[2025-06-06 20:11:46] (step=0223200) Train Loss: 0.1040, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 20:12:41] (step=0223300) Train Loss: 0.1038, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:13:36] (step=0223400) Train Loss: 0.1085, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:14:31] (step=0223500) Train Loss: 0.1051, Train Steps/Sec: 1.81, Samples/Sec: 86.82
+[2025-06-06 20:15:26] (step=0223600) Train Loss: 0.1014, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:16:21] (step=0223700) Train Loss: 0.1058, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:17:16] (step=0223800) Train Loss: 0.1049, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:18:11] (step=0223900) Train Loss: 0.1070, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:19:06] (step=0224000) Train Loss: 0.1090, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:19:19] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 20:20:14] (step=0224100) Train Loss: 0.1046, Train Steps/Sec: 1.47, Samples/Sec: 70.52
+[2025-06-06 20:21:09] (step=0224200) Train Loss: 0.1089, Train Steps/Sec: 1.81, Samples/Sec: 87.11
+[2025-06-06 20:22:04] (step=0224300) Train Loss: 0.1072, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:22:59] (step=0224400) Train Loss: 0.1047, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:23:54] (step=0224500) Train Loss: 0.1048, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:24:49] (step=0224600) Train Loss: 0.1075, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:25:44] (step=0224700) Train Loss: 0.1067, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:26:40] (step=0224800) Train Loss: 0.1079, Train Steps/Sec: 1.81, Samples/Sec: 87.07
+[2025-06-06 20:27:35] (step=0224900) Train Loss: 0.1063, Train Steps/Sec: 1.81, Samples/Sec: 87.08
+[2025-06-06 20:28:30] (step=0225000) Train Loss: 0.1069, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:29:25] (step=0225100) Train Loss: 0.1017, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:30:20] (step=0225200) Train Loss: 0.1026, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 20:31:15] (step=0225300) Train Loss: 0.1082, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:32:10] (step=0225400) Train Loss: 0.1035, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:33:05] (step=0225500) Train Loss: 0.1066, Train Steps/Sec: 1.81, Samples/Sec: 86.96
+[2025-06-06 20:34:00] (step=0225600) Train Loss: 0.1100, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:34:55] (step=0225700) Train Loss: 0.1077, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:35:50] (step=0225800) Train Loss: 0.1065, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:36:45] (step=0225900) Train Loss: 0.1056, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:37:40] (step=0226000) Train Loss: 0.1069, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:37:53] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 20:38:48] (step=0226100) Train Loss: 0.1046, Train Steps/Sec: 1.48, Samples/Sec: 70.90
+[2025-06-06 20:39:43] (step=0226200) Train Loss: 0.1145, Train Steps/Sec: 1.81, Samples/Sec: 86.97
+[2025-06-06 20:40:38] (step=0226300) Train Loss: 0.1067, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 20:41:33] (step=0226400) Train Loss: 0.1073, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:42:28] (step=0226500) Train Loss: 0.1057, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:43:23] (step=0226600) Train Loss: 0.1000, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:44:18] (step=0226700) Train Loss: 0.1067, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:45:13] (step=0226800) Train Loss: 0.1086, Train Steps/Sec: 1.81, Samples/Sec: 87.02
+[2025-06-06 20:46:09] (step=0226900) Train Loss: 0.1042, Train Steps/Sec: 1.81, Samples/Sec: 87.11
+[2025-06-06 20:47:04] (step=0227000) Train Loss: 0.1055, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:47:59] (step=0227100) Train Loss: 0.1047, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 20:48:54] (step=0227200) Train Loss: 0.1050, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:49:49] (step=0227300) Train Loss: 0.1083, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:50:44] (step=0227400) Train Loss: 0.1084, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:51:39] (step=0227500) Train Loss: 0.1049, Train Steps/Sec: 1.81, Samples/Sec: 86.95
+[2025-06-06 20:52:34] (step=0227600) Train Loss: 0.1046, Train Steps/Sec: 1.81, Samples/Sec: 87.10
+[2025-06-06 20:53:29] (step=0227700) Train Loss: 0.1024, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:54:24] (step=0227800) Train Loss: 0.1062, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 20:55:19] (step=0227900) Train Loss: 0.1050, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 20:56:14] (step=0228000) Train Loss: 0.1051, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 20:56:27] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 20:57:22] (step=0228100) Train Loss: 0.1025, Train Steps/Sec: 1.48, Samples/Sec: 70.82
+[2025-06-06 20:58:17] (step=0228200) Train Loss: 0.1034, Train Steps/Sec: 1.81, Samples/Sec: 86.99
+[2025-06-06 20:59:12] (step=0228300) Train Loss: 0.1025, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 21:00:07] (step=0228400) Train Loss: 0.1109, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 21:01:02] (step=0228500) Train Loss: 0.1056, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 21:01:57] (step=0228600) Train Loss: 0.1005, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:02:52] (step=0228700) Train Loss: 0.1099, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:03:47] (step=0228800) Train Loss: 0.1052, Train Steps/Sec: 1.81, Samples/Sec: 87.10
+[2025-06-06 21:04:43] (step=0228900) Train Loss: 0.1009, Train Steps/Sec: 1.81, Samples/Sec: 86.98
+[2025-06-06 21:05:38] (step=0229000) Train Loss: 0.1047, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:06:33] (step=0229100) Train Loss: 0.1045, Train Steps/Sec: 1.82, Samples/Sec: 87.25
+[2025-06-06 21:07:28] (step=0229200) Train Loss: 0.1079, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:08:23] (step=0229300) Train Loss: 0.1024, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:09:18] (step=0229400) Train Loss: 0.1065, Train Steps/Sec: 1.82, Samples/Sec: 87.12
+[2025-06-06 21:10:13] (step=0229500) Train Loss: 0.1052, Train Steps/Sec: 1.82, Samples/Sec: 87.13
+[2025-06-06 21:11:08] (step=0229600) Train Loss: 0.1049, Train Steps/Sec: 1.81, Samples/Sec: 87.12
+[2025-06-06 21:12:03] (step=0229700) Train Loss: 0.1088, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:12:58] (step=0229800) Train Loss: 0.1081, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:13:53] (step=0229900) Train Loss: 0.1067, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:14:48] (step=0230000) Train Loss: 0.1000, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 21:15:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 21:15:04] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 21:17:23] (step=0230000) Perceptual Loss: 0.1342, Eval Time: 141.93
+[2025-06-06 21:18:18] (step=0230100) Train Loss: 0.1127, Train Steps/Sec: 0.48, Samples/Sec: 22.86
+[2025-06-06 21:19:13] (step=0230200) Train Loss: 0.1109, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:20:08] (step=0230300) Train Loss: 0.1015, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:21:03] (step=0230400) Train Loss: 0.1099, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:21:58] (step=0230500) Train Loss: 0.1053, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:22:53] (step=0230600) Train Loss: 0.1057, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 21:23:48] (step=0230700) Train Loss: 0.1122, Train Steps/Sec: 1.81, Samples/Sec: 87.09
+[2025-06-06 21:24:44] (step=0230800) Train Loss: 0.1063, Train Steps/Sec: 1.81, Samples/Sec: 86.98
+[2025-06-06 21:25:39] (step=0230900) Train Loss: 0.1094, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 21:26:34] (step=0231000) Train Loss: 0.1096, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 21:27:29] (step=0231100) Train Loss: 0.1025, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 21:28:24] (step=0231200) Train Loss: 0.1048, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:29:19] (step=0231300) Train Loss: 0.1078, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:30:14] (step=0231400) Train Loss: 0.1037, Train Steps/Sec: 1.81, Samples/Sec: 87.03
+[2025-06-06 21:31:09] (step=0231500) Train Loss: 0.1036, Train Steps/Sec: 1.82, Samples/Sec: 87.12
+[2025-06-06 21:32:04] (step=0231600) Train Loss: 0.1018, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:32:59] (step=0231700) Train Loss: 0.1068, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:33:54] (step=0231800) Train Loss: 0.1083, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 21:34:49] (step=0231900) Train Loss: 0.1070, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:35:44] (step=0232000) Train Loss: 0.1082, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:35:57] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 21:36:52] (step=0232100) Train Loss: 0.1075, Train Steps/Sec: 1.47, Samples/Sec: 70.77
+[2025-06-06 21:37:47] (step=0232200) Train Loss: 0.1040, Train Steps/Sec: 1.81, Samples/Sec: 87.07
+[2025-06-06 21:38:42] (step=0232300) Train Loss: 0.1082, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 21:39:37] (step=0232400) Train Loss: 0.1087, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:40:32] (step=0232500) Train Loss: 0.1028, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 21:41:27] (step=0232600) Train Loss: 0.1073, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:41:37] Beginning epoch 21...
+[2025-06-06 21:42:23] (step=0232700) Train Loss: 0.1084, Train Steps/Sec: 1.81, Samples/Sec: 86.80
+[2025-06-06 21:43:18] (step=0232800) Train Loss: 0.1001, Train Steps/Sec: 1.81, Samples/Sec: 86.88
+[2025-06-06 21:44:13] (step=0232900) Train Loss: 0.1027, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:45:08] (step=0233000) Train Loss: 0.1100, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:46:03] (step=0233100) Train Loss: 0.1090, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:46:58] (step=0233200) Train Loss: 0.1045, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 21:47:53] (step=0233300) Train Loss: 0.1055, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:48:48] (step=0233400) Train Loss: 0.1088, Train Steps/Sec: 1.82, Samples/Sec: 87.12
+[2025-06-06 21:49:43] (step=0233500) Train Loss: 0.1058, Train Steps/Sec: 1.81, Samples/Sec: 86.95
+[2025-06-06 21:50:38] (step=0233600) Train Loss: 0.0998, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:51:33] (step=0233700) Train Loss: 0.1125, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 21:52:28] (step=0233800) Train Loss: 0.1096, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:53:23] (step=0233900) Train Loss: 0.1063, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:54:18] (step=0234000) Train Loss: 0.1072, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 21:54:31] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 21:55:26] (step=0234100) Train Loss: 0.1042, Train Steps/Sec: 1.47, Samples/Sec: 70.77
+[2025-06-06 21:56:21] (step=0234200) Train Loss: 0.1062, Train Steps/Sec: 1.81, Samples/Sec: 87.10
+[2025-06-06 21:57:16] (step=0234300) Train Loss: 0.1082, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 21:58:12] (step=0234400) Train Loss: 0.1093, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 21:59:07] (step=0234500) Train Loss: 0.1053, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 22:00:02] (step=0234600) Train Loss: 0.1109, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 22:00:57] (step=0234700) Train Loss: 0.1032, Train Steps/Sec: 1.82, Samples/Sec: 87.15
+[2025-06-06 22:01:52] (step=0234800) Train Loss: 0.1091, Train Steps/Sec: 1.81, Samples/Sec: 87.11
+[2025-06-06 22:02:47] (step=0234900) Train Loss: 0.1029, Train Steps/Sec: 1.81, Samples/Sec: 87.11
+[2025-06-06 22:03:42] (step=0235000) Train Loss: 0.1046, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:04:37] (step=0235100) Train Loss: 0.1035, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 22:05:32] (step=0235200) Train Loss: 0.1077, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:06:27] (step=0235300) Train Loss: 0.1073, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 22:07:22] (step=0235400) Train Loss: 0.1062, Train Steps/Sec: 1.81, Samples/Sec: 87.10
+[2025-06-06 22:08:17] (step=0235500) Train Loss: 0.1047, Train Steps/Sec: 1.81, Samples/Sec: 87.01
+[2025-06-06 22:09:12] (step=0235600) Train Loss: 0.1023, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 22:10:07] (step=0235700) Train Loss: 0.1077, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 22:11:02] (step=0235800) Train Loss: 0.1042, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 22:11:57] (step=0235900) Train Loss: 0.1029, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:12:52] (step=0236000) Train Loss: 0.1091, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 22:13:06] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 22:14:01] (step=0236100) Train Loss: 0.1080, Train Steps/Sec: 1.47, Samples/Sec: 70.41
+[2025-06-06 22:14:56] (step=0236200) Train Loss: 0.1034, Train Steps/Sec: 1.81, Samples/Sec: 86.94
+[2025-06-06 22:15:51] (step=0236300) Train Loss: 0.1060, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 22:16:46] (step=0236400) Train Loss: 0.1084, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:17:41] (step=0236500) Train Loss: 0.1127, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:18:36] (step=0236600) Train Loss: 0.1022, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:19:31] (step=0236700) Train Loss: 0.1038, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 22:20:26] (step=0236800) Train Loss: 0.1038, Train Steps/Sec: 1.81, Samples/Sec: 86.99
+[2025-06-06 22:21:21] (step=0236900) Train Loss: 0.1084, Train Steps/Sec: 1.81, Samples/Sec: 87.11
+[2025-06-06 22:22:16] (step=0237000) Train Loss: 0.1079, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 22:23:11] (step=0237100) Train Loss: 0.1113, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 22:24:06] (step=0237200) Train Loss: 0.1076, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:25:01] (step=0237300) Train Loss: 0.1082, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:25:56] (step=0237400) Train Loss: 0.1040, Train Steps/Sec: 1.82, Samples/Sec: 87.13
+[2025-06-06 22:26:52] (step=0237500) Train Loss: 0.1056, Train Steps/Sec: 1.81, Samples/Sec: 87.11
+[2025-06-06 22:27:47] (step=0237600) Train Loss: 0.1134, Train Steps/Sec: 1.81, Samples/Sec: 87.08
+[2025-06-06 22:28:42] (step=0237700) Train Loss: 0.1033, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:29:37] (step=0237800) Train Loss: 0.1047, Train Steps/Sec: 1.82, Samples/Sec: 87.22
+[2025-06-06 22:30:32] (step=0237900) Train Loss: 0.1044, Train Steps/Sec: 1.82, Samples/Sec: 87.24
+[2025-06-06 22:31:27] (step=0238000) Train Loss: 0.1040, Train Steps/Sec: 1.82, Samples/Sec: 87.23
+[2025-06-06 22:31:40] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-06 22:32:35] (step=0238100) Train Loss: 0.1140, Train Steps/Sec: 1.47, Samples/Sec: 70.73
+[2025-06-06 22:33:30] (step=0238200) Train Loss: 0.1103, Train Steps/Sec: 1.81, Samples/Sec: 86.99
+[2025-06-06 22:34:25] (step=0238300) Train Loss: 0.1082, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 22:35:20] (step=0238400) Train Loss: 0.1028, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 22:36:15] (step=0238500) Train Loss: 0.1098, Train Steps/Sec: 1.82, Samples/Sec: 87.20
+[2025-06-06 22:37:10] (step=0238600) Train Loss: 0.1038, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 22:38:05] (step=0238700) Train Loss: 0.1065, Train Steps/Sec: 1.81, Samples/Sec: 87.11
+[2025-06-06 22:39:00] (step=0238800) Train Loss: 0.1093, Train Steps/Sec: 1.82, Samples/Sec: 87.15
+[2025-06-06 22:39:55] (step=0238900) Train Loss: 0.1083, Train Steps/Sec: 1.81, Samples/Sec: 86.90
+[2025-06-06 22:40:51] (step=0239000) Train Loss: 0.1073, Train Steps/Sec: 1.81, Samples/Sec: 87.00
+[2025-06-06 22:41:46] (step=0239100) Train Loss: 0.1041, Train Steps/Sec: 1.82, Samples/Sec: 87.18
+[2025-06-06 22:42:41] (step=0239200) Train Loss: 0.1091, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 22:43:36] (step=0239300) Train Loss: 0.1084, Train Steps/Sec: 1.82, Samples/Sec: 87.21
+[2025-06-06 22:44:31] (step=0239400) Train Loss: 0.1029, Train Steps/Sec: 1.81, Samples/Sec: 87.08
+[2025-06-06 22:45:26] (step=0239500) Train Loss: 0.1096, Train Steps/Sec: 1.81, Samples/Sec: 87.08
+[2025-06-06 22:46:21] (step=0239600) Train Loss: 0.1077, Train Steps/Sec: 1.81, Samples/Sec: 86.99
+[2025-06-06 22:47:16] (step=0239700) Train Loss: 0.1030, Train Steps/Sec: 1.82, Samples/Sec: 87.14
+[2025-06-06 22:48:11] (step=0239800) Train Loss: 0.1090, Train Steps/Sec: 1.82, Samples/Sec: 87.16
+[2025-06-06 22:49:06] (step=0239900) Train Loss: 0.1041, Train Steps/Sec: 1.82, Samples/Sec: 87.16
+[2025-06-06 22:50:02] (step=0240000) Train Loss: 0.1095, Train Steps/Sec: 1.81, Samples/Sec: 87.08
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 22:50:25] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0240000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-06 22:50:28] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-06 22:52:47] (step=0240000) Perceptual Loss: 0.1556, Eval Time: 141.76
+[2025-06-06 22:53:42] (step=0240100) Train Loss: 0.1064, Train Steps/Sec: 0.45, Samples/Sec: 21.72
+[2025-06-06 22:54:38] (step=0240200) Train Loss: 0.1100, Train Steps/Sec: 1.82, Samples/Sec: 87.18
+[2025-06-06 22:55:33] (step=0240300) Train Loss: 0.1080, Train Steps/Sec: 1.82, Samples/Sec: 87.18
+[2025-06-06 22:56:11,723] torch.distributed.elastic.agent.server.api: [WARNING] Received Signals.SIGINT death signal, shutting down workers
+[2025-06-06 22:56:11,726] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1690786 closing signal SIGINT
+[2025-06-06 22:56:11,726] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1690787 closing signal SIGINT
+[2025-06-06 22:56:11,726] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 1690788 closing signal SIGINT
+Traceback (most recent call last):
+  File "train_recon.py", line 446, in <module>
+    main(args)
+  File "train_recon.py", line 299, in main
+    loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+  File "/data1/tpz/nwm-main/diffusion/respace.py", line 103, in training_losses
+    return super().training_losses(self._wrap_model(model), *args, **kwargs)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 737, in training_losses
+    x_t = self.q_sample(x_start, t, noise=noise)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 234, in q_sample
+    _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 876, in _extract_into_tensor
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+KeyboardInterrupt
+Traceback (most recent call last):
+  File "train_recon.py", line 446, in <module>
+    main(args)
+  File "train_recon.py", line 299, in main
+    loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+  File "/data1/tpz/nwm-main/diffusion/respace.py", line 103, in training_losses
+    return super().training_losses(self._wrap_model(model), *args, **kwargs)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 737, in training_losses
+    x_t = self.q_sample(x_start, t, noise=noise)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 234, in q_sample
+    _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 876, in _extract_into_tensor
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+KeyboardInterrupt
+Traceback (most recent call last):
+  File "train_recon.py", line 446, in <module>
+    main(args)
+  File "train_recon.py", line 299, in main
+    loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+  File "/data1/tpz/nwm-main/diffusion/respace.py", line 103, in training_losses
+    return super().training_losses(self._wrap_model(model), *args, **kwargs)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 737, in training_losses
+    x_t = self.q_sample(x_start, t, noise=noise)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 234, in q_sample
+    _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 876, in _extract_into_tensor
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+KeyboardInterrupt
+Traceback (most recent call last):
+  File "/data1/tpz/anaconda3/envs/nwm2/bin/torchrun", line 8, in <module>
+    sys.exit(main())
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
+    return f(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/run.py", line 806, in main
+    run(args)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/run.py", line 797, in run
+    elastic_launch(
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 255, in launch_agent
+    result = agent.run()
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 124, in wrapper
+    result = f(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 736, in run
+    result = self._invoke_run(role)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 877, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 62, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 1690717 got signal: 2
diff --git a/recon_v2.log b/recon_v2.log
new file mode 100644
index 0000000000000000000000000000000000000000..63fd95cb2b407737a87aca33bc254f861dbaf6c4
--- /dev/null
+++ b/recon_v2.log
@@ -0,0 +1,4600 @@
+[2025-06-07 16:03:38,342] torch.distributed.run: [WARNING] 
+[2025-06-07 16:03:38,342] torch.distributed.run: [WARNING] *****************************************
+[2025-06-07 16:03:38,342] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+[2025-06-07 16:03:38,342] torch.distributed.run: [WARNING] *****************************************
+[2025-06-07 16:03:41] Experiment directory created at logs/nwm_cdit_recon
+[2025-06-07 16:03:53] CDiT Parameters: 1,019,927,840
+[2025-06-07 16:03:54] Dataset contains 132,929 images
+[2025-06-07 16:03:54] Training for 300 epochs...
+[2025-06-07 16:03:54] Beginning epoch 0...
+[2025-06-07 16:04:47] (step=0000100) Train Loss: 0.3141, Train Steps/Sec: 1.89, Samples/Sec: 67.91
+[2025-06-07 16:05:39] (step=0000200) Train Loss: 0.1959, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-07 16:06:30] (step=0000300) Train Loss: 0.2008, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 16:07:22] (step=0000400) Train Loss: 0.1794, Train Steps/Sec: 1.94, Samples/Sec: 70.00
+[2025-06-07 16:08:13] (step=0000500) Train Loss: 0.1841, Train Steps/Sec: 1.94, Samples/Sec: 70.01
+[2025-06-07 16:09:05] (step=0000600) Train Loss: 0.1820, Train Steps/Sec: 1.94, Samples/Sec: 69.89
+[2025-06-07 16:09:56] (step=0000700) Train Loss: 0.1835, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 16:10:48] (step=0000800) Train Loss: 0.1774, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-07 16:11:40] (step=0000900) Train Loss: 0.1799, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 16:12:31] (step=0001000) Train Loss: 0.1807, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 16:13:23] (step=0001100) Train Loss: 0.1855, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 16:14:15] (step=0001200) Train Loss: 0.1699, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 16:15:06] (step=0001300) Train Loss: 0.1684, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 16:15:58] (step=0001400) Train Loss: 0.1646, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 16:16:50] (step=0001500) Train Loss: 0.1674, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-07 16:17:42] (step=0001600) Train Loss: 0.1690, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 16:18:33] (step=0001700) Train Loss: 0.1736, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 16:19:25] (step=0001800) Train Loss: 0.1767, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 16:20:17] (step=0001900) Train Loss: 0.1725, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 16:21:08] (step=0002000) Train Loss: 0.1620, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 16:21:24] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 16:22:16] (step=0002100) Train Loss: 0.1654, Train Steps/Sec: 1.47, Samples/Sec: 53.04
+[2025-06-07 16:23:08] (step=0002200) Train Loss: 0.1717, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-07 16:23:59] (step=0002300) Train Loss: 0.1814, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 16:24:51] (step=0002400) Train Loss: 0.1613, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 16:25:43] (step=0002500) Train Loss: 0.1588, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 16:26:34] (step=0002600) Train Loss: 0.1659, Train Steps/Sec: 1.94, Samples/Sec: 69.79
+[2025-06-07 16:27:26] (step=0002700) Train Loss: 0.1677, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 16:28:18] (step=0002800) Train Loss: 0.1732, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-07 16:29:09] (step=0002900) Train Loss: 0.1625, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 16:30:01] (step=0003000) Train Loss: 0.1593, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 16:30:53] (step=0003100) Train Loss: 0.1669, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 16:31:44] (step=0003200) Train Loss: 0.1561, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 16:32:36] (step=0003300) Train Loss: 0.1608, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 16:33:28] (step=0003400) Train Loss: 0.1582, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-07 16:34:20] (step=0003500) Train Loss: 0.1620, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 16:35:11] (step=0003600) Train Loss: 0.1617, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 16:36:03] (step=0003700) Train Loss: 0.1545, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 16:36:54] (step=0003800) Train Loss: 0.1584, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 16:37:46] (step=0003900) Train Loss: 0.1631, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 16:38:38] (step=0004000) Train Loss: 0.1567, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-07 16:38:55] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 16:39:47] (step=0004100) Train Loss: 0.1515, Train Steps/Sec: 1.45, Samples/Sec: 52.36
+[2025-06-07 16:40:38] (step=0004200) Train Loss: 0.1627, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 16:41:30] (step=0004300) Train Loss: 0.1745, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 16:42:21] (step=0004400) Train Loss: 0.1588, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 16:43:13] (step=0004500) Train Loss: 0.1558, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 16:44:05] (step=0004600) Train Loss: 0.1588, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 16:44:56] (step=0004700) Train Loss: 0.1494, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 16:45:48] (step=0004800) Train Loss: 0.1514, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-07 16:46:40] (step=0004900) Train Loss: 0.1518, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 16:47:31] (step=0005000) Train Loss: 0.1516, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 16:48:23] (step=0005100) Train Loss: 0.1559, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 16:49:15] (step=0005200) Train Loss: 0.1644, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 16:50:06] (step=0005300) Train Loss: 0.1496, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-07 16:50:58] (step=0005400) Train Loss: 0.1595, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-07 16:51:50] (step=0005500) Train Loss: 0.1495, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 16:52:42] (step=0005600) Train Loss: 0.1533, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-07 16:53:33] (step=0005700) Train Loss: 0.1531, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-07 16:54:25] (step=0005800) Train Loss: 0.1555, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-07 16:55:17] (step=0005900) Train Loss: 0.1605, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 16:56:08] (step=0006000) Train Loss: 0.1533, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 16:56:25] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 16:57:17] (step=0006100) Train Loss: 0.1476, Train Steps/Sec: 1.46, Samples/Sec: 52.54
+[2025-06-07 16:58:08] (step=0006200) Train Loss: 0.1476, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-07 16:59:00] (step=0006300) Train Loss: 0.1545, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 16:59:52] (step=0006400) Train Loss: 0.1535, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-07 17:00:43] (step=0006500) Train Loss: 0.1514, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 17:01:35] (step=0006600) Train Loss: 0.1570, Train Steps/Sec: 1.94, Samples/Sec: 69.78
+[2025-06-07 17:02:27] (step=0006700) Train Loss: 0.1466, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-07 17:03:18] (step=0006800) Train Loss: 0.1453, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 17:04:10] (step=0006900) Train Loss: 0.1458, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 17:05:02] (step=0007000) Train Loss: 0.1411, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 17:05:53] (step=0007100) Train Loss: 0.1494, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-07 17:06:45] (step=0007200) Train Loss: 0.1542, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 17:07:37] (step=0007300) Train Loss: 0.1484, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 17:08:29] (step=0007400) Train Loss: 0.1500, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-07 17:09:20] (step=0007500) Train Loss: 0.1412, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-07 17:10:12] (step=0007600) Train Loss: 0.1491, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-07 17:11:03] (step=0007700) Train Loss: 0.1543, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-07 17:11:55] (step=0007800) Train Loss: 0.1537, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-07 17:12:47] (step=0007900) Train Loss: 0.1476, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 17:13:38] (step=0008000) Train Loss: 0.1402, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-07 17:13:55] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 17:14:46] (step=0008100) Train Loss: 0.1485, Train Steps/Sec: 1.47, Samples/Sec: 53.01
+[2025-06-07 17:15:38] (step=0008200) Train Loss: 0.1458, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-07 17:16:30] (step=0008300) Train Loss: 0.1510, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 17:17:21] (step=0008400) Train Loss: 0.1495, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 17:18:13] (step=0008500) Train Loss: 0.1484, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 17:19:04] (step=0008600) Train Loss: 0.1481, Train Steps/Sec: 1.94, Samples/Sec: 69.81
+[2025-06-07 17:19:56] (step=0008700) Train Loss: 0.1465, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-07 17:20:48] (step=0008800) Train Loss: 0.1463, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 17:21:40] (step=0008900) Train Loss: 0.1477, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 17:22:31] (step=0009000) Train Loss: 0.1560, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-07 17:23:23] (step=0009100) Train Loss: 0.1492, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 17:24:15] (step=0009200) Train Loss: 0.1450, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 17:25:06] (step=0009300) Train Loss: 0.1439, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 17:25:58] (step=0009400) Train Loss: 0.1497, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 17:26:50] (step=0009500) Train Loss: 0.1480, Train Steps/Sec: 1.93, Samples/Sec: 69.32
+[2025-06-07 17:27:42] (step=0009600) Train Loss: 0.1402, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 17:28:33] (step=0009700) Train Loss: 0.1417, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-07 17:29:25] (step=0009800) Train Loss: 0.1440, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 17:30:17] (step=0009900) Train Loss: 0.1406, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 17:31:09] (step=0010000) Train Loss: 0.1476, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+[2025-06-07 17:31:24] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+[2025-06-07 17:31:28] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-07 17:33:33] (step=0010000) Perceptual Loss: 0.8214, Eval Time: 128.09
+[2025-06-07 17:34:24] (step=0010100) Train Loss: 0.1479, Train Steps/Sec: 0.51, Samples/Sec: 18.40
+[2025-06-07 17:35:16] (step=0010200) Train Loss: 0.1451, Train Steps/Sec: 1.94, Samples/Sec: 69.80
+[2025-06-07 17:36:08] (step=0010300) Train Loss: 0.1459, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 17:36:59] (step=0010400) Train Loss: 0.1485, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-07 17:37:51] (step=0010500) Train Loss: 0.1475, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 17:38:42] (step=0010600) Train Loss: 0.1483, Train Steps/Sec: 1.94, Samples/Sec: 70.00
+[2025-06-07 17:39:34] (step=0010700) Train Loss: 0.1447, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 17:40:26] (step=0010800) Train Loss: 0.1444, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-07 17:41:17] (step=0010900) Train Loss: 0.1451, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-07 17:42:09] (step=0011000) Train Loss: 0.1429, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-07 17:43:01] (step=0011100) Train Loss: 0.1377, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-07 17:43:53] (step=0011200) Train Loss: 0.1435, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-07 17:44:45] (step=0011300) Train Loss: 0.1405, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-07 17:45:36] (step=0011400) Train Loss: 0.1474, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 17:46:28] (step=0011500) Train Loss: 0.1409, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 17:47:20] (step=0011600) Train Loss: 0.1367, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-07 17:48:12] (step=0011700) Train Loss: 0.1384, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 17:49:03] (step=0011800) Train Loss: 0.1479, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-07 17:49:55] (step=0011900) Train Loss: 0.1390, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 17:50:47] (step=0012000) Train Loss: 0.1451, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 17:51:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 17:51:53] (step=0012100) Train Loss: 0.1432, Train Steps/Sec: 1.52, Samples/Sec: 54.77
+[2025-06-07 17:52:44] (step=0012200) Train Loss: 0.1408, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-07 17:53:36] (step=0012300) Train Loss: 0.1455, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 17:54:28] (step=0012400) Train Loss: 0.1443, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 17:55:19] (step=0012500) Train Loss: 0.1454, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-07 17:56:11] (step=0012600) Train Loss: 0.1400, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 17:57:03] (step=0012700) Train Loss: 0.1374, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 17:57:54] (step=0012800) Train Loss: 0.1441, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 17:58:46] (step=0012900) Train Loss: 0.1362, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 17:59:38] (step=0013000) Train Loss: 0.1464, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:00:29] (step=0013100) Train Loss: 0.1360, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 18:01:21] (step=0013200) Train Loss: 0.1488, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-07 18:02:13] (step=0013300) Train Loss: 0.1446, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:03:04] (step=0013400) Train Loss: 0.1445, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 18:03:56] (step=0013500) Train Loss: 0.1488, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 18:04:48] (step=0013600) Train Loss: 0.1388, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 18:05:40] (step=0013700) Train Loss: 0.1414, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 18:06:31] (step=0013800) Train Loss: 0.1446, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 18:07:23] (step=0013900) Train Loss: 0.1439, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 18:08:15] (step=0014000) Train Loss: 0.1437, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 18:08:28] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 18:09:19] (step=0014100) Train Loss: 0.1393, Train Steps/Sec: 1.54, Samples/Sec: 55.62
+[2025-06-07 18:10:11] (step=0014200) Train Loss: 0.1380, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 18:11:03] (step=0014300) Train Loss: 0.1455, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 18:11:54] (step=0014400) Train Loss: 0.1415, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 18:12:46] (step=0014500) Train Loss: 0.1395, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-07 18:13:38] (step=0014600) Train Loss: 0.1485, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:14:29] (step=0014700) Train Loss: 0.1401, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:15:06] Beginning epoch 1...
+[2025-06-07 18:15:21] (step=0014800) Train Loss: 0.1402, Train Steps/Sec: 1.92, Samples/Sec: 69.28
+[2025-06-07 18:16:13] (step=0014900) Train Loss: 0.1389, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:17:05] (step=0015000) Train Loss: 0.1451, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 18:17:56] (step=0015100) Train Loss: 0.1465, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 18:18:48] (step=0015200) Train Loss: 0.1379, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-07 18:19:40] (step=0015300) Train Loss: 0.1430, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:20:32] (step=0015400) Train Loss: 0.1408, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 18:21:23] (step=0015500) Train Loss: 0.1320, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-07 18:22:15] (step=0015600) Train Loss: 0.1316, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 18:23:07] (step=0015700) Train Loss: 0.1490, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 18:23:58] (step=0015800) Train Loss: 0.1374, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:24:50] (step=0015900) Train Loss: 0.1342, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-07 18:25:42] (step=0016000) Train Loss: 0.1430, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 18:25:55] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 18:26:47] (step=0016100) Train Loss: 0.1469, Train Steps/Sec: 1.54, Samples/Sec: 55.41
+[2025-06-07 18:27:38] (step=0016200) Train Loss: 0.1346, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:28:30] (step=0016300) Train Loss: 0.1425, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:29:22] (step=0016400) Train Loss: 0.1367, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 18:30:13] (step=0016500) Train Loss: 0.1385, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-07 18:31:05] (step=0016600) Train Loss: 0.1373, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 18:31:57] (step=0016700) Train Loss: 0.1426, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:32:49] (step=0016800) Train Loss: 0.1335, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 18:33:40] (step=0016900) Train Loss: 0.1320, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 18:34:32] (step=0017000) Train Loss: 0.1408, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 18:35:24] (step=0017100) Train Loss: 0.1371, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:36:15] (step=0017200) Train Loss: 0.1354, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-07 18:37:07] (step=0017300) Train Loss: 0.1363, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:37:59] (step=0017400) Train Loss: 0.1406, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 18:38:50] (step=0017500) Train Loss: 0.1286, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 18:39:42] (step=0017600) Train Loss: 0.1359, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:40:34] (step=0017700) Train Loss: 0.1336, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 18:41:25] (step=0017800) Train Loss: 0.1402, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 18:42:17] (step=0017900) Train Loss: 0.1336, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-07 18:43:09] (step=0018000) Train Loss: 0.1337, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:43:23] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 18:44:14] (step=0018100) Train Loss: 0.1292, Train Steps/Sec: 1.52, Samples/Sec: 54.85
+[2025-06-07 18:45:06] (step=0018200) Train Loss: 0.1351, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 18:45:58] (step=0018300) Train Loss: 0.1388, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:46:49] (step=0018400) Train Loss: 0.1303, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 18:47:41] (step=0018500) Train Loss: 0.1346, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:48:33] (step=0018600) Train Loss: 0.1419, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-07 18:49:25] (step=0018700) Train Loss: 0.1310, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-07 18:50:16] (step=0018800) Train Loss: 0.1400, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:51:08] (step=0018900) Train Loss: 0.1373, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:51:59] (step=0019000) Train Loss: 0.1389, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 18:52:51] (step=0019100) Train Loss: 0.1293, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:53:43] (step=0019200) Train Loss: 0.1369, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 18:54:35] (step=0019300) Train Loss: 0.1396, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 18:55:26] (step=0019400) Train Loss: 0.1357, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-07 18:56:18] (step=0019500) Train Loss: 0.1428, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 18:57:10] (step=0019600) Train Loss: 0.1368, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 18:58:01] (step=0019700) Train Loss: 0.1323, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 18:58:53] (step=0019800) Train Loss: 0.1376, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 18:59:45] (step=0019900) Train Loss: 0.1443, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-07 19:00:36] (step=0020000) Train Loss: 0.1379, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-07 19:01:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0020000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-07 19:01:04] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-07 19:03:09] (step=0020000) Perceptual Loss: 0.6689, Eval Time: 127.67
+[2025-06-07 19:04:01] (step=0020100) Train Loss: 0.1365, Train Steps/Sec: 0.49, Samples/Sec: 17.63
+[2025-06-07 19:04:52] (step=0020200) Train Loss: 0.1331, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-07 19:05:44] (step=0020300) Train Loss: 0.1286, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 19:06:35] (step=0020400) Train Loss: 0.1369, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 19:07:27] (step=0020500) Train Loss: 0.1337, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 19:08:19] (step=0020600) Train Loss: 0.1423, Train Steps/Sec: 1.94, Samples/Sec: 69.89
+[2025-06-07 19:09:23] (step=0020700) Train Loss: 0.1334, Train Steps/Sec: 1.55, Samples/Sec: 55.82
+[2025-06-07 19:10:15] (step=0020800) Train Loss: 0.1363, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-07 19:11:07] (step=0020900) Train Loss: 0.1391, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-07 19:11:59] (step=0021000) Train Loss: 0.1402, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 19:12:51] (step=0021100) Train Loss: 0.1319, Train Steps/Sec: 1.92, Samples/Sec: 69.12
+[2025-06-07 19:13:42] (step=0021200) Train Loss: 0.1407, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-07 19:14:34] (step=0021300) Train Loss: 0.1389, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-07 19:15:26] (step=0021400) Train Loss: 0.1317, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-07 19:16:18] (step=0021500) Train Loss: 0.1347, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-07 19:17:10] (step=0021600) Train Loss: 0.1372, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-07 19:18:01] (step=0021700) Train Loss: 0.1381, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-07 19:18:53] (step=0021800) Train Loss: 0.1286, Train Steps/Sec: 1.92, Samples/Sec: 69.23
+[2025-06-07 19:19:45] (step=0021900) Train Loss: 0.1342, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-07 19:20:37] (step=0022000) Train Loss: 0.1350, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-07 19:20:50] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 19:21:42] (step=0022100) Train Loss: 0.1324, Train Steps/Sec: 1.54, Samples/Sec: 55.47
+[2025-06-07 19:22:34] (step=0022200) Train Loss: 0.1298, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-07 19:23:26] (step=0022300) Train Loss: 0.1357, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-07 19:24:17] (step=0022400) Train Loss: 0.1397, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-07 19:25:11] (step=0022500) Train Loss: 0.1397, Train Steps/Sec: 1.86, Samples/Sec: 66.99
+[2025-06-07 19:26:03] (step=0022600) Train Loss: 0.1337, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 19:26:54] (step=0022700) Train Loss: 0.1300, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 19:27:46] (step=0022800) Train Loss: 0.1314, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 19:28:38] (step=0022900) Train Loss: 0.1335, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 19:29:29] (step=0023000) Train Loss: 0.1341, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 19:30:21] (step=0023100) Train Loss: 0.1293, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 19:31:13] (step=0023200) Train Loss: 0.1406, Train Steps/Sec: 1.92, Samples/Sec: 69.20
+[2025-06-07 19:32:05] (step=0023300) Train Loss: 0.1372, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 19:32:57] (step=0023400) Train Loss: 0.1408, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 19:33:48] (step=0023500) Train Loss: 0.1290, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 19:34:40] (step=0023600) Train Loss: 0.1381, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 19:35:31] (step=0023700) Train Loss: 0.1355, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 19:36:23] (step=0023800) Train Loss: 0.1400, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-07 19:37:15] (step=0023900) Train Loss: 0.1310, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-07 19:38:07] (step=0024000) Train Loss: 0.1390, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 19:38:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 19:39:11] (step=0024100) Train Loss: 0.1403, Train Steps/Sec: 1.55, Samples/Sec: 55.66
+[2025-06-07 19:40:03] (step=0024200) Train Loss: 0.1341, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 19:40:55] (step=0024300) Train Loss: 0.1316, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-07 19:41:46] (step=0024400) Train Loss: 0.1345, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 19:42:38] (step=0024500) Train Loss: 0.1350, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-07 19:43:30] (step=0024600) Train Loss: 0.1376, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-07 19:44:22] (step=0024700) Train Loss: 0.1337, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 19:45:13] (step=0024800) Train Loss: 0.1382, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 19:46:05] (step=0024900) Train Loss: 0.1375, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 19:46:57] (step=0025000) Train Loss: 0.1344, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-07 19:47:49] (step=0025100) Train Loss: 0.1350, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-07 19:48:40] (step=0025200) Train Loss: 0.1356, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-07 19:49:32] (step=0025300) Train Loss: 0.1344, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 19:50:24] (step=0025400) Train Loss: 0.1276, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-07 19:51:16] (step=0025500) Train Loss: 0.1316, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 19:52:07] (step=0025600) Train Loss: 0.1351, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-07 19:52:59] (step=0025700) Train Loss: 0.1315, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 19:53:51] (step=0025800) Train Loss: 0.1311, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-07 19:54:43] (step=0025900) Train Loss: 0.1337, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-07 19:55:35] (step=0026000) Train Loss: 0.1310, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 19:55:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 19:56:39] (step=0026100) Train Loss: 0.1326, Train Steps/Sec: 1.55, Samples/Sec: 55.76
+[2025-06-07 19:57:31] (step=0026200) Train Loss: 0.1360, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 19:58:22] (step=0026300) Train Loss: 0.1278, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-07 19:59:14] (step=0026400) Train Loss: 0.1319, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-07 20:00:06] (step=0026500) Train Loss: 0.1423, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-07 20:00:58] (step=0026600) Train Loss: 0.1343, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 20:01:50] (step=0026700) Train Loss: 0.1291, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 20:02:41] (step=0026800) Train Loss: 0.1320, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 20:03:33] (step=0026900) Train Loss: 0.1282, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 20:04:25] (step=0027000) Train Loss: 0.1310, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 20:05:16] (step=0027100) Train Loss: 0.1349, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-07 20:06:08] (step=0027200) Train Loss: 0.1375, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-07 20:07:00] (step=0027300) Train Loss: 0.1358, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 20:07:52] (step=0027400) Train Loss: 0.1305, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 20:08:43] (step=0027500) Train Loss: 0.1333, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 20:09:35] (step=0027600) Train Loss: 0.1343, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 20:10:27] (step=0027700) Train Loss: 0.1330, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-07 20:11:18] (step=0027800) Train Loss: 0.1332, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 20:12:10] (step=0027900) Train Loss: 0.1302, Train Steps/Sec: 1.92, Samples/Sec: 69.23
+[2025-06-07 20:13:02] (step=0028000) Train Loss: 0.1263, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 20:13:16] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 20:14:07] (step=0028100) Train Loss: 0.1274, Train Steps/Sec: 1.53, Samples/Sec: 55.21
+[2025-06-07 20:14:59] (step=0028200) Train Loss: 0.1341, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 20:15:51] (step=0028300) Train Loss: 0.1329, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 20:16:42] (step=0028400) Train Loss: 0.1294, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 20:17:34] (step=0028500) Train Loss: 0.1337, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 20:18:26] (step=0028600) Train Loss: 0.1322, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-07 20:19:18] (step=0028700) Train Loss: 0.1365, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 20:20:09] (step=0028800) Train Loss: 0.1242, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 20:21:01] (step=0028900) Train Loss: 0.1256, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 20:21:53] (step=0029000) Train Loss: 0.1293, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 20:22:45] (step=0029100) Train Loss: 0.1368, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 20:23:36] (step=0029200) Train Loss: 0.1325, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-07 20:24:28] (step=0029300) Train Loss: 0.1333, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 20:25:20] (step=0029400) Train Loss: 0.1252, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 20:26:11] (step=0029500) Train Loss: 0.1364, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 20:26:32] Beginning epoch 2...
+[2025-06-07 20:27:03] (step=0029600) Train Loss: 0.1301, Train Steps/Sec: 1.93, Samples/Sec: 69.33
+[2025-06-07 20:27:55] (step=0029700) Train Loss: 0.1309, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 20:28:47] (step=0029800) Train Loss: 0.1297, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 20:29:38] (step=0029900) Train Loss: 0.1316, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 20:30:30] (step=0030000) Train Loss: 0.1301, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-07 20:30:43] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-07 20:30:46] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-07 20:32:50] (step=0030000) Perceptual Loss: 0.3670, Eval Time: 127.03
+[2025-06-07 20:33:42] (step=0030100) Train Loss: 0.1387, Train Steps/Sec: 0.52, Samples/Sec: 18.78
+[2025-06-07 20:34:33] (step=0030200) Train Loss: 0.1371, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 20:35:25] (step=0030300) Train Loss: 0.1276, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-07 20:36:17] (step=0030400) Train Loss: 0.1282, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 20:37:09] (step=0030500) Train Loss: 0.1310, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-07 20:38:00] (step=0030600) Train Loss: 0.1269, Train Steps/Sec: 1.94, Samples/Sec: 69.97
+[2025-06-07 20:38:52] (step=0030700) Train Loss: 0.1320, Train Steps/Sec: 1.95, Samples/Sec: 70.04
+[2025-06-07 20:39:43] (step=0030800) Train Loss: 0.1282, Train Steps/Sec: 1.94, Samples/Sec: 69.90
+[2025-06-07 20:40:35] (step=0030900) Train Loss: 0.1315, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 20:41:26] (step=0031000) Train Loss: 0.1318, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 20:42:18] (step=0031100) Train Loss: 0.1308, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-07 20:43:10] (step=0031200) Train Loss: 0.1397, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 20:44:02] (step=0031300) Train Loss: 0.1341, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 20:44:53] (step=0031400) Train Loss: 0.1272, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 20:45:45] (step=0031500) Train Loss: 0.1276, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 20:46:36] (step=0031600) Train Loss: 0.1297, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 20:47:28] (step=0031700) Train Loss: 0.1258, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 20:48:20] (step=0031800) Train Loss: 0.1300, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-07 20:49:12] (step=0031900) Train Loss: 0.1305, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 20:50:03] (step=0032000) Train Loss: 0.1352, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 20:50:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 20:51:08] (step=0032100) Train Loss: 0.1322, Train Steps/Sec: 1.54, Samples/Sec: 55.32
+[2025-06-07 20:52:00] (step=0032200) Train Loss: 0.1336, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 20:52:52] (step=0032300) Train Loss: 0.1345, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 20:53:43] (step=0032400) Train Loss: 0.1295, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 20:54:35] (step=0032500) Train Loss: 0.1244, Train Steps/Sec: 1.92, Samples/Sec: 69.13
+[2025-06-07 20:55:27] (step=0032600) Train Loss: 0.1253, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-07 20:56:19] (step=0032700) Train Loss: 0.1340, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-07 20:57:11] (step=0032800) Train Loss: 0.1285, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 20:58:03] (step=0032900) Train Loss: 0.1312, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 20:58:54] (step=0033000) Train Loss: 0.1246, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 20:59:46] (step=0033100) Train Loss: 0.1358, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-07 21:00:38] (step=0033200) Train Loss: 0.1287, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:01:29] (step=0033300) Train Loss: 0.1343, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:02:21] (step=0033400) Train Loss: 0.1312, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:03:13] (step=0033500) Train Loss: 0.1341, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 21:04:04] (step=0033600) Train Loss: 0.1368, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-07 21:04:56] (step=0033700) Train Loss: 0.1235, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:05:48] (step=0033800) Train Loss: 0.1328, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-07 21:06:40] (step=0033900) Train Loss: 0.1252, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:07:31] (step=0034000) Train Loss: 0.1271, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 21:07:44] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 21:08:36] (step=0034100) Train Loss: 0.1207, Train Steps/Sec: 1.55, Samples/Sec: 55.76
+[2025-06-07 21:09:27] (step=0034200) Train Loss: 0.1322, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 21:10:19] (step=0034300) Train Loss: 0.1257, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:11:11] (step=0034400) Train Loss: 0.1291, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:12:03] (step=0034500) Train Loss: 0.1300, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-07 21:12:54] (step=0034600) Train Loss: 0.1293, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:13:46] (step=0034700) Train Loss: 0.1300, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:14:37] (step=0034800) Train Loss: 0.1331, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 21:15:29] (step=0034900) Train Loss: 0.1266, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-07 21:16:21] (step=0035000) Train Loss: 0.1268, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 21:17:13] (step=0035100) Train Loss: 0.1279, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 21:18:04] (step=0035200) Train Loss: 0.1180, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-07 21:18:56] (step=0035300) Train Loss: 0.1286, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 21:19:48] (step=0035400) Train Loss: 0.1242, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 21:20:40] (step=0035500) Train Loss: 0.1370, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 21:21:31] (step=0035600) Train Loss: 0.1297, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:22:23] (step=0035700) Train Loss: 0.1252, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:23:15] (step=0035800) Train Loss: 0.1252, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-07 21:24:06] (step=0035900) Train Loss: 0.1210, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 21:24:58] (step=0036000) Train Loss: 0.1269, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 21:25:11] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 21:26:03] (step=0036100) Train Loss: 0.1353, Train Steps/Sec: 1.54, Samples/Sec: 55.50
+[2025-06-07 21:26:55] (step=0036200) Train Loss: 0.1193, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 21:27:46] (step=0036300) Train Loss: 0.1368, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 21:28:38] (step=0036400) Train Loss: 0.1232, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 21:29:30] (step=0036500) Train Loss: 0.1264, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-07 21:30:21] (step=0036600) Train Loss: 0.1331, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:31:13] (step=0036700) Train Loss: 0.1281, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 21:32:05] (step=0036800) Train Loss: 0.1238, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 21:32:56] (step=0036900) Train Loss: 0.1339, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 21:33:48] (step=0037000) Train Loss: 0.1320, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:34:40] (step=0037100) Train Loss: 0.1296, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 21:35:31] (step=0037200) Train Loss: 0.1289, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-07 21:36:23] (step=0037300) Train Loss: 0.1314, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:37:15] (step=0037400) Train Loss: 0.1274, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:38:06] (step=0037500) Train Loss: 0.1297, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 21:38:58] (step=0037600) Train Loss: 0.1287, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:39:50] (step=0037700) Train Loss: 0.1339, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:40:41] (step=0037800) Train Loss: 0.1260, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-07 21:41:33] (step=0037900) Train Loss: 0.1302, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 21:42:25] (step=0038000) Train Loss: 0.1292, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 21:42:38] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 21:43:30] (step=0038100) Train Loss: 0.1268, Train Steps/Sec: 1.55, Samples/Sec: 55.64
+[2025-06-07 21:44:21] (step=0038200) Train Loss: 0.1270, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:45:13] (step=0038300) Train Loss: 0.1324, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:46:04] (step=0038400) Train Loss: 0.1262, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 21:46:56] (step=0038500) Train Loss: 0.1293, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-07 21:47:48] (step=0038600) Train Loss: 0.1325, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:48:40] (step=0038700) Train Loss: 0.1318, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 21:49:31] (step=0038800) Train Loss: 0.1272, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:50:23] (step=0038900) Train Loss: 0.1254, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 21:51:15] (step=0039000) Train Loss: 0.1315, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 21:52:06] (step=0039100) Train Loss: 0.1250, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 21:52:58] (step=0039200) Train Loss: 0.1284, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-07 21:53:50] (step=0039300) Train Loss: 0.1307, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 21:54:42] (step=0039400) Train Loss: 0.1254, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 21:55:33] (step=0039500) Train Loss: 0.1262, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 21:56:25] (step=0039600) Train Loss: 0.1263, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 21:57:17] (step=0039700) Train Loss: 0.1220, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 21:58:08] (step=0039800) Train Loss: 0.1230, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 21:59:00] (step=0039900) Train Loss: 0.1306, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-07 21:59:52] (step=0040000) Train Loss: 0.1309, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-07 22:00:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0040000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-07 22:00:20] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-07 22:02:24] (step=0040000) Perceptual Loss: 0.2707, Eval Time: 127.08
+[2025-06-07 22:03:16] (step=0040100) Train Loss: 0.1303, Train Steps/Sec: 0.49, Samples/Sec: 17.64
+[2025-06-07 22:04:07] (step=0040200) Train Loss: 0.1277, Train Steps/Sec: 1.94, Samples/Sec: 69.84
+[2025-06-07 22:04:59] (step=0040300) Train Loss: 0.1300, Train Steps/Sec: 1.94, Samples/Sec: 69.80
+[2025-06-07 22:05:51] (step=0040400) Train Loss: 0.1341, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-07 22:06:42] (step=0040500) Train Loss: 0.1306, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 22:07:34] (step=0040600) Train Loss: 0.1339, Train Steps/Sec: 1.94, Samples/Sec: 69.81
+[2025-06-07 22:08:26] (step=0040700) Train Loss: 0.1286, Train Steps/Sec: 1.94, Samples/Sec: 69.93
+[2025-06-07 22:09:17] (step=0040800) Train Loss: 0.1237, Train Steps/Sec: 1.95, Samples/Sec: 70.02
+[2025-06-07 22:10:09] (step=0040900) Train Loss: 0.1283, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-07 22:11:00] (step=0041000) Train Loss: 0.1296, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-07 22:11:52] (step=0041100) Train Loss: 0.1270, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-07 22:12:44] (step=0041200) Train Loss: 0.1243, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 22:13:36] (step=0041300) Train Loss: 0.1195, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-07 22:14:27] (step=0041400) Train Loss: 0.1323, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 22:15:19] (step=0041500) Train Loss: 0.1285, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 22:16:11] (step=0041600) Train Loss: 0.1270, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-07 22:17:02] (step=0041700) Train Loss: 0.1230, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 22:17:54] (step=0041800) Train Loss: 0.1268, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-07 22:18:46] (step=0041900) Train Loss: 0.1255, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 22:19:38] (step=0042000) Train Loss: 0.1286, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 22:19:51] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 22:20:43] (step=0042100) Train Loss: 0.1292, Train Steps/Sec: 1.54, Samples/Sec: 55.26
+[2025-06-07 22:21:34] (step=0042200) Train Loss: 0.1263, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 22:22:26] (step=0042300) Train Loss: 0.1306, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 22:23:18] (step=0042400) Train Loss: 0.1262, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-07 22:24:10] (step=0042500) Train Loss: 0.1297, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 22:25:01] (step=0042600) Train Loss: 0.1287, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 22:25:53] (step=0042700) Train Loss: 0.1320, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 22:26:45] (step=0042800) Train Loss: 0.1253, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 22:27:36] (step=0042900) Train Loss: 0.1277, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-07 22:28:28] (step=0043000) Train Loss: 0.1264, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 22:29:20] (step=0043100) Train Loss: 0.1246, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-07 22:30:12] (step=0043200) Train Loss: 0.1300, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 22:31:03] (step=0043300) Train Loss: 0.1267, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-07 22:31:55] (step=0043400) Train Loss: 0.1221, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 22:32:47] (step=0043500) Train Loss: 0.1291, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 22:33:38] (step=0043600) Train Loss: 0.1211, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 22:34:30] (step=0043700) Train Loss: 0.1279, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-07 22:35:22] (step=0043800) Train Loss: 0.1245, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-07 22:36:14] (step=0043900) Train Loss: 0.1285, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 22:37:05] (step=0044000) Train Loss: 0.1223, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-07 22:37:18] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 22:38:10] (step=0044100) Train Loss: 0.1265, Train Steps/Sec: 1.54, Samples/Sec: 55.59
+[2025-06-07 22:39:02] (step=0044200) Train Loss: 0.1260, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 22:39:53] (step=0044300) Train Loss: 0.1256, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 22:39:58] Beginning epoch 3...
+[2025-06-07 22:40:45] (step=0044400) Train Loss: 0.1241, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-07 22:41:37] (step=0044500) Train Loss: 0.1273, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-07 22:42:29] (step=0044600) Train Loss: 0.1250, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 22:43:20] (step=0044700) Train Loss: 0.1304, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 22:44:12] (step=0044800) Train Loss: 0.1317, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 22:45:04] (step=0044900) Train Loss: 0.1215, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-07 22:45:56] (step=0045000) Train Loss: 0.1301, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 22:46:47] (step=0045100) Train Loss: 0.1254, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-07 22:47:39] (step=0045200) Train Loss: 0.1247, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-07 22:48:31] (step=0045300) Train Loss: 0.1232, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 22:49:22] (step=0045400) Train Loss: 0.1217, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 22:50:14] (step=0045500) Train Loss: 0.1183, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-07 22:51:06] (step=0045600) Train Loss: 0.1247, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 22:51:57] (step=0045700) Train Loss: 0.1241, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 22:52:49] (step=0045800) Train Loss: 0.1292, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-07 22:53:41] (step=0045900) Train Loss: 0.1219, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-07 22:54:33] (step=0046000) Train Loss: 0.1280, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 22:54:46] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 22:55:37] (step=0046100) Train Loss: 0.1243, Train Steps/Sec: 1.55, Samples/Sec: 55.72
+[2025-06-07 22:56:29] (step=0046200) Train Loss: 0.1186, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 22:57:21] (step=0046300) Train Loss: 0.1255, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 22:58:12] (step=0046400) Train Loss: 0.1271, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 22:59:04] (step=0046500) Train Loss: 0.1306, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-07 22:59:56] (step=0046600) Train Loss: 0.1163, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 23:00:47] (step=0046700) Train Loss: 0.1185, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 23:01:39] (step=0046800) Train Loss: 0.1262, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 23:02:31] (step=0046900) Train Loss: 0.1280, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-07 23:03:22] (step=0047000) Train Loss: 0.1292, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 23:04:14] (step=0047100) Train Loss: 0.1231, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 23:05:06] (step=0047200) Train Loss: 0.1263, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-07 23:05:58] (step=0047300) Train Loss: 0.1302, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 23:06:49] (step=0047400) Train Loss: 0.1187, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 23:07:41] (step=0047500) Train Loss: 0.1282, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 23:08:33] (step=0047600) Train Loss: 0.1243, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 23:09:24] (step=0047700) Train Loss: 0.1232, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 23:10:16] (step=0047800) Train Loss: 0.1182, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-07 23:11:08] (step=0047900) Train Loss: 0.1240, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-07 23:11:59] (step=0048000) Train Loss: 0.1294, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 23:12:13] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 23:13:04] (step=0048100) Train Loss: 0.1269, Train Steps/Sec: 1.54, Samples/Sec: 55.40
+[2025-06-07 23:13:56] (step=0048200) Train Loss: 0.1233, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 23:14:48] (step=0048300) Train Loss: 0.1229, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 23:15:39] (step=0048400) Train Loss: 0.1244, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-07 23:16:31] (step=0048500) Train Loss: 0.1259, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-07 23:17:23] (step=0048600) Train Loss: 0.1285, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 23:18:15] (step=0048700) Train Loss: 0.1222, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 23:19:06] (step=0048800) Train Loss: 0.1288, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 23:19:58] (step=0048900) Train Loss: 0.1255, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-07 23:20:50] (step=0049000) Train Loss: 0.1249, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-07 23:21:41] (step=0049100) Train Loss: 0.1182, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-07 23:22:33] (step=0049200) Train Loss: 0.1298, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-07 23:23:25] (step=0049300) Train Loss: 0.1266, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 23:24:17] (step=0049400) Train Loss: 0.1257, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-07 23:25:08] (step=0049500) Train Loss: 0.1298, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 23:26:00] (step=0049600) Train Loss: 0.1258, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 23:26:52] (step=0049700) Train Loss: 0.1227, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 23:27:43] (step=0049800) Train Loss: 0.1238, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-07 23:28:35] (step=0049900) Train Loss: 0.1213, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-07 23:29:27] (step=0050000) Train Loss: 0.1254, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-07 23:29:41] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-07 23:29:44] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-07 23:31:48] (step=0050000) Perceptual Loss: 0.2274, Eval Time: 127.28
+[2025-06-07 23:32:40] (step=0050100) Train Loss: 0.1255, Train Steps/Sec: 0.52, Samples/Sec: 18.65
+[2025-06-07 23:33:32] (step=0050200) Train Loss: 0.1304, Train Steps/Sec: 1.94, Samples/Sec: 69.78
+[2025-06-07 23:34:23] (step=0050300) Train Loss: 0.1270, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-07 23:35:15] (step=0050400) Train Loss: 0.1259, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-07 23:36:07] (step=0050500) Train Loss: 0.1254, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-07 23:36:58] (step=0050600) Train Loss: 0.1206, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 23:37:50] (step=0050700) Train Loss: 0.1212, Train Steps/Sec: 1.94, Samples/Sec: 69.96
+[2025-06-07 23:38:41] (step=0050800) Train Loss: 0.1202, Train Steps/Sec: 1.94, Samples/Sec: 70.00
+[2025-06-07 23:39:33] (step=0050900) Train Loss: 0.1227, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-07 23:40:25] (step=0051000) Train Loss: 0.1272, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 23:41:17] (step=0051100) Train Loss: 0.1172, Train Steps/Sec: 1.92, Samples/Sec: 69.25
+[2025-06-07 23:42:08] (step=0051200) Train Loss: 0.1276, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 23:43:00] (step=0051300) Train Loss: 0.1209, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 23:43:52] (step=0051400) Train Loss: 0.1207, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 23:44:43] (step=0051500) Train Loss: 0.1202, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-07 23:45:35] (step=0051600) Train Loss: 0.1237, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-07 23:46:27] (step=0051700) Train Loss: 0.1226, Train Steps/Sec: 1.92, Samples/Sec: 69.26
+[2025-06-07 23:47:19] (step=0051800) Train Loss: 0.1108, Train Steps/Sec: 1.92, Samples/Sec: 69.20
+[2025-06-07 23:48:11] (step=0051900) Train Loss: 0.1266, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-07 23:49:03] (step=0052000) Train Loss: 0.1222, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-07 23:49:15] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-07 23:50:07] (step=0052100) Train Loss: 0.1193, Train Steps/Sec: 1.55, Samples/Sec: 55.73
+[2025-06-07 23:50:59] (step=0052200) Train Loss: 0.1283, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-07 23:51:50] (step=0052300) Train Loss: 0.1238, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-07 23:52:42] (step=0052400) Train Loss: 0.1185, Train Steps/Sec: 1.92, Samples/Sec: 69.23
+[2025-06-07 23:53:34] (step=0052500) Train Loss: 0.1183, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 23:54:26] (step=0052600) Train Loss: 0.1315, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-07 23:55:18] (step=0052700) Train Loss: 0.1244, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-07 23:56:09] (step=0052800) Train Loss: 0.1213, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-07 23:57:01] (step=0052900) Train Loss: 0.1268, Train Steps/Sec: 1.94, Samples/Sec: 69.78
+[2025-06-07 23:57:53] (step=0053000) Train Loss: 0.1235, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-07 23:58:44] (step=0053100) Train Loss: 0.1220, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-07 23:59:36] (step=0053200) Train Loss: 0.1245, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 00:00:28] (step=0053300) Train Loss: 0.1246, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 00:01:19] (step=0053400) Train Loss: 0.1201, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 00:02:11] (step=0053500) Train Loss: 0.1247, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 00:03:02] (step=0053600) Train Loss: 0.1240, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 00:03:54] (step=0053700) Train Loss: 0.1194, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 00:04:46] (step=0053800) Train Loss: 0.1243, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 00:05:38] (step=0053900) Train Loss: 0.1244, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 00:06:29] (step=0054000) Train Loss: 0.1217, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 00:06:43] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 00:07:34] (step=0054100) Train Loss: 0.1152, Train Steps/Sec: 1.54, Samples/Sec: 55.43
+[2025-06-08 00:08:26] (step=0054200) Train Loss: 0.1265, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 00:09:17] (step=0054300) Train Loss: 0.1223, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 00:10:09] (step=0054400) Train Loss: 0.1190, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 00:11:01] (step=0054500) Train Loss: 0.1205, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 00:11:53] (step=0054600) Train Loss: 0.1193, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 00:12:44] (step=0054700) Train Loss: 0.1280, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 00:13:36] (step=0054800) Train Loss: 0.1202, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 00:14:27] (step=0054900) Train Loss: 0.1170, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 00:15:19] (step=0055000) Train Loss: 0.1213, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 00:16:11] (step=0055100) Train Loss: 0.1253, Train Steps/Sec: 1.93, Samples/Sec: 69.33
+[2025-06-08 00:17:03] (step=0055200) Train Loss: 0.1193, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 00:17:55] (step=0055300) Train Loss: 0.1259, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 00:18:46] (step=0055400) Train Loss: 0.1220, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 00:19:38] (step=0055500) Train Loss: 0.1277, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 00:20:30] (step=0055600) Train Loss: 0.1218, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 00:21:22] (step=0055700) Train Loss: 0.1231, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 00:22:14] (step=0055800) Train Loss: 0.1226, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 00:23:05] (step=0055900) Train Loss: 0.1183, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 00:23:57] (step=0056000) Train Loss: 0.1233, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 00:24:11] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 00:25:03] (step=0056100) Train Loss: 0.1178, Train Steps/Sec: 1.52, Samples/Sec: 54.72
+[2025-06-08 00:25:54] (step=0056200) Train Loss: 0.1229, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 00:26:46] (step=0056300) Train Loss: 0.1316, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 00:27:38] (step=0056400) Train Loss: 0.1266, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 00:28:30] (step=0056500) Train Loss: 0.1282, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 00:29:21] (step=0056600) Train Loss: 0.1188, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 00:30:13] (step=0056700) Train Loss: 0.1254, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 00:31:05] (step=0056800) Train Loss: 0.1231, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 00:31:56] (step=0056900) Train Loss: 0.1246, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 00:32:48] (step=0057000) Train Loss: 0.1215, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 00:33:40] (step=0057100) Train Loss: 0.1214, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 00:34:31] (step=0057200) Train Loss: 0.1199, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 00:35:23] (step=0057300) Train Loss: 0.1196, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 00:36:15] (step=0057400) Train Loss: 0.1258, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 00:37:06] (step=0057500) Train Loss: 0.1192, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 00:37:58] (step=0057600) Train Loss: 0.1221, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 00:38:50] (step=0057700) Train Loss: 0.1171, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 00:39:42] (step=0057800) Train Loss: 0.1254, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 00:40:33] (step=0057900) Train Loss: 0.1169, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 00:41:25] (step=0058000) Train Loss: 0.1207, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 00:41:38] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 00:42:33] (step=0058100) Train Loss: 0.1186, Train Steps/Sec: 1.46, Samples/Sec: 52.62
+[2025-06-08 00:43:25] (step=0058200) Train Loss: 0.1238, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-08 00:44:17] (step=0058300) Train Loss: 0.1311, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 00:45:09] (step=0058400) Train Loss: 0.1181, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 00:46:09] (step=0058500) Train Loss: 0.1201, Train Steps/Sec: 1.66, Samples/Sec: 59.63
+[2025-06-08 00:47:01] (step=0058600) Train Loss: 0.1185, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 00:47:55] (step=0058700) Train Loss: 0.1259, Train Steps/Sec: 1.84, Samples/Sec: 66.39
+[2025-06-08 00:48:47] (step=0058800) Train Loss: 0.1207, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 00:49:38] (step=0058900) Train Loss: 0.1208, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 00:50:35] (step=0059000) Train Loss: 0.1221, Train Steps/Sec: 1.78, Samples/Sec: 64.07
+[2025-06-08 00:51:22] Beginning epoch 4...
+[2025-06-08 00:51:32] (step=0059100) Train Loss: 0.1238, Train Steps/Sec: 1.73, Samples/Sec: 62.43
+[2025-06-08 00:52:24] (step=0059200) Train Loss: 0.1207, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-08 00:53:16] (step=0059300) Train Loss: 0.1277, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 00:54:08] (step=0059400) Train Loss: 0.1262, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 00:55:10] (step=0059500) Train Loss: 0.1290, Train Steps/Sec: 1.61, Samples/Sec: 58.02
+[2025-06-08 00:56:01] (step=0059600) Train Loss: 0.1286, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 00:56:57] (step=0059700) Train Loss: 0.1243, Train Steps/Sec: 1.81, Samples/Sec: 65.13
+[2025-06-08 00:57:48] (step=0059800) Train Loss: 0.1177, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 00:58:40] (step=0059900) Train Loss: 0.1203, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 00:59:35] (step=0060000) Train Loss: 0.1165, Train Steps/Sec: 1.83, Samples/Sec: 65.88
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 01:00:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0060000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 01:00:03] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 01:02:10] (step=0060000) Perceptual Loss: 0.1810, Eval Time: 129.40
+[2025-06-08 01:03:02] (step=0060100) Train Loss: 0.1207, Train Steps/Sec: 0.48, Samples/Sec: 17.40
+[2025-06-08 01:03:53] (step=0060200) Train Loss: 0.1250, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 01:04:45] (step=0060300) Train Loss: 0.1200, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 01:05:37] (step=0060400) Train Loss: 0.1182, Train Steps/Sec: 1.92, Samples/Sec: 69.22
+[2025-06-08 01:06:29] (step=0060500) Train Loss: 0.1202, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 01:07:21] (step=0060600) Train Loss: 0.1212, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 01:08:17] (step=0060700) Train Loss: 0.1268, Train Steps/Sec: 1.78, Samples/Sec: 64.16
+[2025-06-08 01:09:08] (step=0060800) Train Loss: 0.1191, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 01:10:00] (step=0060900) Train Loss: 0.1219, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 01:10:53] (step=0061000) Train Loss: 0.1255, Train Steps/Sec: 1.88, Samples/Sec: 67.79
+[2025-06-08 01:11:45] (step=0061100) Train Loss: 0.1230, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 01:12:37] (step=0061200) Train Loss: 0.1248, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 01:13:28] (step=0061300) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 01:14:20] (step=0061400) Train Loss: 0.1157, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 01:15:12] (step=0061500) Train Loss: 0.1174, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 01:16:04] (step=0061600) Train Loss: 0.1202, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 01:16:55] (step=0061700) Train Loss: 0.1241, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 01:17:47] (step=0061800) Train Loss: 0.1202, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 01:18:39] (step=0061900) Train Loss: 0.1205, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 01:19:31] (step=0062000) Train Loss: 0.1290, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 01:19:45] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 01:20:37] (step=0062100) Train Loss: 0.1189, Train Steps/Sec: 1.52, Samples/Sec: 54.72
+[2025-06-08 01:21:32] (step=0062200) Train Loss: 0.1171, Train Steps/Sec: 1.80, Samples/Sec: 64.81
+[2025-06-08 01:22:24] (step=0062300) Train Loss: 0.1237, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 01:23:16] (step=0062400) Train Loss: 0.1272, Train Steps/Sec: 1.92, Samples/Sec: 69.29
+[2025-06-08 01:24:08] (step=0062500) Train Loss: 0.1171, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 01:24:59] (step=0062600) Train Loss: 0.1263, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 01:25:51] (step=0062700) Train Loss: 0.1202, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 01:26:43] (step=0062800) Train Loss: 0.1207, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 01:27:35] (step=0062900) Train Loss: 0.1245, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 01:28:26] (step=0063000) Train Loss: 0.1176, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 01:29:18] (step=0063100) Train Loss: 0.1236, Train Steps/Sec: 1.92, Samples/Sec: 69.02
+[2025-06-08 01:30:10] (step=0063200) Train Loss: 0.1258, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 01:31:02] (step=0063300) Train Loss: 0.1231, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 01:31:53] (step=0063400) Train Loss: 0.1272, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 01:32:45] (step=0063500) Train Loss: 0.1246, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 01:33:37] (step=0063600) Train Loss: 0.1197, Train Steps/Sec: 1.92, Samples/Sec: 69.28
+[2025-06-08 01:34:29] (step=0063700) Train Loss: 0.1269, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 01:35:21] (step=0063800) Train Loss: 0.1177, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 01:36:13] (step=0063900) Train Loss: 0.1240, Train Steps/Sec: 1.93, Samples/Sec: 69.33
+[2025-06-08 01:37:04] (step=0064000) Train Loss: 0.1189, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 01:37:19] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 01:38:10] (step=0064100) Train Loss: 0.1253, Train Steps/Sec: 1.52, Samples/Sec: 54.72
+[2025-06-08 01:39:02] (step=0064200) Train Loss: 0.1235, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 01:39:53] (step=0064300) Train Loss: 0.1209, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 01:40:45] (step=0064400) Train Loss: 0.1214, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 01:41:37] (step=0064500) Train Loss: 0.1232, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 01:42:29] (step=0064600) Train Loss: 0.1148, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 01:43:21] (step=0064700) Train Loss: 0.1271, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 01:44:12] (step=0064800) Train Loss: 0.1220, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 01:45:04] (step=0064900) Train Loss: 0.1192, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 01:45:56] (step=0065000) Train Loss: 0.1175, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 01:46:48] (step=0065100) Train Loss: 0.1248, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 01:47:39] (step=0065200) Train Loss: 0.1265, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 01:48:31] (step=0065300) Train Loss: 0.1253, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 01:49:23] (step=0065400) Train Loss: 0.1170, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 01:50:15] (step=0065500) Train Loss: 0.1192, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 01:51:06] (step=0065600) Train Loss: 0.1192, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 01:51:58] (step=0065700) Train Loss: 0.1200, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 01:52:50] (step=0065800) Train Loss: 0.1147, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 01:53:42] (step=0065900) Train Loss: 0.1209, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 01:54:33] (step=0066000) Train Loss: 0.1189, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 01:54:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 01:55:39] (step=0066100) Train Loss: 0.1224, Train Steps/Sec: 1.52, Samples/Sec: 54.76
+[2025-06-08 01:56:31] (step=0066200) Train Loss: 0.1260, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 01:57:22] (step=0066300) Train Loss: 0.1248, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 01:58:14] (step=0066400) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 01:59:06] (step=0066500) Train Loss: 0.1141, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 01:59:58] (step=0066600) Train Loss: 0.1175, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 02:00:49] (step=0066700) Train Loss: 0.1262, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 02:01:41] (step=0066800) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 02:02:33] (step=0066900) Train Loss: 0.1192, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 02:03:24] (step=0067000) Train Loss: 0.1160, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 02:04:16] (step=0067100) Train Loss: 0.1157, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 02:05:08] (step=0067200) Train Loss: 0.1172, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 02:06:00] (step=0067300) Train Loss: 0.1163, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 02:06:51] (step=0067400) Train Loss: 0.1161, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 02:07:43] (step=0067500) Train Loss: 0.1178, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 02:08:35] (step=0067600) Train Loss: 0.1275, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 02:09:26] (step=0067700) Train Loss: 0.1236, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 02:10:18] (step=0067800) Train Loss: 0.1238, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 02:11:10] (step=0067900) Train Loss: 0.1244, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 02:12:02] (step=0068000) Train Loss: 0.1163, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 02:12:16] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 02:13:07] (step=0068100) Train Loss: 0.1215, Train Steps/Sec: 1.53, Samples/Sec: 54.92
+[2025-06-08 02:14:01] (step=0068200) Train Loss: 0.1237, Train Steps/Sec: 1.87, Samples/Sec: 67.32
+[2025-06-08 02:14:53] (step=0068300) Train Loss: 0.1235, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 02:15:44] (step=0068400) Train Loss: 0.1205, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 02:16:36] (step=0068500) Train Loss: 0.1251, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 02:17:28] (step=0068600) Train Loss: 0.1240, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 02:18:20] (step=0068700) Train Loss: 0.1240, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 02:19:12] (step=0068800) Train Loss: 0.1194, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 02:20:03] (step=0068900) Train Loss: 0.1296, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 02:20:55] (step=0069000) Train Loss: 0.1192, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 02:21:47] (step=0069100) Train Loss: 0.1175, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 02:22:39] (step=0069200) Train Loss: 0.1289, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 02:23:30] (step=0069300) Train Loss: 0.1205, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 02:24:22] (step=0069400) Train Loss: 0.1230, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 02:25:14] (step=0069500) Train Loss: 0.1190, Train Steps/Sec: 1.91, Samples/Sec: 68.76
+[2025-06-08 02:26:06] (step=0069600) Train Loss: 0.1177, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 02:26:59] (step=0069700) Train Loss: 0.1230, Train Steps/Sec: 1.89, Samples/Sec: 68.04
+[2025-06-08 02:27:54] (step=0069800) Train Loss: 0.1235, Train Steps/Sec: 1.82, Samples/Sec: 65.41
+[2025-06-08 02:28:51] (step=0069900) Train Loss: 0.1179, Train Steps/Sec: 1.77, Samples/Sec: 63.83
+[2025-06-08 02:29:42] (step=0070000) Train Loss: 0.1181, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 02:30:00] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 02:30:02] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 02:32:07] (step=0070000) Perceptual Loss: 0.1753, Eval Time: 127.45
+[2025-06-08 02:33:00] (step=0070100) Train Loss: 0.1184, Train Steps/Sec: 0.51, Samples/Sec: 18.22
+[2025-06-08 02:33:52] (step=0070200) Train Loss: 0.1184, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 02:34:43] (step=0070300) Train Loss: 0.1162, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 02:35:35] (step=0070400) Train Loss: 0.1219, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-08 02:36:27] (step=0070500) Train Loss: 0.1239, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 02:37:19] (step=0070600) Train Loss: 0.1228, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 02:38:11] (step=0070700) Train Loss: 0.1175, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 02:39:02] (step=0070800) Train Loss: 0.1171, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 02:39:54] (step=0070900) Train Loss: 0.1194, Train Steps/Sec: 1.94, Samples/Sec: 69.95
+[2025-06-08 02:40:45] (step=0071000) Train Loss: 0.1221, Train Steps/Sec: 1.94, Samples/Sec: 69.90
+[2025-06-08 02:41:37] (step=0071100) Train Loss: 0.1235, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 02:42:29] (step=0071200) Train Loss: 0.1200, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 02:43:21] (step=0071300) Train Loss: 0.1244, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 02:44:12] (step=0071400) Train Loss: 0.1196, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 02:45:04] (step=0071500) Train Loss: 0.1198, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 02:45:56] (step=0071600) Train Loss: 0.1249, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 02:46:53] (step=0071700) Train Loss: 0.1253, Train Steps/Sec: 1.76, Samples/Sec: 63.20
+[2025-06-08 02:47:45] (step=0071800) Train Loss: 0.1184, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 02:48:37] (step=0071900) Train Loss: 0.1259, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 02:49:29] (step=0072000) Train Loss: 0.1233, Train Steps/Sec: 1.92, Samples/Sec: 69.25
+[2025-06-08 02:49:43] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 02:50:35] (step=0072100) Train Loss: 0.1184, Train Steps/Sec: 1.50, Samples/Sec: 54.12
+[2025-06-08 02:51:27] (step=0072200) Train Loss: 0.1235, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 02:52:19] (step=0072300) Train Loss: 0.1202, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 02:53:11] (step=0072400) Train Loss: 0.1196, Train Steps/Sec: 1.93, Samples/Sec: 69.31
+[2025-06-08 02:54:02] (step=0072500) Train Loss: 0.1221, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 02:54:54] (step=0072600) Train Loss: 0.1159, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 02:55:46] (step=0072700) Train Loss: 0.1220, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 02:56:39] (step=0072800) Train Loss: 0.1135, Train Steps/Sec: 1.88, Samples/Sec: 67.53
+[2025-06-08 02:57:31] (step=0072900) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 02:58:23] (step=0073000) Train Loss: 0.1283, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 02:59:14] (step=0073100) Train Loss: 0.1134, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 03:00:06] (step=0073200) Train Loss: 0.1176, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 03:00:58] (step=0073300) Train Loss: 0.1125, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 03:01:49] (step=0073400) Train Loss: 0.1187, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 03:02:41] (step=0073500) Train Loss: 0.1156, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 03:03:33] (step=0073600) Train Loss: 0.1161, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 03:04:25] (step=0073700) Train Loss: 0.1273, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 03:05:16] (step=0073800) Train Loss: 0.1199, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 03:05:42] Beginning epoch 5...
+[2025-06-08 03:06:08] (step=0073900) Train Loss: 0.1162, Train Steps/Sec: 1.93, Samples/Sec: 69.32
+[2025-06-08 03:07:00] (step=0074000) Train Loss: 0.1163, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 03:07:14] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 03:08:05] (step=0074100) Train Loss: 0.1176, Train Steps/Sec: 1.53, Samples/Sec: 54.98
+[2025-06-08 03:08:57] (step=0074200) Train Loss: 0.1223, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 03:09:49] (step=0074300) Train Loss: 0.1182, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 03:10:40] (step=0074400) Train Loss: 0.1234, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 03:11:32] (step=0074500) Train Loss: 0.1110, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 03:12:24] (step=0074600) Train Loss: 0.1151, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:13:15] (step=0074700) Train Loss: 0.1196, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 03:14:07] (step=0074800) Train Loss: 0.1150, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 03:14:59] (step=0074900) Train Loss: 0.1186, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 03:15:50] (step=0075000) Train Loss: 0.1167, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 03:16:42] (step=0075100) Train Loss: 0.1195, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 03:17:34] (step=0075200) Train Loss: 0.1215, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 03:18:25] (step=0075300) Train Loss: 0.1210, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:19:17] (step=0075400) Train Loss: 0.1165, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 03:20:09] (step=0075500) Train Loss: 0.1216, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 03:21:00] (step=0075600) Train Loss: 0.1225, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:21:52] (step=0075700) Train Loss: 0.1138, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:22:44] (step=0075800) Train Loss: 0.1215, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 03:23:35] (step=0075900) Train Loss: 0.1216, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 03:24:27] (step=0076000) Train Loss: 0.1290, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:24:41] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 03:25:32] (step=0076100) Train Loss: 0.1179, Train Steps/Sec: 1.53, Samples/Sec: 55.19
+[2025-06-08 03:26:24] (step=0076200) Train Loss: 0.1178, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 03:27:16] (step=0076300) Train Loss: 0.1212, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 03:28:07] (step=0076400) Train Loss: 0.1234, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 03:28:59] (step=0076500) Train Loss: 0.1215, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 03:29:51] (step=0076600) Train Loss: 0.1218, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 03:30:42] (step=0076700) Train Loss: 0.1174, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 03:31:34] (step=0076800) Train Loss: 0.1205, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 03:32:26] (step=0076900) Train Loss: 0.1250, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 03:33:17] (step=0077000) Train Loss: 0.1178, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 03:34:09] (step=0077100) Train Loss: 0.1103, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 03:35:01] (step=0077200) Train Loss: 0.1206, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 03:35:52] (step=0077300) Train Loss: 0.1124, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 03:36:44] (step=0077400) Train Loss: 0.1173, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 03:37:36] (step=0077500) Train Loss: 0.1216, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 03:38:27] (step=0077600) Train Loss: 0.1213, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:39:19] (step=0077700) Train Loss: 0.1211, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 03:40:11] (step=0077800) Train Loss: 0.1209, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 03:41:03] (step=0077900) Train Loss: 0.1274, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 03:41:54] (step=0078000) Train Loss: 0.1190, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:42:08] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 03:43:00] (step=0078100) Train Loss: 0.1199, Train Steps/Sec: 1.53, Samples/Sec: 55.14
+[2025-06-08 03:43:51] (step=0078200) Train Loss: 0.1221, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 03:44:43] (step=0078300) Train Loss: 0.1226, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 03:45:34] (step=0078400) Train Loss: 0.1103, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 03:46:26] (step=0078500) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 03:47:18] (step=0078600) Train Loss: 0.1232, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 03:48:09] (step=0078700) Train Loss: 0.1196, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 03:49:01] (step=0078800) Train Loss: 0.1205, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:49:53] (step=0078900) Train Loss: 0.1196, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 03:50:45] (step=0079000) Train Loss: 0.1121, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 03:51:36] (step=0079100) Train Loss: 0.1185, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 03:52:28] (step=0079200) Train Loss: 0.1156, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 03:53:20] (step=0079300) Train Loss: 0.1239, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 03:54:11] (step=0079400) Train Loss: 0.1184, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 03:55:03] (step=0079500) Train Loss: 0.1186, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 03:55:55] (step=0079600) Train Loss: 0.1214, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 03:56:46] (step=0079700) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 03:57:38] (step=0079800) Train Loss: 0.1166, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 03:58:30] (step=0079900) Train Loss: 0.1197, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 03:59:21] (step=0080000) Train Loss: 0.1194, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 03:59:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0080000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 03:59:50] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 04:01:55] (step=0080000) Perceptual Loss: 0.1614, Eval Time: 127.40
+[2025-06-08 04:02:46] (step=0080100) Train Loss: 0.1185, Train Steps/Sec: 0.49, Samples/Sec: 17.57
+[2025-06-08 04:03:38] (step=0080200) Train Loss: 0.1151, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 04:04:30] (step=0080300) Train Loss: 0.1196, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 04:05:22] (step=0080400) Train Loss: 0.1226, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 04:06:13] (step=0080500) Train Loss: 0.1248, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 04:07:05] (step=0080600) Train Loss: 0.1180, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 04:07:56] (step=0080700) Train Loss: 0.1258, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 04:08:48] (step=0080800) Train Loss: 0.1139, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 04:09:40] (step=0080900) Train Loss: 0.1164, Train Steps/Sec: 1.94, Samples/Sec: 69.93
+[2025-06-08 04:10:31] (step=0081000) Train Loss: 0.1151, Train Steps/Sec: 1.94, Samples/Sec: 69.94
+[2025-06-08 04:11:23] (step=0081100) Train Loss: 0.1243, Train Steps/Sec: 1.94, Samples/Sec: 69.81
+[2025-06-08 04:12:14] (step=0081200) Train Loss: 0.1229, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:13:06] (step=0081300) Train Loss: 0.1257, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 04:13:58] (step=0081400) Train Loss: 0.1168, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:14:49] (step=0081500) Train Loss: 0.1239, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 04:15:41] (step=0081600) Train Loss: 0.1183, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 04:16:33] (step=0081700) Train Loss: 0.1162, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-08 04:17:25] (step=0081800) Train Loss: 0.1225, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 04:18:16] (step=0081900) Train Loss: 0.1207, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 04:19:08] (step=0082000) Train Loss: 0.1205, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:19:21] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 04:20:13] (step=0082100) Train Loss: 0.1180, Train Steps/Sec: 1.54, Samples/Sec: 55.36
+[2025-06-08 04:21:05] (step=0082200) Train Loss: 0.1253, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 04:21:57] (step=0082300) Train Loss: 0.1164, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:22:48] (step=0082400) Train Loss: 0.1193, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 04:23:40] (step=0082500) Train Loss: 0.1184, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 04:24:32] (step=0082600) Train Loss: 0.1226, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 04:25:24] (step=0082700) Train Loss: 0.1195, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:26:15] (step=0082800) Train Loss: 0.1197, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 04:27:07] (step=0082900) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 04:27:59] (step=0083000) Train Loss: 0.1229, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 04:28:51] (step=0083100) Train Loss: 0.1176, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 04:29:42] (step=0083200) Train Loss: 0.1155, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:30:34] (step=0083300) Train Loss: 0.1213, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 04:31:26] (step=0083400) Train Loss: 0.1201, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 04:32:17] (step=0083500) Train Loss: 0.1153, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 04:33:09] (step=0083600) Train Loss: 0.1218, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 04:34:01] (step=0083700) Train Loss: 0.1191, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-08 04:34:53] (step=0083800) Train Loss: 0.1164, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 04:35:44] (step=0083900) Train Loss: 0.1186, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 04:36:36] (step=0084000) Train Loss: 0.1177, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:36:50] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 04:37:41] (step=0084100) Train Loss: 0.1153, Train Steps/Sec: 1.52, Samples/Sec: 54.87
+[2025-06-08 04:38:33] (step=0084200) Train Loss: 0.1231, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 04:39:25] (step=0084300) Train Loss: 0.1168, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 04:40:17] (step=0084400) Train Loss: 0.1211, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-08 04:41:08] (step=0084500) Train Loss: 0.1190, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:42:00] (step=0084600) Train Loss: 0.1185, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 04:42:52] (step=0084700) Train Loss: 0.1212, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 04:43:43] (step=0084800) Train Loss: 0.1187, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 04:44:35] (step=0084900) Train Loss: 0.1282, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 04:45:27] (step=0085000) Train Loss: 0.1208, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:46:19] (step=0085100) Train Loss: 0.1172, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 04:47:10] (step=0085200) Train Loss: 0.1203, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 04:48:02] (step=0085300) Train Loss: 0.1239, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 04:48:54] (step=0085400) Train Loss: 0.1179, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 04:49:45] (step=0085500) Train Loss: 0.1186, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 04:50:37] (step=0085600) Train Loss: 0.1196, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 04:51:29] (step=0085700) Train Loss: 0.1170, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 04:52:21] (step=0085800) Train Loss: 0.1183, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 04:53:12] (step=0085900) Train Loss: 0.1293, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 04:54:04] (step=0086000) Train Loss: 0.1167, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 04:54:18] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 04:55:09] (step=0086100) Train Loss: 0.1218, Train Steps/Sec: 1.53, Samples/Sec: 54.92
+[2025-06-08 04:56:01] (step=0086200) Train Loss: 0.1219, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 04:56:53] (step=0086300) Train Loss: 0.1084, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 04:57:45] (step=0086400) Train Loss: 0.1182, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 04:58:36] (step=0086500) Train Loss: 0.1204, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 04:59:28] (step=0086600) Train Loss: 0.1172, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 05:00:20] (step=0086700) Train Loss: 0.1160, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 05:01:11] (step=0086800) Train Loss: 0.1140, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 05:02:03] (step=0086900) Train Loss: 0.1191, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 05:02:55] (step=0087000) Train Loss: 0.1131, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 05:03:46] (step=0087100) Train Loss: 0.1165, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 05:04:38] (step=0087200) Train Loss: 0.1165, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 05:05:30] (step=0087300) Train Loss: 0.1223, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 05:06:21] (step=0087400) Train Loss: 0.1175, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 05:07:13] (step=0087500) Train Loss: 0.1208, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 05:08:05] (step=0087600) Train Loss: 0.1156, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 05:08:57] (step=0087700) Train Loss: 0.1202, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 05:09:48] (step=0087800) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 05:10:40] (step=0087900) Train Loss: 0.1153, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 05:11:32] (step=0088000) Train Loss: 0.1181, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 05:11:46] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 05:12:37] (step=0088100) Train Loss: 0.1223, Train Steps/Sec: 1.53, Samples/Sec: 54.99
+[2025-06-08 05:13:29] (step=0088200) Train Loss: 0.1180, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 05:14:20] (step=0088300) Train Loss: 0.1233, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 05:15:12] (step=0088400) Train Loss: 0.1258, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 05:16:04] (step=0088500) Train Loss: 0.1156, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 05:16:56] (step=0088600) Train Loss: 0.1147, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 05:17:06] Beginning epoch 6...
+[2025-06-08 05:17:47] (step=0088700) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 05:18:39] (step=0088800) Train Loss: 0.1131, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 05:19:31] (step=0088900) Train Loss: 0.1192, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 05:20:22] (step=0089000) Train Loss: 0.1174, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 05:21:14] (step=0089100) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 05:22:06] (step=0089200) Train Loss: 0.1128, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 05:22:58] (step=0089300) Train Loss: 0.1150, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 05:23:49] (step=0089400) Train Loss: 0.1208, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 05:24:41] (step=0089500) Train Loss: 0.1137, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 05:25:33] (step=0089600) Train Loss: 0.1215, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 05:26:24] (step=0089700) Train Loss: 0.1198, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 05:27:16] (step=0089800) Train Loss: 0.1200, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 05:28:08] (step=0089900) Train Loss: 0.1248, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 05:29:00] (step=0090000) Train Loss: 0.1259, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 05:29:14] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 05:29:16] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 05:31:20] (step=0090000) Perceptual Loss: 0.1751, Eval Time: 126.91
+[2025-06-08 05:32:12] (step=0090100) Train Loss: 0.1099, Train Steps/Sec: 0.52, Samples/Sec: 18.69
+[2025-06-08 05:33:04] (step=0090200) Train Loss: 0.1189, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 05:33:55] (step=0090300) Train Loss: 0.1164, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 05:34:47] (step=0090400) Train Loss: 0.1268, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 05:35:39] (step=0090500) Train Loss: 0.1118, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 05:36:31] (step=0090600) Train Loss: 0.1146, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 05:37:22] (step=0090700) Train Loss: 0.1194, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 05:38:14] (step=0090800) Train Loss: 0.1121, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 05:39:05] (step=0090900) Train Loss: 0.1162, Train Steps/Sec: 1.94, Samples/Sec: 69.81
+[2025-06-08 05:39:57] (step=0091000) Train Loss: 0.1229, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 05:40:48] (step=0091100) Train Loss: 0.1117, Train Steps/Sec: 1.94, Samples/Sec: 70.01
+[2025-06-08 05:41:40] (step=0091200) Train Loss: 0.1177, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 05:42:32] (step=0091300) Train Loss: 0.1166, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 05:43:24] (step=0091400) Train Loss: 0.1170, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 05:44:15] (step=0091500) Train Loss: 0.1186, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 05:45:07] (step=0091600) Train Loss: 0.1178, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 05:45:59] (step=0091700) Train Loss: 0.1172, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 05:46:50] (step=0091800) Train Loss: 0.1171, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 05:47:42] (step=0091900) Train Loss: 0.1185, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 05:48:34] (step=0092000) Train Loss: 0.1174, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 05:48:48] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 05:49:39] (step=0092100) Train Loss: 0.1161, Train Steps/Sec: 1.52, Samples/Sec: 54.80
+[2025-06-08 05:50:31] (step=0092200) Train Loss: 0.1100, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 05:51:23] (step=0092300) Train Loss: 0.1215, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 05:52:15] (step=0092400) Train Loss: 0.1162, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 05:53:06] (step=0092500) Train Loss: 0.1156, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 05:53:58] (step=0092600) Train Loss: 0.1141, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 05:54:50] (step=0092700) Train Loss: 0.1195, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 05:55:41] (step=0092800) Train Loss: 0.1128, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 05:56:33] (step=0092900) Train Loss: 0.1167, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 05:57:25] (step=0093000) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 05:58:16] (step=0093100) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 05:59:08] (step=0093200) Train Loss: 0.1251, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 06:00:00] (step=0093300) Train Loss: 0.1156, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 06:00:51] (step=0093400) Train Loss: 0.1215, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 06:01:43] (step=0093500) Train Loss: 0.1178, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 06:02:35] (step=0093600) Train Loss: 0.1225, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 06:03:27] (step=0093700) Train Loss: 0.1178, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 06:04:18] (step=0093800) Train Loss: 0.1179, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 06:05:10] (step=0093900) Train Loss: 0.1149, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 06:06:02] (step=0094000) Train Loss: 0.1159, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 06:06:15] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 06:07:07] (step=0094100) Train Loss: 0.1150, Train Steps/Sec: 1.53, Samples/Sec: 54.99
+[2025-06-08 06:07:59] (step=0094200) Train Loss: 0.1204, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 06:08:50] (step=0094300) Train Loss: 0.1162, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 06:09:42] (step=0094400) Train Loss: 0.1128, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 06:10:34] (step=0094500) Train Loss: 0.1178, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 06:11:25] (step=0094600) Train Loss: 0.1200, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 06:12:17] (step=0094700) Train Loss: 0.1164, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 06:13:09] (step=0094800) Train Loss: 0.1138, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 06:14:00] (step=0094900) Train Loss: 0.1240, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 06:14:52] (step=0095000) Train Loss: 0.1187, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 06:15:44] (step=0095100) Train Loss: 0.1197, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 06:16:35] (step=0095200) Train Loss: 0.1145, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 06:17:27] (step=0095300) Train Loss: 0.1176, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 06:18:19] (step=0095400) Train Loss: 0.1202, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 06:19:10] (step=0095500) Train Loss: 0.1207, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 06:20:02] (step=0095600) Train Loss: 0.1174, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 06:20:54] (step=0095700) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 06:21:45] (step=0095800) Train Loss: 0.1159, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 06:22:37] (step=0095900) Train Loss: 0.1166, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 06:23:29] (step=0096000) Train Loss: 0.1229, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 06:23:43] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 06:24:34] (step=0096100) Train Loss: 0.1123, Train Steps/Sec: 1.53, Samples/Sec: 55.07
+[2025-06-08 06:25:26] (step=0096200) Train Loss: 0.1146, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 06:26:17] (step=0096300) Train Loss: 0.1195, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 06:27:09] (step=0096400) Train Loss: 0.1196, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 06:28:01] (step=0096500) Train Loss: 0.1144, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 06:28:53] (step=0096600) Train Loss: 0.1117, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 06:29:44] (step=0096700) Train Loss: 0.1155, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 06:30:36] (step=0096800) Train Loss: 0.1156, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 06:31:28] (step=0096900) Train Loss: 0.1129, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 06:32:19] (step=0097000) Train Loss: 0.1219, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 06:33:11] (step=0097100) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 06:34:03] (step=0097200) Train Loss: 0.1221, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 06:34:54] (step=0097300) Train Loss: 0.1174, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 06:35:46] (step=0097400) Train Loss: 0.1252, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 06:36:38] (step=0097500) Train Loss: 0.1162, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 06:37:29] (step=0097600) Train Loss: 0.1229, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 06:38:21] (step=0097700) Train Loss: 0.1142, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 06:39:13] (step=0097800) Train Loss: 0.1227, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 06:40:05] (step=0097900) Train Loss: 0.1175, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 06:40:56] (step=0098000) Train Loss: 0.1235, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 06:41:10] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 06:42:02] (step=0098100) Train Loss: 0.1154, Train Steps/Sec: 1.53, Samples/Sec: 54.98
+[2025-06-08 06:42:53] (step=0098200) Train Loss: 0.1109, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 06:43:45] (step=0098300) Train Loss: 0.1185, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 06:44:37] (step=0098400) Train Loss: 0.1179, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 06:45:28] (step=0098500) Train Loss: 0.1173, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 06:46:20] (step=0098600) Train Loss: 0.1172, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 06:47:12] (step=0098700) Train Loss: 0.1084, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 06:48:04] (step=0098800) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 06:48:55] (step=0098900) Train Loss: 0.1174, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 06:49:47] (step=0099000) Train Loss: 0.1196, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 06:50:39] (step=0099100) Train Loss: 0.1181, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 06:51:30] (step=0099200) Train Loss: 0.1203, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 06:52:22] (step=0099300) Train Loss: 0.1166, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 06:53:14] (step=0099400) Train Loss: 0.1121, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 06:54:05] (step=0099500) Train Loss: 0.1110, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 06:54:57] (step=0099600) Train Loss: 0.1216, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 06:55:49] (step=0099700) Train Loss: 0.1194, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 06:56:40] (step=0099800) Train Loss: 0.1202, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 06:57:32] (step=0099900) Train Loss: 0.1171, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 06:58:24] (step=0100000) Train Loss: 0.1175, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 06:58:50] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0100000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 06:58:53] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 07:00:57] (step=0100000) Perceptual Loss: 0.1667, Eval Time: 127.32
+[2025-06-08 07:01:49] (step=0100100) Train Loss: 0.1135, Train Steps/Sec: 0.49, Samples/Sec: 17.57
+[2025-06-08 07:02:40] (step=0100200) Train Loss: 0.1219, Train Steps/Sec: 1.94, Samples/Sec: 69.80
+[2025-06-08 07:03:32] (step=0100300) Train Loss: 0.1106, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 07:04:24] (step=0100400) Train Loss: 0.1186, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 07:05:16] (step=0100500) Train Loss: 0.1191, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 07:06:07] (step=0100600) Train Loss: 0.1218, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 07:06:59] (step=0100700) Train Loss: 0.1188, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 07:07:50] (step=0100800) Train Loss: 0.1242, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 07:08:42] (step=0100900) Train Loss: 0.1199, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 07:09:34] (step=0101000) Train Loss: 0.1178, Train Steps/Sec: 1.94, Samples/Sec: 69.85
+[2025-06-08 07:10:25] (step=0101100) Train Loss: 0.1184, Train Steps/Sec: 1.94, Samples/Sec: 69.97
+[2025-06-08 07:11:17] (step=0101200) Train Loss: 0.1139, Train Steps/Sec: 1.94, Samples/Sec: 69.82
+[2025-06-08 07:12:08] (step=0101300) Train Loss: 0.1254, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 07:13:00] (step=0101400) Train Loss: 0.1119, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 07:13:52] (step=0101500) Train Loss: 0.1182, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 07:14:43] (step=0101600) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 07:15:35] (step=0101700) Train Loss: 0.1108, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 07:16:27] (step=0101800) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 07:17:19] (step=0101900) Train Loss: 0.1126, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 07:18:11] (step=0102000) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 07:18:24] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 07:19:16] (step=0102100) Train Loss: 0.1141, Train Steps/Sec: 1.53, Samples/Sec: 55.10
+[2025-06-08 07:20:07] (step=0102200) Train Loss: 0.1136, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 07:20:59] (step=0102300) Train Loss: 0.1216, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 07:21:51] (step=0102400) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 07:22:43] (step=0102500) Train Loss: 0.1086, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 07:23:35] (step=0102600) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 07:24:26] (step=0102700) Train Loss: 0.1165, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 07:25:18] (step=0102800) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 07:26:10] (step=0102900) Train Loss: 0.1219, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 07:27:01] (step=0103000) Train Loss: 0.1204, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 07:27:53] (step=0103100) Train Loss: 0.1207, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 07:28:45] (step=0103200) Train Loss: 0.1138, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 07:29:37] (step=0103300) Train Loss: 0.1168, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 07:30:23] Beginning epoch 7...
+[2025-06-08 07:30:29] (step=0103400) Train Loss: 0.1133, Train Steps/Sec: 1.92, Samples/Sec: 69.26
+[2025-06-08 07:31:20] (step=0103500) Train Loss: 0.1147, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 07:32:12] (step=0103600) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 07:33:04] (step=0103700) Train Loss: 0.1154, Train Steps/Sec: 1.92, Samples/Sec: 69.27
+[2025-06-08 07:33:56] (step=0103800) Train Loss: 0.1140, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 07:34:47] (step=0103900) Train Loss: 0.1190, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 07:35:39] (step=0104000) Train Loss: 0.1161, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 07:35:53] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 07:36:44] (step=0104100) Train Loss: 0.1208, Train Steps/Sec: 1.53, Samples/Sec: 55.03
+[2025-06-08 07:37:36] (step=0104200) Train Loss: 0.1142, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 07:38:28] (step=0104300) Train Loss: 0.1197, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 07:39:20] (step=0104400) Train Loss: 0.1157, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 07:40:11] (step=0104500) Train Loss: 0.1130, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 07:41:03] (step=0104600) Train Loss: 0.1155, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 07:41:55] (step=0104700) Train Loss: 0.1157, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 07:42:46] (step=0104800) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 07:43:38] (step=0104900) Train Loss: 0.1093, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 07:44:30] (step=0105000) Train Loss: 0.1177, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 07:45:22] (step=0105100) Train Loss: 0.1138, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 07:46:14] (step=0105200) Train Loss: 0.1233, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 07:47:05] (step=0105300) Train Loss: 0.1156, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 07:47:57] (step=0105400) Train Loss: 0.1291, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 07:48:49] (step=0105500) Train Loss: 0.1161, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 07:49:40] (step=0105600) Train Loss: 0.1259, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 07:50:32] (step=0105700) Train Loss: 0.1149, Train Steps/Sec: 1.92, Samples/Sec: 69.18
+[2025-06-08 07:51:24] (step=0105800) Train Loss: 0.1229, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 07:52:16] (step=0105900) Train Loss: 0.1179, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 07:53:07] (step=0106000) Train Loss: 0.1141, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 07:53:21] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 07:54:13] (step=0106100) Train Loss: 0.1126, Train Steps/Sec: 1.53, Samples/Sec: 54.94
+[2025-06-08 07:55:05] (step=0106200) Train Loss: 0.1082, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 07:55:56] (step=0106300) Train Loss: 0.1207, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 07:56:48] (step=0106400) Train Loss: 0.1155, Train Steps/Sec: 1.92, Samples/Sec: 69.24
+[2025-06-08 07:57:40] (step=0106500) Train Loss: 0.1110, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 07:58:32] (step=0106600) Train Loss: 0.1162, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 07:59:23] (step=0106700) Train Loss: 0.1210, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 08:00:15] (step=0106800) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 08:01:07] (step=0106900) Train Loss: 0.1142, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 08:01:59] (step=0107000) Train Loss: 0.1192, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 08:02:51] (step=0107100) Train Loss: 0.1129, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 08:03:42] (step=0107200) Train Loss: 0.1155, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 08:04:34] (step=0107300) Train Loss: 0.1144, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 08:05:26] (step=0107400) Train Loss: 0.1167, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 08:06:17] (step=0107500) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 08:07:09] (step=0107600) Train Loss: 0.1165, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 08:08:01] (step=0107700) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 08:08:53] (step=0107800) Train Loss: 0.1148, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 08:09:44] (step=0107900) Train Loss: 0.1205, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 08:10:36] (step=0108000) Train Loss: 0.1151, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 08:10:49] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 08:11:41] (step=0108100) Train Loss: 0.1120, Train Steps/Sec: 1.54, Samples/Sec: 55.48
+[2025-06-08 08:12:33] (step=0108200) Train Loss: 0.1167, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 08:13:24] (step=0108300) Train Loss: 0.1123, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 08:14:16] (step=0108400) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 08:15:08] (step=0108500) Train Loss: 0.1208, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 08:16:00] (step=0108600) Train Loss: 0.1165, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 08:16:51] (step=0108700) Train Loss: 0.1110, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 08:17:43] (step=0108800) Train Loss: 0.1195, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 08:18:35] (step=0108900) Train Loss: 0.1160, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 08:19:26] (step=0109000) Train Loss: 0.1163, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 08:20:18] (step=0109100) Train Loss: 0.1063, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-08 08:21:10] (step=0109200) Train Loss: 0.1170, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 08:22:02] (step=0109300) Train Loss: 0.1116, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 08:22:53] (step=0109400) Train Loss: 0.1152, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 08:23:45] (step=0109500) Train Loss: 0.1150, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 08:24:37] (step=0109600) Train Loss: 0.1212, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 08:25:28] (step=0109700) Train Loss: 0.1157, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 08:26:20] (step=0109800) Train Loss: 0.1128, Train Steps/Sec: 1.93, Samples/Sec: 69.31
+[2025-06-08 08:27:12] (step=0109900) Train Loss: 0.1169, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 08:28:04] (step=0110000) Train Loss: 0.1230, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 08:28:18] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 08:28:20] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 08:30:25] (step=0110000) Perceptual Loss: 0.1470, Eval Time: 127.20
+[2025-06-08 08:31:16] (step=0110100) Train Loss: 0.1136, Train Steps/Sec: 0.52, Samples/Sec: 18.69
+[2025-06-08 08:32:08] (step=0110200) Train Loss: 0.1141, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 08:33:00] (step=0110300) Train Loss: 0.1133, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-08 08:33:52] (step=0110400) Train Loss: 0.1195, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 08:34:44] (step=0110500) Train Loss: 0.1132, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 08:35:35] (step=0110600) Train Loss: 0.1147, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 08:36:27] (step=0110700) Train Loss: 0.1109, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 08:37:19] (step=0110800) Train Loss: 0.1155, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 08:38:11] (step=0110900) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 08:39:02] (step=0111000) Train Loss: 0.1142, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 08:39:54] (step=0111100) Train Loss: 0.1091, Train Steps/Sec: 1.95, Samples/Sec: 70.06
+[2025-06-08 08:40:45] (step=0111200) Train Loss: 0.1071, Train Steps/Sec: 1.95, Samples/Sec: 70.07
+[2025-06-08 08:41:37] (step=0111300) Train Loss: 0.1172, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 08:42:28] (step=0111400) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 08:43:20] (step=0111500) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 08:44:12] (step=0111600) Train Loss: 0.1165, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 08:45:04] (step=0111700) Train Loss: 0.1170, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 08:45:55] (step=0111800) Train Loss: 0.1119, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 08:46:47] (step=0111900) Train Loss: 0.1106, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 08:47:39] (step=0112000) Train Loss: 0.1117, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 08:47:52] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 08:48:44] (step=0112100) Train Loss: 0.1148, Train Steps/Sec: 1.53, Samples/Sec: 55.25
+[2025-06-08 08:49:36] (step=0112200) Train Loss: 0.1117, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 08:50:27] (step=0112300) Train Loss: 0.1208, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 08:51:19] (step=0112400) Train Loss: 0.1125, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 08:52:11] (step=0112500) Train Loss: 0.1201, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 08:53:02] (step=0112600) Train Loss: 0.1142, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 08:53:54] (step=0112700) Train Loss: 0.1108, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 08:54:46] (step=0112800) Train Loss: 0.1220, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 08:55:37] (step=0112900) Train Loss: 0.1107, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 08:56:29] (step=0113000) Train Loss: 0.1199, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 08:57:21] (step=0113100) Train Loss: 0.1165, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 08:58:13] (step=0113200) Train Loss: 0.1093, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 08:59:04] (step=0113300) Train Loss: 0.1163, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 08:59:56] (step=0113400) Train Loss: 0.1097, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 09:00:48] (step=0113500) Train Loss: 0.1100, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 09:01:39] (step=0113600) Train Loss: 0.1162, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 09:02:31] (step=0113700) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 09:03:23] (step=0113800) Train Loss: 0.1159, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 09:04:15] (step=0113900) Train Loss: 0.1104, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 09:05:06] (step=0114000) Train Loss: 0.1050, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 09:05:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 09:06:12] (step=0114100) Train Loss: 0.1133, Train Steps/Sec: 1.54, Samples/Sec: 55.26
+[2025-06-08 09:07:03] (step=0114200) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 09:07:55] (step=0114300) Train Loss: 0.1110, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 09:08:47] (step=0114400) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 09:09:38] (step=0114500) Train Loss: 0.1258, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 09:10:30] (step=0114600) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 09:11:22] (step=0114700) Train Loss: 0.1162, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 09:12:14] (step=0114800) Train Loss: 0.1154, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 09:13:05] (step=0114900) Train Loss: 0.1212, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 09:13:57] (step=0115000) Train Loss: 0.1178, Train Steps/Sec: 1.93, Samples/Sec: 69.31
+[2025-06-08 09:14:49] (step=0115100) Train Loss: 0.1126, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 09:15:41] (step=0115200) Train Loss: 0.1175, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 09:16:32] (step=0115300) Train Loss: 0.1162, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 09:17:24] (step=0115400) Train Loss: 0.1081, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 09:18:16] (step=0115500) Train Loss: 0.1121, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 09:19:07] (step=0115600) Train Loss: 0.1188, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 09:19:59] (step=0115700) Train Loss: 0.1254, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 09:20:51] (step=0115800) Train Loss: 0.1180, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 09:21:42] (step=0115900) Train Loss: 0.1125, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 09:22:34] (step=0116000) Train Loss: 0.1135, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 09:22:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 09:23:39] (step=0116100) Train Loss: 0.1144, Train Steps/Sec: 1.54, Samples/Sec: 55.33
+[2025-06-08 09:24:31] (step=0116200) Train Loss: 0.1113, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 09:25:22] (step=0116300) Train Loss: 0.1124, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 09:26:14] (step=0116400) Train Loss: 0.1160, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 09:27:06] (step=0116500) Train Loss: 0.1223, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 09:27:58] (step=0116600) Train Loss: 0.1122, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 09:28:49] (step=0116700) Train Loss: 0.1096, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 09:29:41] (step=0116800) Train Loss: 0.1075, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 09:30:33] (step=0116900) Train Loss: 0.1208, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 09:31:25] (step=0117000) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 09:32:16] (step=0117100) Train Loss: 0.1122, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 09:33:08] (step=0117200) Train Loss: 0.1134, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 09:34:00] (step=0117300) Train Loss: 0.1085, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 09:34:51] (step=0117400) Train Loss: 0.1125, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 09:35:43] (step=0117500) Train Loss: 0.1203, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 09:36:35] (step=0117600) Train Loss: 0.1213, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 09:37:27] (step=0117700) Train Loss: 0.1180, Train Steps/Sec: 1.93, Samples/Sec: 69.33
+[2025-06-08 09:38:19] (step=0117800) Train Loss: 0.1109, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 09:39:10] (step=0117900) Train Loss: 0.1159, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 09:40:02] (step=0118000) Train Loss: 0.1144, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 09:40:15] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 09:41:07] (step=0118100) Train Loss: 0.1236, Train Steps/Sec: 1.54, Samples/Sec: 55.30
+[2025-06-08 09:41:38] Beginning epoch 8...
+[2025-06-08 09:41:59] (step=0118200) Train Loss: 0.1128, Train Steps/Sec: 1.92, Samples/Sec: 69.29
+[2025-06-08 09:42:51] (step=0118300) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 09:43:42] (step=0118400) Train Loss: 0.1199, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 09:44:34] (step=0118500) Train Loss: 0.1133, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 09:45:26] (step=0118600) Train Loss: 0.1127, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 09:46:17] (step=0118700) Train Loss: 0.1157, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 09:47:09] (step=0118800) Train Loss: 0.1232, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 09:48:01] (step=0118900) Train Loss: 0.1218, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 09:48:52] (step=0119000) Train Loss: 0.1133, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 09:49:44] (step=0119100) Train Loss: 0.1220, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 09:50:36] (step=0119200) Train Loss: 0.1204, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 09:51:28] (step=0119300) Train Loss: 0.1114, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 09:52:19] (step=0119400) Train Loss: 0.1159, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 09:53:11] (step=0119500) Train Loss: 0.1161, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 09:54:02] (step=0119600) Train Loss: 0.1177, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 09:54:54] (step=0119700) Train Loss: 0.1252, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 09:55:46] (step=0119800) Train Loss: 0.1159, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 09:56:37] (step=0119900) Train Loss: 0.1161, Train Steps/Sec: 1.94, Samples/Sec: 69.77
+[2025-06-08 09:57:29] (step=0120000) Train Loss: 0.1129, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 09:57:55] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0120000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 09:57:58] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 10:00:02] (step=0120000) Perceptual Loss: 0.1635, Eval Time: 127.19
+[2025-06-08 10:00:54] (step=0120100) Train Loss: 0.1160, Train Steps/Sec: 0.49, Samples/Sec: 17.60
+[2025-06-08 10:01:45] (step=0120200) Train Loss: 0.1180, Train Steps/Sec: 1.94, Samples/Sec: 69.85
+[2025-06-08 10:02:37] (step=0120300) Train Loss: 0.1125, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 10:03:29] (step=0120400) Train Loss: 0.1172, Train Steps/Sec: 1.94, Samples/Sec: 69.83
+[2025-06-08 10:04:20] (step=0120500) Train Loss: 0.1109, Train Steps/Sec: 1.94, Samples/Sec: 69.82
+[2025-06-08 10:05:12] (step=0120600) Train Loss: 0.1102, Train Steps/Sec: 1.94, Samples/Sec: 69.80
+[2025-06-08 10:06:03] (step=0120700) Train Loss: 0.1103, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 10:06:55] (step=0120800) Train Loss: 0.1217, Train Steps/Sec: 1.94, Samples/Sec: 69.80
+[2025-06-08 10:07:47] (step=0120900) Train Loss: 0.1167, Train Steps/Sec: 1.94, Samples/Sec: 69.78
+[2025-06-08 10:08:38] (step=0121000) Train Loss: 0.1154, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 10:09:30] (step=0121100) Train Loss: 0.1168, Train Steps/Sec: 1.95, Samples/Sec: 70.04
+[2025-06-08 10:10:21] (step=0121200) Train Loss: 0.1114, Train Steps/Sec: 1.95, Samples/Sec: 70.07
+[2025-06-08 10:11:13] (step=0121300) Train Loss: 0.1142, Train Steps/Sec: 1.94, Samples/Sec: 69.84
+[2025-06-08 10:12:04] (step=0121400) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 10:12:56] (step=0121500) Train Loss: 0.1121, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 10:13:48] (step=0121600) Train Loss: 0.1154, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 10:14:40] (step=0121700) Train Loss: 0.1121, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 10:15:31] (step=0121800) Train Loss: 0.1172, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 10:16:23] (step=0121900) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 10:17:15] (step=0122000) Train Loss: 0.1190, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 10:17:29] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 10:18:20] (step=0122100) Train Loss: 0.1084, Train Steps/Sec: 1.53, Samples/Sec: 55.13
+[2025-06-08 10:19:12] (step=0122200) Train Loss: 0.1116, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 10:20:04] (step=0122300) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 10:20:55] (step=0122400) Train Loss: 0.1129, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 10:21:47] (step=0122500) Train Loss: 0.1141, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 10:22:39] (step=0122600) Train Loss: 0.1133, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 10:23:31] (step=0122700) Train Loss: 0.1145, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 10:24:22] (step=0122800) Train Loss: 0.1119, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 10:25:14] (step=0122900) Train Loss: 0.1157, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 10:26:06] (step=0123000) Train Loss: 0.1161, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 10:26:57] (step=0123100) Train Loss: 0.1125, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 10:27:49] (step=0123200) Train Loss: 0.1147, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 10:28:41] (step=0123300) Train Loss: 0.1141, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 10:29:33] (step=0123400) Train Loss: 0.1081, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 10:30:24] (step=0123500) Train Loss: 0.1199, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 10:31:16] (step=0123600) Train Loss: 0.1140, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 10:32:08] (step=0123700) Train Loss: 0.1218, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 10:33:00] (step=0123800) Train Loss: 0.1068, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 10:33:51] (step=0123900) Train Loss: 0.1178, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 10:34:43] (step=0124000) Train Loss: 0.1128, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 10:34:56] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 10:35:48] (step=0124100) Train Loss: 0.1129, Train Steps/Sec: 1.54, Samples/Sec: 55.45
+[2025-06-08 10:36:40] (step=0124200) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 10:37:32] (step=0124300) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 10:38:23] (step=0124400) Train Loss: 0.1145, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 10:39:15] (step=0124500) Train Loss: 0.1177, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 10:40:07] (step=0124600) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 10:40:59] (step=0124700) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 10:41:51] (step=0124800) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 10:42:42] (step=0124900) Train Loss: 0.1183, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 10:43:34] (step=0125000) Train Loss: 0.1094, Train Steps/Sec: 1.92, Samples/Sec: 69.24
+[2025-06-08 10:44:26] (step=0125100) Train Loss: 0.1168, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 10:45:18] (step=0125200) Train Loss: 0.1136, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 10:46:10] (step=0125300) Train Loss: 0.1135, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 10:47:01] (step=0125400) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 10:47:53] (step=0125500) Train Loss: 0.1081, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 10:48:45] (step=0125600) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 10:49:37] (step=0125700) Train Loss: 0.1185, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 10:50:28] (step=0125800) Train Loss: 0.1109, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 10:51:20] (step=0125900) Train Loss: 0.1089, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 10:52:12] (step=0126000) Train Loss: 0.1131, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 10:52:26] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 10:53:17] (step=0126100) Train Loss: 0.1134, Train Steps/Sec: 1.53, Samples/Sec: 54.99
+[2025-06-08 10:54:09] (step=0126200) Train Loss: 0.1140, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 10:55:01] (step=0126300) Train Loss: 0.1182, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 10:55:53] (step=0126400) Train Loss: 0.1203, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 10:56:45] (step=0126500) Train Loss: 0.1176, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 10:57:36] (step=0126600) Train Loss: 0.1123, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 10:58:28] (step=0126700) Train Loss: 0.1094, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 10:59:20] (step=0126800) Train Loss: 0.1068, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 11:00:11] (step=0126900) Train Loss: 0.1156, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 11:01:03] (step=0127000) Train Loss: 0.1125, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 11:01:55] (step=0127100) Train Loss: 0.1141, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 11:02:47] (step=0127200) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 11:03:38] (step=0127300) Train Loss: 0.1140, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 11:04:30] (step=0127400) Train Loss: 0.1101, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 11:05:22] (step=0127500) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 11:06:13] (step=0127600) Train Loss: 0.1028, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 11:07:05] (step=0127700) Train Loss: 0.1128, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 11:07:57] (step=0127800) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 11:08:49] (step=0127900) Train Loss: 0.1154, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 11:09:41] (step=0128000) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 11:09:54] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 11:10:46] (step=0128100) Train Loss: 0.1095, Train Steps/Sec: 1.53, Samples/Sec: 55.23
+[2025-06-08 11:11:37] (step=0128200) Train Loss: 0.1129, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 11:12:29] (step=0128300) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 11:13:21] (step=0128400) Train Loss: 0.1087, Train Steps/Sec: 1.92, Samples/Sec: 69.24
+[2025-06-08 11:14:13] (step=0128500) Train Loss: 0.1088, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 11:15:05] (step=0128600) Train Loss: 0.1148, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 11:15:56] (step=0128700) Train Loss: 0.1148, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 11:16:48] (step=0128800) Train Loss: 0.1164, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 11:17:40] (step=0128900) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 11:18:31] (step=0129000) Train Loss: 0.1154, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 11:19:24] (step=0129100) Train Loss: 0.1126, Train Steps/Sec: 1.92, Samples/Sec: 69.10
+[2025-06-08 11:20:15] (step=0129200) Train Loss: 0.1141, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 11:21:07] (step=0129300) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 11:21:59] (step=0129400) Train Loss: 0.1103, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 11:22:50] (step=0129500) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 11:23:42] (step=0129600) Train Loss: 0.1140, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 11:24:34] (step=0129700) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 11:25:26] (step=0129800) Train Loss: 0.1152, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 11:26:17] (step=0129900) Train Loss: 0.1132, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 11:27:09] (step=0130000) Train Loss: 0.1164, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 11:27:23] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 11:27:25] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 11:29:30] (step=0130000) Perceptual Loss: 0.1506, Eval Time: 127.50
+[2025-06-08 11:30:22] (step=0130100) Train Loss: 0.1076, Train Steps/Sec: 0.52, Samples/Sec: 18.70
+[2025-06-08 11:31:13] (step=0130200) Train Loss: 0.1096, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 11:32:05] (step=0130300) Train Loss: 0.1135, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-08 11:32:57] (step=0130400) Train Loss: 0.1156, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 11:33:49] (step=0130500) Train Loss: 0.1079, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 11:34:40] (step=0130600) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 11:35:32] (step=0130700) Train Loss: 0.1159, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 11:36:24] (step=0130800) Train Loss: 0.1153, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 11:37:15] (step=0130900) Train Loss: 0.1168, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 11:38:07] (step=0131000) Train Loss: 0.1193, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 11:38:59] (step=0131100) Train Loss: 0.1112, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 11:39:50] (step=0131200) Train Loss: 0.1201, Train Steps/Sec: 1.95, Samples/Sec: 70.02
+[2025-06-08 11:40:42] (step=0131300) Train Loss: 0.1141, Train Steps/Sec: 1.95, Samples/Sec: 70.03
+[2025-06-08 11:41:33] (step=0131400) Train Loss: 0.1119, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 11:42:25] (step=0131500) Train Loss: 0.1085, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 11:43:17] (step=0131600) Train Loss: 0.1153, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 11:44:09] (step=0131700) Train Loss: 0.1157, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 11:45:00] (step=0131800) Train Loss: 0.1219, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 11:45:52] (step=0131900) Train Loss: 0.1078, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 11:46:44] (step=0132000) Train Loss: 0.1066, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 11:46:57] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 11:47:48] (step=0132100) Train Loss: 0.1113, Train Steps/Sec: 1.55, Samples/Sec: 55.71
+[2025-06-08 11:48:40] (step=0132200) Train Loss: 0.1209, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 11:49:32] (step=0132300) Train Loss: 0.1088, Train Steps/Sec: 1.92, Samples/Sec: 69.08
+[2025-06-08 11:50:24] (step=0132400) Train Loss: 0.1087, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 11:51:16] (step=0132500) Train Loss: 0.1098, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 11:52:07] (step=0132600) Train Loss: 0.1128, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 11:52:59] (step=0132700) Train Loss: 0.1210, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 11:53:51] (step=0132800) Train Loss: 0.1207, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 11:54:42] (step=0132900) Train Loss: 0.1128, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 11:54:58] Beginning epoch 9...
+[2025-06-08 11:55:35] (step=0133000) Train Loss: 0.1153, Train Steps/Sec: 1.91, Samples/Sec: 68.77
+[2025-06-08 11:56:26] (step=0133100) Train Loss: 0.1150, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 11:57:18] (step=0133200) Train Loss: 0.1115, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 11:58:10] (step=0133300) Train Loss: 0.1135, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 11:59:01] (step=0133400) Train Loss: 0.1086, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 11:59:53] (step=0133500) Train Loss: 0.1131, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 12:00:45] (step=0133600) Train Loss: 0.1250, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 12:01:37] (step=0133700) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 12:02:28] (step=0133800) Train Loss: 0.1155, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 12:03:20] (step=0133900) Train Loss: 0.1145, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 12:04:12] (step=0134000) Train Loss: 0.1136, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 12:04:25] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 12:05:17] (step=0134100) Train Loss: 0.1162, Train Steps/Sec: 1.53, Samples/Sec: 55.23
+[2025-06-08 12:06:09] (step=0134200) Train Loss: 0.1114, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 12:07:01] (step=0134300) Train Loss: 0.1120, Train Steps/Sec: 1.92, Samples/Sec: 69.27
+[2025-06-08 12:07:52] (step=0134400) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 12:08:44] (step=0134500) Train Loss: 0.1118, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 12:09:36] (step=0134600) Train Loss: 0.1092, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 12:10:27] (step=0134700) Train Loss: 0.1118, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 12:11:19] (step=0134800) Train Loss: 0.1209, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 12:12:11] (step=0134900) Train Loss: 0.1225, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 12:13:03] (step=0135000) Train Loss: 0.1108, Train Steps/Sec: 1.92, Samples/Sec: 69.18
+[2025-06-08 12:13:54] (step=0135100) Train Loss: 0.1122, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 12:14:46] (step=0135200) Train Loss: 0.1146, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 12:15:38] (step=0135300) Train Loss: 0.1081, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 12:16:29] (step=0135400) Train Loss: 0.1101, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 12:17:21] (step=0135500) Train Loss: 0.1131, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 12:18:13] (step=0135600) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 12:19:05] (step=0135700) Train Loss: 0.1110, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 12:19:56] (step=0135800) Train Loss: 0.1133, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 12:20:48] (step=0135900) Train Loss: 0.1133, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 12:21:40] (step=0136000) Train Loss: 0.1171, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 12:21:53] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 12:22:45] (step=0136100) Train Loss: 0.1079, Train Steps/Sec: 1.53, Samples/Sec: 55.12
+[2025-06-08 12:23:37] (step=0136200) Train Loss: 0.1150, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 12:24:29] (step=0136300) Train Loss: 0.1196, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 12:25:21] (step=0136400) Train Loss: 0.1139, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 12:26:12] (step=0136500) Train Loss: 0.1181, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 12:27:04] (step=0136600) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 12:27:56] (step=0136700) Train Loss: 0.1113, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 12:28:47] (step=0136800) Train Loss: 0.1116, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 12:29:39] (step=0136900) Train Loss: 0.1194, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 12:30:31] (step=0137000) Train Loss: 0.1110, Train Steps/Sec: 1.92, Samples/Sec: 69.28
+[2025-06-08 12:31:23] (step=0137100) Train Loss: 0.1202, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 12:32:15] (step=0137200) Train Loss: 0.1127, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 12:33:06] (step=0137300) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 12:33:58] (step=0137400) Train Loss: 0.1113, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 12:34:50] (step=0137500) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 12:35:41] (step=0137600) Train Loss: 0.1149, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 12:36:33] (step=0137700) Train Loss: 0.1151, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 12:37:25] (step=0137800) Train Loss: 0.1137, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 12:38:17] (step=0137900) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 12:39:08] (step=0138000) Train Loss: 0.1149, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 12:39:21] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 12:40:13] (step=0138100) Train Loss: 0.1121, Train Steps/Sec: 1.55, Samples/Sec: 55.71
+[2025-06-08 12:41:05] (step=0138200) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 12:41:57] (step=0138300) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 12:42:49] (step=0138400) Train Loss: 0.1044, Train Steps/Sec: 1.92, Samples/Sec: 69.24
+[2025-06-08 12:43:40] (step=0138500) Train Loss: 0.1091, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 12:44:32] (step=0138600) Train Loss: 0.1163, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 12:45:24] (step=0138700) Train Loss: 0.1143, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 12:46:15] (step=0138800) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 12:47:07] (step=0138900) Train Loss: 0.1121, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 12:47:59] (step=0139000) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 12:48:51] (step=0139100) Train Loss: 0.1155, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 12:49:42] (step=0139200) Train Loss: 0.1163, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 12:50:34] (step=0139300) Train Loss: 0.1111, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 12:51:26] (step=0139400) Train Loss: 0.1062, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 12:52:17] (step=0139500) Train Loss: 0.1128, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 12:53:09] (step=0139600) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 12:54:01] (step=0139700) Train Loss: 0.1102, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 12:54:53] (step=0139800) Train Loss: 0.1159, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 12:55:44] (step=0139900) Train Loss: 0.1167, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 12:56:36] (step=0140000) Train Loss: 0.1152, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 12:57:02] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0140000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 12:57:04] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 12:59:09] (step=0140000) Perceptual Loss: 0.1457, Eval Time: 127.41
+[2025-06-08 13:00:01] (step=0140100) Train Loss: 0.1266, Train Steps/Sec: 0.49, Samples/Sec: 17.59
+[2025-06-08 13:00:52] (step=0140200) Train Loss: 0.1126, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 13:01:44] (step=0140300) Train Loss: 0.1093, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 13:02:36] (step=0140400) Train Loss: 0.1167, Train Steps/Sec: 1.94, Samples/Sec: 69.78
+[2025-06-08 13:03:27] (step=0140500) Train Loss: 0.1127, Train Steps/Sec: 1.94, Samples/Sec: 69.80
+[2025-06-08 13:04:19] (step=0140600) Train Loss: 0.1102, Train Steps/Sec: 1.94, Samples/Sec: 69.78
+[2025-06-08 13:05:11] (step=0140700) Train Loss: 0.1082, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 13:06:02] (step=0140800) Train Loss: 0.1111, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 13:06:54] (step=0140900) Train Loss: 0.1150, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 13:07:46] (step=0141000) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 13:08:37] (step=0141100) Train Loss: 0.1126, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 13:09:29] (step=0141200) Train Loss: 0.1062, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 13:10:20] (step=0141300) Train Loss: 0.1057, Train Steps/Sec: 1.95, Samples/Sec: 70.04
+[2025-06-08 13:11:12] (step=0141400) Train Loss: 0.1107, Train Steps/Sec: 1.94, Samples/Sec: 69.92
+[2025-06-08 13:12:03] (step=0141500) Train Loss: 0.1155, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 13:12:55] (step=0141600) Train Loss: 0.1128, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-08 13:13:47] (step=0141700) Train Loss: 0.1122, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 13:14:39] (step=0141800) Train Loss: 0.1172, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 13:15:30] (step=0141900) Train Loss: 0.1106, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 13:16:22] (step=0142000) Train Loss: 0.1165, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 13:16:36] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 13:17:27] (step=0142100) Train Loss: 0.1137, Train Steps/Sec: 1.53, Samples/Sec: 55.10
+[2025-06-08 13:18:19] (step=0142200) Train Loss: 0.1107, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 13:19:11] (step=0142300) Train Loss: 0.1137, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 13:20:03] (step=0142400) Train Loss: 0.1156, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 13:20:54] (step=0142500) Train Loss: 0.1102, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 13:21:46] (step=0142600) Train Loss: 0.1153, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 13:22:38] (step=0142700) Train Loss: 0.1217, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 13:23:30] (step=0142800) Train Loss: 0.1076, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 13:24:21] (step=0142900) Train Loss: 0.1140, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 13:25:13] (step=0143000) Train Loss: 0.1101, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 13:26:05] (step=0143100) Train Loss: 0.1136, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 13:26:56] (step=0143200) Train Loss: 0.1135, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 13:27:48] (step=0143300) Train Loss: 0.1119, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 13:28:40] (step=0143400) Train Loss: 0.1137, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 13:29:31] (step=0143500) Train Loss: 0.1159, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 13:30:23] (step=0143600) Train Loss: 0.1086, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 13:31:15] (step=0143700) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 13:32:07] (step=0143800) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 13:32:58] (step=0143900) Train Loss: 0.1056, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 13:33:50] (step=0144000) Train Loss: 0.1154, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 13:34:03] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 13:34:55] (step=0144100) Train Loss: 0.1156, Train Steps/Sec: 1.55, Samples/Sec: 55.70
+[2025-06-08 13:35:46] (step=0144200) Train Loss: 0.1168, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 13:36:38] (step=0144300) Train Loss: 0.1171, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 13:37:30] (step=0144400) Train Loss: 0.1155, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 13:38:22] (step=0144500) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 13:39:13] (step=0144600) Train Loss: 0.1171, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 13:40:05] (step=0144700) Train Loss: 0.1108, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 13:40:57] (step=0144800) Train Loss: 0.1106, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 13:41:49] (step=0144900) Train Loss: 0.1115, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 13:42:40] (step=0145000) Train Loss: 0.1123, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-08 13:43:32] (step=0145100) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 13:44:24] (step=0145200) Train Loss: 0.1076, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 13:45:15] (step=0145300) Train Loss: 0.1121, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 13:46:07] (step=0145400) Train Loss: 0.1087, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 13:46:59] (step=0145500) Train Loss: 0.1069, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 13:47:51] (step=0145600) Train Loss: 0.1178, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 13:48:43] (step=0145700) Train Loss: 0.1091, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 13:49:34] (step=0145800) Train Loss: 0.1164, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 13:50:26] (step=0145900) Train Loss: 0.1130, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 13:51:18] (step=0146000) Train Loss: 0.1110, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 13:51:31] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 13:52:22] (step=0146100) Train Loss: 0.1074, Train Steps/Sec: 1.55, Samples/Sec: 55.84
+[2025-06-08 13:53:14] (step=0146200) Train Loss: 0.1105, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 13:54:06] (step=0146300) Train Loss: 0.1068, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 13:54:57] (step=0146400) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 13:55:49] (step=0146500) Train Loss: 0.1117, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 13:56:41] (step=0146600) Train Loss: 0.1100, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 13:57:32] (step=0146700) Train Loss: 0.1192, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 13:58:24] (step=0146800) Train Loss: 0.1071, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 13:59:16] (step=0146900) Train Loss: 0.1138, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 14:00:08] (step=0147000) Train Loss: 0.1136, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 14:00:59] (step=0147100) Train Loss: 0.1098, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 14:01:51] (step=0147200) Train Loss: 0.1153, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 14:02:43] (step=0147300) Train Loss: 0.1180, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 14:03:34] (step=0147400) Train Loss: 0.1108, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 14:04:26] (step=0147500) Train Loss: 0.1158, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 14:05:18] (step=0147600) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 14:06:10] (step=0147700) Train Loss: 0.1084, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 14:06:10] Beginning epoch 10...
+[2025-06-08 14:07:02] (step=0147800) Train Loss: 0.1163, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 14:07:53] (step=0147900) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 14:08:45] (step=0148000) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 14:08:58] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 14:09:50] (step=0148100) Train Loss: 0.1136, Train Steps/Sec: 1.55, Samples/Sec: 55.63
+[2025-06-08 14:10:41] (step=0148200) Train Loss: 0.1080, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 14:11:33] (step=0148300) Train Loss: 0.1155, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 14:12:25] (step=0148400) Train Loss: 0.1168, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 14:13:17] (step=0148500) Train Loss: 0.1124, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 14:14:09] (step=0148600) Train Loss: 0.1188, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 14:15:00] (step=0148700) Train Loss: 0.1093, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 14:15:52] (step=0148800) Train Loss: 0.1224, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 14:16:44] (step=0148900) Train Loss: 0.1151, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 14:17:36] (step=0149000) Train Loss: 0.1115, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 14:18:27] (step=0149100) Train Loss: 0.1069, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 14:19:19] (step=0149200) Train Loss: 0.1131, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 14:20:11] (step=0149300) Train Loss: 0.1190, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 14:21:02] (step=0149400) Train Loss: 0.1123, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 14:21:54] (step=0149500) Train Loss: 0.1120, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 14:22:46] (step=0149600) Train Loss: 0.1101, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 14:23:38] (step=0149700) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 14:24:29] (step=0149800) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 14:25:21] (step=0149900) Train Loss: 0.1116, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 14:26:13] (step=0150000) Train Loss: 0.1172, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 14:26:26] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 14:26:29] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 14:28:33] (step=0150000) Perceptual Loss: 0.1516, Eval Time: 127.13
+[2025-06-08 14:29:25] (step=0150100) Train Loss: 0.1128, Train Steps/Sec: 0.52, Samples/Sec: 18.78
+[2025-06-08 14:30:16] (step=0150200) Train Loss: 0.1182, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 14:31:08] (step=0150300) Train Loss: 0.1152, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 14:32:00] (step=0150400) Train Loss: 0.1089, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 14:32:52] (step=0150500) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 14:33:43] (step=0150600) Train Loss: 0.1170, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 14:34:35] (step=0150700) Train Loss: 0.1083, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 14:35:27] (step=0150800) Train Loss: 0.1133, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 14:36:19] (step=0150900) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 14:37:10] (step=0151000) Train Loss: 0.1141, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 14:38:02] (step=0151100) Train Loss: 0.1092, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 14:38:54] (step=0151200) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 14:39:45] (step=0151300) Train Loss: 0.1130, Train Steps/Sec: 1.94, Samples/Sec: 69.81
+[2025-06-08 14:40:37] (step=0151400) Train Loss: 0.1074, Train Steps/Sec: 1.95, Samples/Sec: 70.05
+[2025-06-08 14:41:28] (step=0151500) Train Loss: 0.1106, Train Steps/Sec: 1.95, Samples/Sec: 70.03
+[2025-06-08 14:42:20] (step=0151600) Train Loss: 0.1096, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-08 14:43:12] (step=0151700) Train Loss: 0.1049, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 14:44:03] (step=0151800) Train Loss: 0.1124, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 14:44:55] (step=0151900) Train Loss: 0.1086, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 14:45:47] (step=0152000) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 14:46:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 14:46:52] (step=0152100) Train Loss: 0.1045, Train Steps/Sec: 1.53, Samples/Sec: 55.20
+[2025-06-08 14:47:44] (step=0152200) Train Loss: 0.1103, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 14:48:36] (step=0152300) Train Loss: 0.1161, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 14:49:27] (step=0152400) Train Loss: 0.1197, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 14:50:19] (step=0152500) Train Loss: 0.1167, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 14:51:11] (step=0152600) Train Loss: 0.1102, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 14:52:03] (step=0152700) Train Loss: 0.1136, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 14:52:54] (step=0152800) Train Loss: 0.1211, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 14:53:46] (step=0152900) Train Loss: 0.1164, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 14:54:38] (step=0153000) Train Loss: 0.1064, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 14:55:30] (step=0153100) Train Loss: 0.1083, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 14:56:21] (step=0153200) Train Loss: 0.1103, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 14:57:13] (step=0153300) Train Loss: 0.1068, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 14:58:05] (step=0153400) Train Loss: 0.1135, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 14:58:56] (step=0153500) Train Loss: 0.1185, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 14:59:48] (step=0153600) Train Loss: 0.1135, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 15:00:40] (step=0153700) Train Loss: 0.1090, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 15:01:32] (step=0153800) Train Loss: 0.1142, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 15:02:24] (step=0153900) Train Loss: 0.1183, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 15:03:15] (step=0154000) Train Loss: 0.1114, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 15:03:28] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 15:04:20] (step=0154100) Train Loss: 0.1128, Train Steps/Sec: 1.55, Samples/Sec: 55.75
+[2025-06-08 15:05:12] (step=0154200) Train Loss: 0.1085, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 15:06:03] (step=0154300) Train Loss: 0.1153, Train Steps/Sec: 1.93, Samples/Sec: 69.30
+[2025-06-08 15:06:55] (step=0154400) Train Loss: 0.1150, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 15:07:47] (step=0154500) Train Loss: 0.1131, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 15:08:39] (step=0154600) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 15:09:30] (step=0154700) Train Loss: 0.1089, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 15:10:22] (step=0154800) Train Loss: 0.1089, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 15:11:14] (step=0154900) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 15:12:05] (step=0155000) Train Loss: 0.1080, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 15:12:57] (step=0155100) Train Loss: 0.1087, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 15:13:49] (step=0155200) Train Loss: 0.1106, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 15:14:41] (step=0155300) Train Loss: 0.1122, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 15:15:32] (step=0155400) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 15:16:24] (step=0155500) Train Loss: 0.1159, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 15:17:16] (step=0155600) Train Loss: 0.1135, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 15:18:07] (step=0155700) Train Loss: 0.1112, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 15:18:59] (step=0155800) Train Loss: 0.1170, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 15:19:51] (step=0155900) Train Loss: 0.1137, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 15:20:43] (step=0156000) Train Loss: 0.1083, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 15:20:56] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 15:21:48] (step=0156100) Train Loss: 0.1108, Train Steps/Sec: 1.54, Samples/Sec: 55.55
+[2025-06-08 15:22:39] (step=0156200) Train Loss: 0.1174, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 15:23:31] (step=0156300) Train Loss: 0.1082, Train Steps/Sec: 1.93, Samples/Sec: 69.32
+[2025-06-08 15:24:23] (step=0156400) Train Loss: 0.1167, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 15:25:15] (step=0156500) Train Loss: 0.1166, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 15:26:06] (step=0156600) Train Loss: 0.1113, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 15:26:58] (step=0156700) Train Loss: 0.1079, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 15:27:50] (step=0156800) Train Loss: 0.1168, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 15:28:41] (step=0156900) Train Loss: 0.1125, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 15:29:33] (step=0157000) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 15:30:25] (step=0157100) Train Loss: 0.1110, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 15:31:17] (step=0157200) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 15:32:08] (step=0157300) Train Loss: 0.1098, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 15:33:00] (step=0157400) Train Loss: 0.1138, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 15:33:52] (step=0157500) Train Loss: 0.1033, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 15:34:44] (step=0157600) Train Loss: 0.1094, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 15:35:35] (step=0157700) Train Loss: 0.1101, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 15:36:27] (step=0157800) Train Loss: 0.1138, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 15:37:19] (step=0157900) Train Loss: 0.1076, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 15:38:11] (step=0158000) Train Loss: 0.1087, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 15:38:24] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 15:39:16] (step=0158100) Train Loss: 0.1088, Train Steps/Sec: 1.54, Samples/Sec: 55.28
+[2025-06-08 15:40:07] (step=0158200) Train Loss: 0.1140, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 15:40:59] (step=0158300) Train Loss: 0.1137, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 15:41:51] (step=0158400) Train Loss: 0.1077, Train Steps/Sec: 1.92, Samples/Sec: 69.26
+[2025-06-08 15:42:43] (step=0158500) Train Loss: 0.1125, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 15:43:34] (step=0158600) Train Loss: 0.1056, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 15:44:26] (step=0158700) Train Loss: 0.1034, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 15:45:18] (step=0158800) Train Loss: 0.1074, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 15:46:10] (step=0158900) Train Loss: 0.1089, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 15:47:02] (step=0159000) Train Loss: 0.1192, Train Steps/Sec: 1.92, Samples/Sec: 69.26
+[2025-06-08 15:47:53] (step=0159100) Train Loss: 0.1110, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 15:48:45] (step=0159200) Train Loss: 0.1067, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 15:49:37] (step=0159300) Train Loss: 0.1053, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 15:50:28] (step=0159400) Train Loss: 0.1154, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 15:51:20] (step=0159500) Train Loss: 0.1144, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 15:52:12] (step=0159600) Train Loss: 0.1108, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 15:53:04] (step=0159700) Train Loss: 0.1168, Train Steps/Sec: 1.92, Samples/Sec: 69.23
+[2025-06-08 15:53:56] (step=0159800) Train Loss: 0.1106, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 15:54:47] (step=0159900) Train Loss: 0.1098, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 15:55:39] (step=0160000) Train Loss: 0.1098, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 15:56:05] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0160000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 15:56:07] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 15:58:12] (step=0160000) Perceptual Loss: 0.1448, Eval Time: 127.71
+[2025-06-08 15:59:04] (step=0160100) Train Loss: 0.1140, Train Steps/Sec: 0.49, Samples/Sec: 17.56
+[2025-06-08 15:59:56] (step=0160200) Train Loss: 0.1075, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 16:00:48] (step=0160300) Train Loss: 0.1112, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 16:01:39] (step=0160400) Train Loss: 0.1126, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 16:02:31] (step=0160500) Train Loss: 0.1153, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 16:03:22] (step=0160600) Train Loss: 0.1137, Train Steps/Sec: 1.94, Samples/Sec: 69.75
+[2025-06-08 16:04:14] (step=0160700) Train Loss: 0.1134, Train Steps/Sec: 1.95, Samples/Sec: 70.07
+[2025-06-08 16:05:05] (step=0160800) Train Loss: 0.1089, Train Steps/Sec: 1.95, Samples/Sec: 70.07
+[2025-06-08 16:05:57] (step=0160900) Train Loss: 0.1073, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 16:06:49] (step=0161000) Train Loss: 0.1117, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 16:07:41] (step=0161100) Train Loss: 0.1182, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 16:08:32] (step=0161200) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 16:09:24] (step=0161300) Train Loss: 0.1051, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 16:10:16] (step=0161400) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 16:11:07] (step=0161500) Train Loss: 0.1148, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 16:11:59] (step=0161600) Train Loss: 0.1081, Train Steps/Sec: 1.92, Samples/Sec: 69.17
+[2025-06-08 16:12:51] (step=0161700) Train Loss: 0.1127, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 16:13:43] (step=0161800) Train Loss: 0.1056, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 16:14:34] (step=0161900) Train Loss: 0.1114, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 16:15:26] (step=0162000) Train Loss: 0.1071, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 16:15:39] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 16:16:31] (step=0162100) Train Loss: 0.1169, Train Steps/Sec: 1.55, Samples/Sec: 55.67
+[2025-06-08 16:17:23] (step=0162200) Train Loss: 0.1146, Train Steps/Sec: 1.92, Samples/Sec: 69.27
+[2025-06-08 16:18:15] (step=0162300) Train Loss: 0.1051, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 16:19:06] (step=0162400) Train Loss: 0.1096, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 16:19:43] Beginning epoch 11...
+[2025-06-08 16:19:58] (step=0162500) Train Loss: 0.1089, Train Steps/Sec: 1.92, Samples/Sec: 69.27
+[2025-06-08 16:20:50] (step=0162600) Train Loss: 0.1161, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 16:21:42] (step=0162700) Train Loss: 0.1190, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 16:22:33] (step=0162800) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 16:23:25] (step=0162900) Train Loss: 0.1089, Train Steps/Sec: 1.92, Samples/Sec: 69.19
+[2025-06-08 16:24:17] (step=0163000) Train Loss: 0.1115, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 16:25:09] (step=0163100) Train Loss: 0.1151, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 16:26:00] (step=0163200) Train Loss: 0.1044, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 16:26:52] (step=0163300) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 16:27:44] (step=0163400) Train Loss: 0.1081, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 16:28:36] (step=0163500) Train Loss: 0.1089, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 16:29:28] (step=0163600) Train Loss: 0.1069, Train Steps/Sec: 1.92, Samples/Sec: 69.20
+[2025-06-08 16:30:19] (step=0163700) Train Loss: 0.1106, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 16:31:11] (step=0163800) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 16:32:03] (step=0163900) Train Loss: 0.1164, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 16:32:54] (step=0164000) Train Loss: 0.1131, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 16:33:08] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 16:33:59] (step=0164100) Train Loss: 0.1044, Train Steps/Sec: 1.54, Samples/Sec: 55.45
+[2025-06-08 16:34:51] (step=0164200) Train Loss: 0.1054, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 16:35:43] (step=0164300) Train Loss: 0.1055, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 16:36:35] (step=0164400) Train Loss: 0.1225, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 16:37:26] (step=0164500) Train Loss: 0.1084, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 16:38:18] (step=0164600) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 16:39:10] (step=0164700) Train Loss: 0.1106, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 16:40:02] (step=0164800) Train Loss: 0.1072, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 16:40:53] (step=0164900) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 16:41:45] (step=0165000) Train Loss: 0.1086, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 16:42:37] (step=0165100) Train Loss: 0.1138, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 16:43:29] (step=0165200) Train Loss: 0.1064, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 16:44:20] (step=0165300) Train Loss: 0.1150, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 16:45:12] (step=0165400) Train Loss: 0.1127, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 16:46:04] (step=0165500) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 16:46:56] (step=0165600) Train Loss: 0.1144, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-08 16:47:47] (step=0165700) Train Loss: 0.1105, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 16:48:39] (step=0165800) Train Loss: 0.1113, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 16:49:31] (step=0165900) Train Loss: 0.1074, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 16:50:23] (step=0166000) Train Loss: 0.1112, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 16:50:36] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 16:51:28] (step=0166100) Train Loss: 0.1202, Train Steps/Sec: 1.53, Samples/Sec: 55.07
+[2025-06-08 16:52:20] (step=0166200) Train Loss: 0.1116, Train Steps/Sec: 1.94, Samples/Sec: 69.74
+[2025-06-08 16:53:11] (step=0166300) Train Loss: 0.1116, Train Steps/Sec: 1.93, Samples/Sec: 69.33
+[2025-06-08 16:54:03] (step=0166400) Train Loss: 0.1142, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 16:54:55] (step=0166500) Train Loss: 0.1133, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 16:55:47] (step=0166600) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 16:56:38] (step=0166700) Train Loss: 0.1106, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 16:57:30] (step=0166800) Train Loss: 0.1100, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 16:58:22] (step=0166900) Train Loss: 0.1112, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 16:59:14] (step=0167000) Train Loss: 0.1091, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 17:00:05] (step=0167100) Train Loss: 0.1051, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 17:00:57] (step=0167200) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 17:01:49] (step=0167300) Train Loss: 0.1052, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 17:02:41] (step=0167400) Train Loss: 0.1047, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 17:03:32] (step=0167500) Train Loss: 0.1162, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 17:04:24] (step=0167600) Train Loss: 0.1131, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 17:05:16] (step=0167700) Train Loss: 0.1123, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 17:06:08] (step=0167800) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 17:06:59] (step=0167900) Train Loss: 0.1079, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 17:07:51] (step=0168000) Train Loss: 0.1097, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 17:08:11] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 17:09:03] (step=0168100) Train Loss: 0.1089, Train Steps/Sec: 1.39, Samples/Sec: 50.12
+[2025-06-08 17:09:55] (step=0168200) Train Loss: 0.1147, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 17:10:46] (step=0168300) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.32
+[2025-06-08 17:11:38] (step=0168400) Train Loss: 0.1075, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 17:12:30] (step=0168500) Train Loss: 0.1170, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 17:13:22] (step=0168600) Train Loss: 0.1057, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 17:14:13] (step=0168700) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 17:15:05] (step=0168800) Train Loss: 0.1150, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 17:15:57] (step=0168900) Train Loss: 0.1102, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 17:16:49] (step=0169000) Train Loss: 0.1141, Train Steps/Sec: 1.92, Samples/Sec: 69.23
+[2025-06-08 17:17:41] (step=0169100) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 17:18:32] (step=0169200) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 17:19:24] (step=0169300) Train Loss: 0.1176, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 17:20:16] (step=0169400) Train Loss: 0.1127, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 17:21:08] (step=0169500) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 17:21:59] (step=0169600) Train Loss: 0.1082, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 17:22:51] (step=0169700) Train Loss: 0.1151, Train Steps/Sec: 1.92, Samples/Sec: 69.25
+[2025-06-08 17:23:43] (step=0169800) Train Loss: 0.1088, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 17:24:34] (step=0169900) Train Loss: 0.1067, Train Steps/Sec: 1.95, Samples/Sec: 70.02
+[2025-06-08 17:25:26] (step=0170000) Train Loss: 0.1149, Train Steps/Sec: 1.94, Samples/Sec: 69.99
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 17:25:38] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 17:25:41] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 17:27:45] (step=0170000) Perceptual Loss: 0.1629, Eval Time: 127.14
+[2025-06-08 17:28:37] (step=0170100) Train Loss: 0.1155, Train Steps/Sec: 0.52, Samples/Sec: 18.83
+[2025-06-08 17:29:29] (step=0170200) Train Loss: 0.1116, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 17:30:21] (step=0170300) Train Loss: 0.1095, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 17:31:12] (step=0170400) Train Loss: 0.1136, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 17:32:04] (step=0170500) Train Loss: 0.1084, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 17:32:56] (step=0170600) Train Loss: 0.1073, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 17:33:47] (step=0170700) Train Loss: 0.1122, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 17:34:39] (step=0170800) Train Loss: 0.1174, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 17:35:31] (step=0170900) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.33
+[2025-06-08 17:36:23] (step=0171000) Train Loss: 0.1091, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 17:37:14] (step=0171100) Train Loss: 0.1153, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 17:38:06] (step=0171200) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 17:38:58] (step=0171300) Train Loss: 0.1103, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 17:39:50] (step=0171400) Train Loss: 0.1056, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 17:40:41] (step=0171500) Train Loss: 0.1158, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 17:41:33] (step=0171600) Train Loss: 0.1125, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 17:42:25] (step=0171700) Train Loss: 0.1080, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 17:43:17] (step=0171800) Train Loss: 0.1045, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 17:44:08] (step=0171900) Train Loss: 0.1081, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 17:45:00] (step=0172000) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 17:45:13] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 17:46:05] (step=0172100) Train Loss: 0.1160, Train Steps/Sec: 1.54, Samples/Sec: 55.58
+[2025-06-08 17:46:57] (step=0172200) Train Loss: 0.1109, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 17:47:48] (step=0172300) Train Loss: 0.1112, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 17:48:40] (step=0172400) Train Loss: 0.1191, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 17:49:32] (step=0172500) Train Loss: 0.1039, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 17:50:23] (step=0172600) Train Loss: 0.1094, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 17:51:15] (step=0172700) Train Loss: 0.1061, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 17:52:07] (step=0172800) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 17:52:59] (step=0172900) Train Loss: 0.1121, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 17:53:50] (step=0173000) Train Loss: 0.1166, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 17:54:42] (step=0173100) Train Loss: 0.1172, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 17:55:34] (step=0173200) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 17:56:26] (step=0173300) Train Loss: 0.1078, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 17:57:17] (step=0173400) Train Loss: 0.1116, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 17:58:09] (step=0173500) Train Loss: 0.1129, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 17:59:01] (step=0173600) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 17:59:53] (step=0173700) Train Loss: 0.1081, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 18:00:44] (step=0173800) Train Loss: 0.1111, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 18:01:36] (step=0173900) Train Loss: 0.1120, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 18:02:28] (step=0174000) Train Loss: 0.1173, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 18:02:41] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 18:03:33] (step=0174100) Train Loss: 0.1228, Train Steps/Sec: 1.53, Samples/Sec: 55.20
+[2025-06-08 18:04:25] (step=0174200) Train Loss: 0.1116, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 18:05:16] (step=0174300) Train Loss: 0.1102, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 18:06:08] (step=0174400) Train Loss: 0.1019, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 18:07:00] (step=0174500) Train Loss: 0.1181, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 18:07:51] (step=0174600) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 18:08:43] (step=0174700) Train Loss: 0.1086, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 18:09:35] (step=0174800) Train Loss: 0.1113, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 18:10:27] (step=0174900) Train Loss: 0.1124, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 18:11:18] (step=0175000) Train Loss: 0.1139, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 18:12:10] (step=0175100) Train Loss: 0.1040, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 18:13:02] (step=0175200) Train Loss: 0.1138, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 18:13:53] (step=0175300) Train Loss: 0.1097, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 18:14:45] (step=0175400) Train Loss: 0.1167, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 18:15:37] (step=0175500) Train Loss: 0.1030, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 18:16:29] (step=0175600) Train Loss: 0.1082, Train Steps/Sec: 1.93, Samples/Sec: 69.32
+[2025-06-08 18:17:20] (step=0175700) Train Loss: 0.1059, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 18:18:12] (step=0175800) Train Loss: 0.1067, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 18:19:04] (step=0175900) Train Loss: 0.1089, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 18:19:56] (step=0176000) Train Loss: 0.1121, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 18:20:09] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 18:21:00] (step=0176100) Train Loss: 0.1083, Train Steps/Sec: 1.54, Samples/Sec: 55.61
+[2025-06-08 18:21:52] (step=0176200) Train Loss: 0.1092, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 18:22:44] (step=0176300) Train Loss: 0.1110, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 18:23:35] (step=0176400) Train Loss: 0.1085, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 18:24:27] (step=0176500) Train Loss: 0.1148, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 18:25:19] (step=0176600) Train Loss: 0.1101, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 18:26:11] (step=0176700) Train Loss: 0.1062, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 18:27:02] (step=0176800) Train Loss: 0.1108, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 18:27:54] (step=0176900) Train Loss: 0.1090, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 18:28:46] (step=0177000) Train Loss: 0.1124, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 18:29:38] (step=0177100) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 18:30:29] (step=0177200) Train Loss: 0.1141, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 18:30:50] Beginning epoch 12...
+[2025-06-08 18:31:21] (step=0177300) Train Loss: 0.1031, Train Steps/Sec: 1.92, Samples/Sec: 69.25
+[2025-06-08 18:32:13] (step=0177400) Train Loss: 0.1172, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 18:33:05] (step=0177500) Train Loss: 0.1080, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 18:33:57] (step=0177600) Train Loss: 0.1103, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-08 18:34:48] (step=0177700) Train Loss: 0.1051, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 18:35:40] (step=0177800) Train Loss: 0.1131, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 18:36:32] (step=0177900) Train Loss: 0.1126, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 18:37:23] (step=0178000) Train Loss: 0.1116, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 18:37:37] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 18:38:28] (step=0178100) Train Loss: 0.1074, Train Steps/Sec: 1.54, Samples/Sec: 55.56
+[2025-06-08 18:39:20] (step=0178200) Train Loss: 0.1109, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 18:40:12] (step=0178300) Train Loss: 0.1109, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 18:41:04] (step=0178400) Train Loss: 0.1134, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 18:41:55] (step=0178500) Train Loss: 0.1181, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 18:42:47] (step=0178600) Train Loss: 0.1176, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 18:43:39] (step=0178700) Train Loss: 0.1088, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 18:44:30] (step=0178800) Train Loss: 0.1103, Train Steps/Sec: 1.94, Samples/Sec: 69.93
+[2025-06-08 18:45:22] (step=0178900) Train Loss: 0.1145, Train Steps/Sec: 1.95, Samples/Sec: 70.08
+[2025-06-08 18:46:13] (step=0179000) Train Loss: 0.1090, Train Steps/Sec: 1.94, Samples/Sec: 69.76
+[2025-06-08 18:47:05] (step=0179100) Train Loss: 0.1139, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 18:47:57] (step=0179200) Train Loss: 0.1135, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 18:48:48] (step=0179300) Train Loss: 0.1057, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 18:49:40] (step=0179400) Train Loss: 0.1085, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 18:50:32] (step=0179500) Train Loss: 0.1076, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 18:51:23] (step=0179600) Train Loss: 0.1102, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 18:52:15] (step=0179700) Train Loss: 0.1087, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 18:53:07] (step=0179800) Train Loss: 0.1123, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 18:53:59] (step=0179900) Train Loss: 0.1069, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 18:54:50] (step=0180000) Train Loss: 0.1112, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 18:55:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0180000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 18:55:19] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 18:57:24] (step=0180000) Perceptual Loss: 0.1502, Eval Time: 127.38
+[2025-06-08 18:58:16] (step=0180100) Train Loss: 0.1125, Train Steps/Sec: 0.49, Samples/Sec: 17.53
+[2025-06-08 18:59:08] (step=0180200) Train Loss: 0.1126, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 18:59:59] (step=0180300) Train Loss: 0.1080, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 19:00:51] (step=0180400) Train Loss: 0.1057, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 19:01:42] (step=0180500) Train Loss: 0.1032, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 19:02:34] (step=0180600) Train Loss: 0.1113, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 19:03:26] (step=0180700) Train Loss: 0.1112, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 19:04:18] (step=0180800) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 19:05:10] (step=0180900) Train Loss: 0.1151, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-08 19:06:01] (step=0181000) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 19:06:53] (step=0181100) Train Loss: 0.1077, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 19:07:45] (step=0181200) Train Loss: 0.1102, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 19:08:36] (step=0181300) Train Loss: 0.1049, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 19:09:28] (step=0181400) Train Loss: 0.1115, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 19:10:20] (step=0181500) Train Loss: 0.1175, Train Steps/Sec: 1.93, Samples/Sec: 69.45
+[2025-06-08 19:11:12] (step=0181600) Train Loss: 0.1074, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 19:12:03] (step=0181700) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 19:12:55] (step=0181800) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 19:13:47] (step=0181900) Train Loss: 0.1187, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 19:14:39] (step=0182000) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 19:14:52] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 19:15:44] (step=0182100) Train Loss: 0.1097, Train Steps/Sec: 1.53, Samples/Sec: 55.19
+[2025-06-08 19:16:36] (step=0182200) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-08 19:17:27] (step=0182300) Train Loss: 0.1185, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 19:18:19] (step=0182400) Train Loss: 0.1031, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 19:19:11] (step=0182500) Train Loss: 0.1080, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 19:20:03] (step=0182600) Train Loss: 0.1046, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 19:20:54] (step=0182700) Train Loss: 0.1127, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 19:21:46] (step=0182800) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 19:22:38] (step=0182900) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 19:23:30] (step=0183000) Train Loss: 0.1084, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 19:24:21] (step=0183100) Train Loss: 0.1155, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 19:25:13] (step=0183200) Train Loss: 0.1063, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 19:26:05] (step=0183300) Train Loss: 0.1149, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 19:26:57] (step=0183400) Train Loss: 0.1094, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 19:27:48] (step=0183500) Train Loss: 0.1058, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 19:28:40] (step=0183600) Train Loss: 0.1054, Train Steps/Sec: 1.93, Samples/Sec: 69.31
+[2025-06-08 19:29:32] (step=0183700) Train Loss: 0.1135, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 19:30:24] (step=0183800) Train Loss: 0.1084, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 19:31:16] (step=0183900) Train Loss: 0.1124, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 19:32:07] (step=0184000) Train Loss: 0.1088, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 19:32:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 19:33:12] (step=0184100) Train Loss: 0.1074, Train Steps/Sec: 1.55, Samples/Sec: 55.81
+[2025-06-08 19:34:04] (step=0184200) Train Loss: 0.1073, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 19:34:56] (step=0184300) Train Loss: 0.1156, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 19:35:47] (step=0184400) Train Loss: 0.1065, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 19:36:39] (step=0184500) Train Loss: 0.1100, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 19:37:31] (step=0184600) Train Loss: 0.1138, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 19:38:22] (step=0184700) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 19:39:14] (step=0184800) Train Loss: 0.1097, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 19:40:06] (step=0184900) Train Loss: 0.1082, Train Steps/Sec: 1.93, Samples/Sec: 69.41
+[2025-06-08 19:40:58] (step=0185000) Train Loss: 0.1089, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 19:41:49] (step=0185100) Train Loss: 0.1155, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 19:42:41] (step=0185200) Train Loss: 0.1080, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 19:43:33] (step=0185300) Train Loss: 0.1066, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 19:44:25] (step=0185400) Train Loss: 0.1063, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 19:45:16] (step=0185500) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 19:46:08] (step=0185600) Train Loss: 0.1090, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 19:47:00] (step=0185700) Train Loss: 0.1080, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 19:47:52] (step=0185800) Train Loss: 0.1081, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 19:48:43] (step=0185900) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 19:49:35] (step=0186000) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 19:49:48] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 19:50:40] (step=0186100) Train Loss: 0.1084, Train Steps/Sec: 1.54, Samples/Sec: 55.45
+[2025-06-08 19:51:32] (step=0186200) Train Loss: 0.1065, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 19:52:24] (step=0186300) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 19:53:15] (step=0186400) Train Loss: 0.1108, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 19:54:07] (step=0186500) Train Loss: 0.1098, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 19:54:59] (step=0186600) Train Loss: 0.1045, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 19:55:51] (step=0186700) Train Loss: 0.1101, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 19:56:42] (step=0186800) Train Loss: 0.1086, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 19:57:34] (step=0186900) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 19:58:26] (step=0187000) Train Loss: 0.1089, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 19:59:18] (step=0187100) Train Loss: 0.1082, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 20:00:09] (step=0187200) Train Loss: 0.1023, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 20:01:01] (step=0187300) Train Loss: 0.1065, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 20:01:53] (step=0187400) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 20:02:44] (step=0187500) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 20:03:36] (step=0187600) Train Loss: 0.1073, Train Steps/Sec: 1.92, Samples/Sec: 69.27
+[2025-06-08 20:04:28] (step=0187700) Train Loss: 0.1126, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 20:05:19] (step=0187800) Train Loss: 0.1125, Train Steps/Sec: 1.95, Samples/Sec: 70.07
+[2025-06-08 20:06:11] (step=0187900) Train Loss: 0.1097, Train Steps/Sec: 1.94, Samples/Sec: 69.95
+[2025-06-08 20:07:03] (step=0188000) Train Loss: 0.1110, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 20:07:15] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 20:08:07] (step=0188100) Train Loss: 0.1056, Train Steps/Sec: 1.55, Samples/Sec: 55.75
+[2025-06-08 20:08:59] (step=0188200) Train Loss: 0.1146, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 20:09:51] (step=0188300) Train Loss: 0.1041, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 20:10:42] (step=0188400) Train Loss: 0.1097, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 20:11:34] (step=0188500) Train Loss: 0.1024, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 20:12:26] (step=0188600) Train Loss: 0.1105, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 20:13:18] (step=0188700) Train Loss: 0.1042, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 20:14:09] (step=0188800) Train Loss: 0.1114, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 20:15:01] (step=0188900) Train Loss: 0.1090, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 20:15:53] (step=0189000) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 20:16:45] (step=0189100) Train Loss: 0.1137, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 20:17:36] (step=0189200) Train Loss: 0.1018, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 20:18:28] (step=0189300) Train Loss: 0.1081, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 20:19:20] (step=0189400) Train Loss: 0.1124, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 20:20:11] (step=0189500) Train Loss: 0.1085, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 20:21:03] (step=0189600) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 20:21:55] (step=0189700) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 20:22:47] (step=0189800) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 20:23:39] (step=0189900) Train Loss: 0.1109, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 20:24:30] (step=0190000) Train Loss: 0.1143, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 20:24:43] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 20:24:46] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 20:26:51] (step=0190000) Perceptual Loss: 0.1308, Eval Time: 127.64
+[2025-06-08 20:27:43] (step=0190100) Train Loss: 0.1110, Train Steps/Sec: 0.52, Samples/Sec: 18.70
+[2025-06-08 20:28:35] (step=0190200) Train Loss: 0.1031, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 20:29:26] (step=0190300) Train Loss: 0.1100, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 20:30:18] (step=0190400) Train Loss: 0.1087, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 20:31:10] (step=0190500) Train Loss: 0.1138, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 20:32:02] (step=0190600) Train Loss: 0.1161, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 20:32:53] (step=0190700) Train Loss: 0.1068, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 20:33:45] (step=0190800) Train Loss: 0.1016, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 20:34:37] (step=0190900) Train Loss: 0.1094, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 20:35:29] (step=0191000) Train Loss: 0.1187, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 20:36:20] (step=0191100) Train Loss: 0.1046, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 20:37:12] (step=0191200) Train Loss: 0.1059, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 20:38:04] (step=0191300) Train Loss: 0.1124, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 20:38:55] (step=0191400) Train Loss: 0.1117, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 20:39:47] (step=0191500) Train Loss: 0.1138, Train Steps/Sec: 1.92, Samples/Sec: 69.29
+[2025-06-08 20:40:39] (step=0191600) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 20:41:31] (step=0191700) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 20:42:23] (step=0191800) Train Loss: 0.1117, Train Steps/Sec: 1.93, Samples/Sec: 69.37
+[2025-06-08 20:43:14] (step=0191900) Train Loss: 0.1080, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 20:44:06] (step=0192000) Train Loss: 0.1076, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 20:44:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 20:44:25] Beginning epoch 13...
+[2025-06-08 20:45:12] (step=0192100) Train Loss: 0.1076, Train Steps/Sec: 1.52, Samples/Sec: 54.57
+[2025-06-08 20:46:04] (step=0192200) Train Loss: 0.1133, Train Steps/Sec: 1.93, Samples/Sec: 69.30
+[2025-06-08 20:46:56] (step=0192300) Train Loss: 0.1151, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 20:47:48] (step=0192400) Train Loss: 0.1047, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 20:48:39] (step=0192500) Train Loss: 0.1102, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 20:49:31] (step=0192600) Train Loss: 0.1026, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 20:50:23] (step=0192700) Train Loss: 0.1098, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 20:51:14] (step=0192800) Train Loss: 0.1083, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 20:52:06] (step=0192900) Train Loss: 0.1106, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 20:52:58] (step=0193000) Train Loss: 0.1085, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 20:53:50] (step=0193100) Train Loss: 0.1084, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 20:54:41] (step=0193200) Train Loss: 0.1102, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 20:55:33] (step=0193300) Train Loss: 0.1090, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 20:56:25] (step=0193400) Train Loss: 0.1032, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 20:57:17] (step=0193500) Train Loss: 0.1108, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 20:58:08] (step=0193600) Train Loss: 0.1093, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 20:59:00] (step=0193700) Train Loss: 0.1106, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 20:59:52] (step=0193800) Train Loss: 0.1068, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 21:00:43] (step=0193900) Train Loss: 0.1065, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 21:01:35] (step=0194000) Train Loss: 0.1168, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 21:01:48] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 21:02:40] (step=0194100) Train Loss: 0.1092, Train Steps/Sec: 1.54, Samples/Sec: 55.56
+[2025-06-08 21:03:32] (step=0194200) Train Loss: 0.1039, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-08 21:04:24] (step=0194300) Train Loss: 0.1042, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 21:05:15] (step=0194400) Train Loss: 0.1075, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 21:06:07] (step=0194500) Train Loss: 0.1133, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 21:06:59] (step=0194600) Train Loss: 0.1109, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 21:07:50] (step=0194700) Train Loss: 0.1089, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 21:08:42] (step=0194800) Train Loss: 0.1065, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 21:09:34] (step=0194900) Train Loss: 0.1068, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 21:10:26] (step=0195000) Train Loss: 0.1073, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 21:11:17] (step=0195100) Train Loss: 0.1112, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 21:12:09] (step=0195200) Train Loss: 0.1133, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 21:13:01] (step=0195300) Train Loss: 0.1078, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 21:13:53] (step=0195400) Train Loss: 0.1138, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 21:14:44] (step=0195500) Train Loss: 0.1150, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 21:15:36] (step=0195600) Train Loss: 0.1103, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 21:16:28] (step=0195700) Train Loss: 0.1166, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 21:17:20] (step=0195800) Train Loss: 0.1091, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 21:18:11] (step=0195900) Train Loss: 0.1028, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 21:19:03] (step=0196000) Train Loss: 0.1068, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 21:19:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 21:20:09] (step=0196100) Train Loss: 0.1018, Train Steps/Sec: 1.52, Samples/Sec: 54.74
+[2025-06-08 21:21:01] (step=0196200) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.52
+[2025-06-08 21:21:52] (step=0196300) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 21:22:44] (step=0196400) Train Loss: 0.1094, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 21:23:36] (step=0196500) Train Loss: 0.1105, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 21:24:27] (step=0196600) Train Loss: 0.1028, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 21:25:19] (step=0196700) Train Loss: 0.1059, Train Steps/Sec: 1.94, Samples/Sec: 69.98
+[2025-06-08 21:26:10] (step=0196800) Train Loss: 0.1103, Train Steps/Sec: 1.94, Samples/Sec: 70.01
+[2025-06-08 21:27:02] (step=0196900) Train Loss: 0.1078, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 21:27:54] (step=0197000) Train Loss: 0.1115, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 21:28:46] (step=0197100) Train Loss: 0.1145, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 21:29:37] (step=0197200) Train Loss: 0.1091, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 21:30:29] (step=0197300) Train Loss: 0.1097, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 21:31:21] (step=0197400) Train Loss: 0.1087, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 21:32:12] (step=0197500) Train Loss: 0.1090, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 21:33:04] (step=0197600) Train Loss: 0.1090, Train Steps/Sec: 1.93, Samples/Sec: 69.30
+[2025-06-08 21:33:56] (step=0197700) Train Loss: 0.1121, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 21:34:48] (step=0197800) Train Loss: 0.1073, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 21:35:40] (step=0197900) Train Loss: 0.1072, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 21:36:31] (step=0198000) Train Loss: 0.1155, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 21:36:45] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 21:37:36] (step=0198100) Train Loss: 0.1066, Train Steps/Sec: 1.54, Samples/Sec: 55.34
+[2025-06-08 21:38:28] (step=0198200) Train Loss: 0.1073, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 21:39:20] (step=0198300) Train Loss: 0.1126, Train Steps/Sec: 1.93, Samples/Sec: 69.39
+[2025-06-08 21:40:12] (step=0198400) Train Loss: 0.1050, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 21:41:03] (step=0198500) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 21:41:55] (step=0198600) Train Loss: 0.1089, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 21:42:47] (step=0198700) Train Loss: 0.1078, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 21:43:38] (step=0198800) Train Loss: 0.1083, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 21:44:30] (step=0198900) Train Loss: 0.1084, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 21:45:22] (step=0199000) Train Loss: 0.1106, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 21:46:14] (step=0199100) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 21:47:05] (step=0199200) Train Loss: 0.1096, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 21:47:57] (step=0199300) Train Loss: 0.1047, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 21:48:49] (step=0199400) Train Loss: 0.1176, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 21:49:41] (step=0199500) Train Loss: 0.1084, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 21:50:32] (step=0199600) Train Loss: 0.1123, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 21:51:24] (step=0199700) Train Loss: 0.1036, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 21:52:16] (step=0199800) Train Loss: 0.1096, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 21:53:07] (step=0199900) Train Loss: 0.1122, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 21:53:59] (step=0200000) Train Loss: 0.1010, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 21:54:24] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0200000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 21:54:27] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 21:56:32] (step=0200000) Perceptual Loss: 0.1352, Eval Time: 127.29
+[2025-06-08 21:57:23] (step=0200100) Train Loss: 0.1120, Train Steps/Sec: 0.49, Samples/Sec: 17.64
+[2025-06-08 21:58:15] (step=0200200) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 21:59:07] (step=0200300) Train Loss: 0.1097, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 21:59:59] (step=0200400) Train Loss: 0.1078, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 22:00:50] (step=0200500) Train Loss: 0.1070, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 22:01:42] (step=0200600) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 22:02:34] (step=0200700) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 22:03:26] (step=0200800) Train Loss: 0.1077, Train Steps/Sec: 1.92, Samples/Sec: 69.20
+[2025-06-08 22:04:17] (step=0200900) Train Loss: 0.1108, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 22:05:09] (step=0201000) Train Loss: 0.1007, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 22:06:01] (step=0201100) Train Loss: 0.1053, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 22:06:53] (step=0201200) Train Loss: 0.1084, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 22:07:44] (step=0201300) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 22:08:36] (step=0201400) Train Loss: 0.1103, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 22:09:28] (step=0201500) Train Loss: 0.1069, Train Steps/Sec: 1.92, Samples/Sec: 69.12
+[2025-06-08 22:10:20] (step=0201600) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 22:11:11] (step=0201700) Train Loss: 0.1046, Train Steps/Sec: 1.93, Samples/Sec: 69.55
+[2025-06-08 22:12:03] (step=0201800) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 22:12:55] (step=0201900) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 22:13:47] (step=0202000) Train Loss: 0.1143, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 22:14:05] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 22:14:57] (step=0202100) Train Loss: 0.1070, Train Steps/Sec: 1.43, Samples/Sec: 51.37
+[2025-06-08 22:15:49] (step=0202200) Train Loss: 0.1068, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 22:16:40] (step=0202300) Train Loss: 0.1083, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 22:17:32] (step=0202400) Train Loss: 0.1105, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 22:18:24] (step=0202500) Train Loss: 0.1058, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 22:19:15] (step=0202600) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 22:20:07] (step=0202700) Train Loss: 0.1076, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 22:20:59] (step=0202800) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 22:21:51] (step=0202900) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 22:22:42] (step=0203000) Train Loss: 0.1068, Train Steps/Sec: 1.94, Samples/Sec: 69.66
+[2025-06-08 22:23:34] (step=0203100) Train Loss: 0.1052, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 22:24:26] (step=0203200) Train Loss: 0.0992, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 22:25:18] (step=0203300) Train Loss: 0.1015, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 22:26:09] (step=0203400) Train Loss: 0.1015, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 22:27:01] (step=0203500) Train Loss: 0.1060, Train Steps/Sec: 1.92, Samples/Sec: 69.27
+[2025-06-08 22:27:53] (step=0203600) Train Loss: 0.1064, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 22:28:45] (step=0203700) Train Loss: 0.1138, Train Steps/Sec: 1.94, Samples/Sec: 69.71
+[2025-06-08 22:29:36] (step=0203800) Train Loss: 0.1079, Train Steps/Sec: 1.94, Samples/Sec: 69.73
+[2025-06-08 22:30:28] (step=0203900) Train Loss: 0.1054, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 22:31:20] (step=0204000) Train Loss: 0.1114, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 22:31:33] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 22:32:25] (step=0204100) Train Loss: 0.1084, Train Steps/Sec: 1.54, Samples/Sec: 55.32
+[2025-06-08 22:33:17] (step=0204200) Train Loss: 0.1018, Train Steps/Sec: 1.93, Samples/Sec: 69.30
+[2025-06-08 22:34:08] (step=0204300) Train Loss: 0.1131, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 22:35:00] (step=0204400) Train Loss: 0.1101, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 22:35:52] (step=0204500) Train Loss: 0.1055, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 22:36:43] (step=0204600) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 22:37:35] (step=0204700) Train Loss: 0.1073, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 22:38:27] (step=0204800) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 22:39:19] (step=0204900) Train Loss: 0.1044, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 22:40:11] (step=0205000) Train Loss: 0.1052, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 22:41:02] (step=0205100) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 22:41:54] (step=0205200) Train Loss: 0.1130, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 22:42:46] (step=0205300) Train Loss: 0.1051, Train Steps/Sec: 1.93, Samples/Sec: 69.58
+[2025-06-08 22:43:38] (step=0205400) Train Loss: 0.1123, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 22:44:29] (step=0205500) Train Loss: 0.1134, Train Steps/Sec: 1.93, Samples/Sec: 69.46
+[2025-06-08 22:45:21] (step=0205600) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 22:46:12] (step=0205700) Train Loss: 0.1055, Train Steps/Sec: 1.95, Samples/Sec: 70.07
+[2025-06-08 22:47:04] (step=0205800) Train Loss: 0.1111, Train Steps/Sec: 1.95, Samples/Sec: 70.08
+[2025-06-08 22:47:55] (step=0205900) Train Loss: 0.1056, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 22:48:47] (step=0206000) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 22:49:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 22:49:52] (step=0206100) Train Loss: 0.1078, Train Steps/Sec: 1.53, Samples/Sec: 55.19
+[2025-06-08 22:50:44] (step=0206200) Train Loss: 0.1120, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-08 22:51:36] (step=0206300) Train Loss: 0.1108, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 22:52:28] (step=0206400) Train Loss: 0.1058, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 22:53:19] (step=0206500) Train Loss: 0.1093, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 22:54:11] (step=0206600) Train Loss: 0.1041, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 22:55:03] (step=0206700) Train Loss: 0.1107, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 22:55:44] Beginning epoch 14...
+[2025-06-08 22:55:55] (step=0206800) Train Loss: 0.1057, Train Steps/Sec: 1.92, Samples/Sec: 69.11
+[2025-06-08 22:56:47] (step=0206900) Train Loss: 0.1043, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 22:57:39] (step=0207000) Train Loss: 0.1101, Train Steps/Sec: 1.94, Samples/Sec: 69.70
+[2025-06-08 22:58:30] (step=0207100) Train Loss: 0.1079, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 22:59:22] (step=0207200) Train Loss: 0.1140, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 23:00:14] (step=0207300) Train Loss: 0.1101, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 23:01:06] (step=0207400) Train Loss: 0.1056, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 23:01:57] (step=0207500) Train Loss: 0.1096, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 23:02:49] (step=0207600) Train Loss: 0.1113, Train Steps/Sec: 1.93, Samples/Sec: 69.40
+[2025-06-08 23:03:41] (step=0207700) Train Loss: 0.1083, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 23:04:32] (step=0207800) Train Loss: 0.1083, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 23:05:24] (step=0207900) Train Loss: 0.1091, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 23:06:16] (step=0208000) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.66
+[2025-06-08 23:06:30] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 23:07:21] (step=0208100) Train Loss: 0.1064, Train Steps/Sec: 1.52, Samples/Sec: 54.88
+[2025-06-08 23:08:13] (step=0208200) Train Loss: 0.1092, Train Steps/Sec: 1.93, Samples/Sec: 69.51
+[2025-06-08 23:09:05] (step=0208300) Train Loss: 0.1115, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 23:09:57] (step=0208400) Train Loss: 0.1084, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 23:10:48] (step=0208500) Train Loss: 0.1104, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 23:11:40] (step=0208600) Train Loss: 0.1053, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 23:12:32] (step=0208700) Train Loss: 0.1060, Train Steps/Sec: 1.93, Samples/Sec: 69.42
+[2025-06-08 23:13:24] (step=0208800) Train Loss: 0.1098, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 23:14:16] (step=0208900) Train Loss: 0.1023, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 23:15:07] (step=0209000) Train Loss: 0.1096, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 23:15:59] (step=0209100) Train Loss: 0.1118, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 23:16:51] (step=0209200) Train Loss: 0.1119, Train Steps/Sec: 1.94, Samples/Sec: 69.68
+[2025-06-08 23:17:42] (step=0209300) Train Loss: 0.1070, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 23:18:34] (step=0209400) Train Loss: 0.1097, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 23:19:26] (step=0209500) Train Loss: 0.1074, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 23:20:18] (step=0209600) Train Loss: 0.1070, Train Steps/Sec: 1.92, Samples/Sec: 69.29
+[2025-06-08 23:21:10] (step=0209700) Train Loss: 0.1119, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 23:22:01] (step=0209800) Train Loss: 0.1035, Train Steps/Sec: 1.93, Samples/Sec: 69.65
+[2025-06-08 23:22:53] (step=0209900) Train Loss: 0.1023, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 23:23:45] (step=0210000) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 23:23:58] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-08 23:24:01] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-08 23:26:06] (step=0210000) Perceptual Loss: 0.1250, Eval Time: 127.60
+[2025-06-08 23:26:58] (step=0210100) Train Loss: 0.1037, Train Steps/Sec: 0.52, Samples/Sec: 18.68
+[2025-06-08 23:27:50] (step=0210200) Train Loss: 0.1065, Train Steps/Sec: 1.93, Samples/Sec: 69.43
+[2025-06-08 23:28:41] (step=0210300) Train Loss: 0.1070, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 23:29:33] (step=0210400) Train Loss: 0.1053, Train Steps/Sec: 1.94, Samples/Sec: 69.67
+[2025-06-08 23:30:25] (step=0210500) Train Loss: 0.1138, Train Steps/Sec: 1.94, Samples/Sec: 69.69
+[2025-06-08 23:31:16] (step=0210600) Train Loss: 0.1099, Train Steps/Sec: 1.93, Samples/Sec: 69.44
+[2025-06-08 23:32:08] (step=0210700) Train Loss: 0.1040, Train Steps/Sec: 1.93, Samples/Sec: 69.56
+[2025-06-08 23:33:00] (step=0210800) Train Loss: 0.1074, Train Steps/Sec: 1.93, Samples/Sec: 69.34
+[2025-06-08 23:33:52] (step=0210900) Train Loss: 0.1065, Train Steps/Sec: 1.94, Samples/Sec: 69.72
+[2025-06-08 23:34:43] (step=0211000) Train Loss: 0.1068, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 23:35:35] (step=0211100) Train Loss: 0.1128, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 23:36:27] (step=0211200) Train Loss: 0.1104, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 23:37:19] (step=0211300) Train Loss: 0.1033, Train Steps/Sec: 1.93, Samples/Sec: 69.48
+[2025-06-08 23:38:10] (step=0211400) Train Loss: 0.1095, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 23:39:02] (step=0211500) Train Loss: 0.1035, Train Steps/Sec: 1.93, Samples/Sec: 69.35
+[2025-06-08 23:39:54] (step=0211600) Train Loss: 0.1079, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 23:40:46] (step=0211700) Train Loss: 0.1179, Train Steps/Sec: 1.93, Samples/Sec: 69.54
+[2025-06-08 23:41:37] (step=0211800) Train Loss: 0.1072, Train Steps/Sec: 1.93, Samples/Sec: 69.64
+[2025-06-08 23:42:29] (step=0211900) Train Loss: 0.1047, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 23:43:21] (step=0212000) Train Loss: 0.1096, Train Steps/Sec: 1.93, Samples/Sec: 69.47
+[2025-06-08 23:43:35] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-08 23:44:27] (step=0212100) Train Loss: 0.1064, Train Steps/Sec: 1.52, Samples/Sec: 54.70
+[2025-06-08 23:45:19] (step=0212200) Train Loss: 0.1044, Train Steps/Sec: 1.93, Samples/Sec: 69.36
+[2025-06-08 23:46:10] (step=0212300) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 23:47:02] (step=0212400) Train Loss: 0.1048, Train Steps/Sec: 1.93, Samples/Sec: 69.63
+[2025-06-08 23:47:54] (step=0212500) Train Loss: 0.1075, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 23:48:46] (step=0212600) Train Loss: 0.1132, Train Steps/Sec: 1.93, Samples/Sec: 69.50
+[2025-06-08 23:49:37] (step=0212700) Train Loss: 0.1094, Train Steps/Sec: 1.93, Samples/Sec: 69.59
+[2025-06-08 23:50:29] (step=0212800) Train Loss: 0.1147, Train Steps/Sec: 1.93, Samples/Sec: 69.38
+[2025-06-08 23:51:21] (step=0212900) Train Loss: 0.1017, Train Steps/Sec: 1.93, Samples/Sec: 69.60
+[2025-06-08 23:52:13] (step=0213000) Train Loss: 0.1036, Train Steps/Sec: 1.93, Samples/Sec: 69.53
+[2025-06-08 23:53:04] (step=0213100) Train Loss: 0.1032, Train Steps/Sec: 1.93, Samples/Sec: 69.62
+[2025-06-08 23:53:56] (step=0213200) Train Loss: 0.1080, Train Steps/Sec: 1.93, Samples/Sec: 69.57
+[2025-06-08 23:54:48] (step=0213300) Train Loss: 0.1063, Train Steps/Sec: 1.93, Samples/Sec: 69.49
+[2025-06-08 23:55:40] (step=0213400) Train Loss: 0.1111, Train Steps/Sec: 1.93, Samples/Sec: 69.61
+[2025-06-08 23:56:32] (step=0213500) Train Loss: 0.1130, Train Steps/Sec: 1.92, Samples/Sec: 69.27
+[2025-06-08 23:57:22] (step=0213600) Train Loss: 0.1146, Train Steps/Sec: 1.98, Samples/Sec: 71.35
+[2025-06-08 23:58:08] (step=0213700) Train Loss: 0.1043, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-08 23:58:54] (step=0213800) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-08 23:59:40] (step=0213900) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 00:00:26] (step=0214000) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 00:00:40] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 00:01:25] (step=0214100) Train Loss: 0.1144, Train Steps/Sec: 1.67, Samples/Sec: 60.24
+[2025-06-09 00:02:12] (step=0214200) Train Loss: 0.1152, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-06-09 00:02:57] (step=0214300) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 00:03:43] (step=0214400) Train Loss: 0.1130, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 00:04:29] (step=0214500) Train Loss: 0.1089, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-06-09 00:05:15] (step=0214600) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 00:06:01] (step=0214700) Train Loss: 0.1055, Train Steps/Sec: 2.19, Samples/Sec: 78.91
+[2025-06-09 00:06:47] (step=0214800) Train Loss: 0.0984, Train Steps/Sec: 2.19, Samples/Sec: 78.79
+[2025-06-09 00:07:32] (step=0214900) Train Loss: 0.1057, Train Steps/Sec: 2.19, Samples/Sec: 78.80
+[2025-06-09 00:08:18] (step=0215000) Train Loss: 0.1144, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 00:09:04] (step=0215100) Train Loss: 0.1145, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 00:09:50] (step=0215200) Train Loss: 0.1136, Train Steps/Sec: 2.17, Samples/Sec: 78.30
+[2025-06-09 00:10:36] (step=0215300) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 00:11:22] (step=0215400) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 00:12:08] (step=0215500) Train Loss: 0.1077, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-06-09 00:12:54] (step=0215600) Train Loss: 0.1128, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 00:13:40] (step=0215700) Train Loss: 0.0939, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 00:14:26] (step=0215800) Train Loss: 0.1023, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 00:15:12] (step=0215900) Train Loss: 0.1050, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 00:15:57] (step=0216000) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 00:16:11] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 00:16:57] (step=0216100) Train Loss: 0.1097, Train Steps/Sec: 1.68, Samples/Sec: 60.57
+[2025-06-09 00:17:43] (step=0216200) Train Loss: 0.1125, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 00:18:29] (step=0216300) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 00:19:15] (step=0216400) Train Loss: 0.1118, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 00:20:01] (step=0216500) Train Loss: 0.1070, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 00:20:47] (step=0216600) Train Loss: 0.1019, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 00:21:33] (step=0216700) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 00:22:18] (step=0216800) Train Loss: 0.1032, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 00:23:05] (step=0216900) Train Loss: 0.1066, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 00:23:50] (step=0217000) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 00:24:36] (step=0217100) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 00:25:22] (step=0217200) Train Loss: 0.1177, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 00:26:08] (step=0217300) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 00:26:54] (step=0217400) Train Loss: 0.1080, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 00:27:40] (step=0217500) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 00:28:26] (step=0217600) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 00:29:12] (step=0217700) Train Loss: 0.1121, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 00:29:58] (step=0217800) Train Loss: 0.1059, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 00:30:44] (step=0217900) Train Loss: 0.1111, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 00:31:30] (step=0218000) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 00:31:44] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 00:32:30] (step=0218100) Train Loss: 0.1098, Train Steps/Sec: 1.66, Samples/Sec: 59.74
+[2025-06-09 00:33:16] (step=0218200) Train Loss: 0.1020, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 00:34:02] (step=0218300) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 00:34:48] (step=0218400) Train Loss: 0.1108, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 00:35:34] (step=0218500) Train Loss: 0.1128, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 00:36:19] (step=0218600) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 00:37:05] (step=0218700) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 00:37:51] (step=0218800) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 00:38:37] (step=0218900) Train Loss: 0.1072, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 00:39:23] (step=0219000) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 00:40:09] (step=0219100) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 00:40:55] (step=0219200) Train Loss: 0.1029, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 00:41:41] (step=0219300) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 00:42:27] (step=0219400) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 00:43:14] (step=0219500) Train Loss: 0.1020, Train Steps/Sec: 2.13, Samples/Sec: 76.60
+[2025-06-09 00:44:00] (step=0219600) Train Loss: 0.1090, Train Steps/Sec: 2.16, Samples/Sec: 77.63
+[2025-06-09 00:44:46] (step=0219700) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 00:45:32] (step=0219800) Train Loss: 0.1170, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-06-09 00:46:18] (step=0219900) Train Loss: 0.1121, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 00:47:04] (step=0220000) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 00:47:30] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0220000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 00:47:34] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 00:49:41] (step=0220000) Perceptual Loss: 0.1389, Eval Time: 130.54
+[2025-06-09 00:50:27] (step=0220100) Train Loss: 0.1044, Train Steps/Sec: 0.49, Samples/Sec: 17.71
+[2025-06-09 00:51:13] (step=0220200) Train Loss: 0.1037, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 00:51:59] (step=0220300) Train Loss: 0.1048, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 00:52:45] (step=0220400) Train Loss: 0.1060, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 00:53:30] (step=0220500) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 00:54:16] (step=0220600) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 00:55:03] (step=0220700) Train Loss: 0.1050, Train Steps/Sec: 2.13, Samples/Sec: 76.57
+[2025-06-09 00:55:50] (step=0220800) Train Loss: 0.1078, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-06-09 00:56:36] (step=0220900) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 00:57:21] (step=0221000) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 00:58:10] (step=0221100) Train Loss: 0.1046, Train Steps/Sec: 2.04, Samples/Sec: 73.61
+[2025-06-09 00:58:56] (step=0221200) Train Loss: 0.1085, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 00:59:42] (step=0221300) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 01:00:28] (step=0221400) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 01:01:14] (step=0221500) Train Loss: 0.1067, Train Steps/Sec: 2.17, Samples/Sec: 78.20
+[2025-06-09 01:01:37] Beginning epoch 15...
+[2025-06-09 01:02:00] (step=0221600) Train Loss: 0.1074, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-06-09 01:02:46] (step=0221700) Train Loss: 0.1080, Train Steps/Sec: 2.17, Samples/Sec: 78.24
+[2025-06-09 01:03:34] (step=0221800) Train Loss: 0.1127, Train Steps/Sec: 2.12, Samples/Sec: 76.34
+[2025-06-09 01:04:20] (step=0221900) Train Loss: 0.1070, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 01:05:06] (step=0222000) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 01:05:19] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 01:06:05] (step=0222100) Train Loss: 0.1074, Train Steps/Sec: 1.67, Samples/Sec: 60.17
+[2025-06-09 01:06:51] (step=0222200) Train Loss: 0.1015, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-06-09 01:07:37] (step=0222300) Train Loss: 0.1113, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-06-09 01:08:23] (step=0222400) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 01:09:09] (step=0222500) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 01:09:56] (step=0222600) Train Loss: 0.1111, Train Steps/Sec: 2.15, Samples/Sec: 77.35
+[2025-06-09 01:10:42] (step=0222700) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 01:11:28] (step=0222800) Train Loss: 0.1075, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-06-09 01:12:14] (step=0222900) Train Loss: 0.1132, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 01:13:00] (step=0223000) Train Loss: 0.1108, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-06-09 01:13:46] (step=0223100) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 01:14:32] (step=0223200) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 01:15:18] (step=0223300) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 01:16:04] (step=0223400) Train Loss: 0.1098, Train Steps/Sec: 2.16, Samples/Sec: 77.64
+[2025-06-09 01:16:50] (step=0223500) Train Loss: 0.1066, Train Steps/Sec: 2.15, Samples/Sec: 77.54
+[2025-06-09 01:17:36] (step=0223600) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 01:18:22] (step=0223700) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 01:19:08] (step=0223800) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 01:19:54] (step=0223900) Train Loss: 0.1074, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-06-09 01:20:40] (step=0224000) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 01:20:54] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 01:21:40] (step=0224100) Train Loss: 0.1092, Train Steps/Sec: 1.67, Samples/Sec: 60.02
+[2025-06-09 01:22:26] (step=0224200) Train Loss: 0.1111, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-06-09 01:23:12] (step=0224300) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 01:23:58] (step=0224400) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 01:24:44] (step=0224500) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 01:25:30] (step=0224600) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 01:26:17] (step=0224700) Train Loss: 0.1115, Train Steps/Sec: 2.13, Samples/Sec: 76.74
+[2025-06-09 01:27:03] (step=0224800) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 01:27:48] (step=0224900) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 01:28:36] (step=0225000) Train Loss: 0.1075, Train Steps/Sec: 2.12, Samples/Sec: 76.26
+[2025-06-09 01:29:22] (step=0225100) Train Loss: 0.1031, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 01:30:07] (step=0225200) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 01:30:53] (step=0225300) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 01:32:26] (step=0225400) Train Loss: 0.1072, Train Steps/Sec: 1.07, Samples/Sec: 38.65
+[2025-06-09 01:33:13] (step=0225500) Train Loss: 0.1128, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-06-09 01:33:59] (step=0225600) Train Loss: 0.1127, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 01:34:45] (step=0225700) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 01:35:34] (step=0225800) Train Loss: 0.1125, Train Steps/Sec: 2.01, Samples/Sec: 72.25
+[2025-06-09 01:36:21] (step=0225900) Train Loss: 0.1097, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-06-09 01:37:07] (step=0226000) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 01:37:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 01:38:06] (step=0226100) Train Loss: 0.1082, Train Steps/Sec: 1.69, Samples/Sec: 60.76
+[2025-06-09 01:38:52] (step=0226200) Train Loss: 0.1142, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-06-09 01:39:38] (step=0226300) Train Loss: 0.1076, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 01:40:24] (step=0226400) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 01:41:10] (step=0226500) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 01:41:56] (step=0226600) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 01:42:42] (step=0226700) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 01:43:28] (step=0226800) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 01:44:15] (step=0226900) Train Loss: 0.1099, Train Steps/Sec: 2.09, Samples/Sec: 75.17
+[2025-06-09 01:45:01] (step=0227000) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 01:45:47] (step=0227100) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 01:46:36] (step=0227200) Train Loss: 0.1083, Train Steps/Sec: 2.06, Samples/Sec: 74.05
+[2025-06-09 01:47:22] (step=0227300) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 01:48:08] (step=0227400) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 01:48:55] (step=0227500) Train Loss: 0.1022, Train Steps/Sec: 2.09, Samples/Sec: 75.24
+[2025-06-09 01:49:42] (step=0227600) Train Loss: 0.1070, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-06-09 01:50:27] (step=0227700) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.30
+[2025-06-09 01:51:15] (step=0227800) Train Loss: 0.1066, Train Steps/Sec: 2.12, Samples/Sec: 76.38
+[2025-06-09 01:52:04] (step=0227900) Train Loss: 0.1057, Train Steps/Sec: 2.05, Samples/Sec: 73.88
+[2025-06-09 01:52:50] (step=0228000) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 01:53:03] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 01:53:49] (step=0228100) Train Loss: 0.1065, Train Steps/Sec: 1.68, Samples/Sec: 60.55
+[2025-06-09 01:54:35] (step=0228200) Train Loss: 0.1071, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 01:55:21] (step=0228300) Train Loss: 0.1032, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 01:56:07] (step=0228400) Train Loss: 0.1111, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 01:56:53] (step=0228500) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 01:57:39] (step=0228600) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 01:58:25] (step=0228700) Train Loss: 0.1140, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 01:59:10] (step=0228800) Train Loss: 0.1035, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 01:59:57] (step=0228900) Train Loss: 0.1080, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-06-09 02:00:43] (step=0229000) Train Loss: 0.1089, Train Steps/Sec: 2.17, Samples/Sec: 78.28
+[2025-06-09 02:01:28] (step=0229100) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 02:02:14] (step=0229200) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 02:03:00] (step=0229300) Train Loss: 0.1043, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 02:03:46] (step=0229400) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 02:04:32] (step=0229500) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 02:05:18] (step=0229600) Train Loss: 0.1060, Train Steps/Sec: 2.17, Samples/Sec: 78.20
+[2025-06-09 02:06:04] (step=0229700) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 02:06:50] (step=0229800) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 02:07:36] (step=0229900) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 02:08:21] (step=0230000) Train Loss: 0.1035, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 02:08:35] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 02:08:38] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 02:10:42] (step=0230000) Perceptual Loss: 0.1402, Eval Time: 127.27
+[2025-06-09 02:11:28] (step=0230100) Train Loss: 0.1122, Train Steps/Sec: 0.53, Samples/Sec: 19.24
+[2025-06-09 02:12:14] (step=0230200) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 02:13:00] (step=0230300) Train Loss: 0.1000, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 02:13:46] (step=0230400) Train Loss: 0.1139, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 02:14:32] (step=0230500) Train Loss: 0.1029, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 02:15:18] (step=0230600) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 02:16:03] (step=0230700) Train Loss: 0.1170, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 02:16:50] (step=0230800) Train Loss: 0.1090, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-06-09 02:17:36] (step=0230900) Train Loss: 0.1084, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 02:18:21] (step=0231000) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 02:19:07] (step=0231100) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 02:19:54] (step=0231200) Train Loss: 0.1106, Train Steps/Sec: 2.13, Samples/Sec: 76.68
+[2025-06-09 02:20:40] (step=0231300) Train Loss: 0.1128, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 02:21:26] (step=0231400) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 02:22:14] (step=0231500) Train Loss: 0.1069, Train Steps/Sec: 2.10, Samples/Sec: 75.59
+[2025-06-09 02:23:00] (step=0231600) Train Loss: 0.1041, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-06-09 02:23:46] (step=0231700) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 02:24:32] (step=0231800) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 02:25:18] (step=0231900) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 02:26:04] (step=0232000) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 02:26:18] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 02:27:04] (step=0232100) Train Loss: 0.1083, Train Steps/Sec: 1.67, Samples/Sec: 59.94
+[2025-06-09 02:27:50] (step=0232200) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 02:28:38] (step=0232300) Train Loss: 0.1092, Train Steps/Sec: 2.07, Samples/Sec: 74.60
+[2025-06-09 02:29:24] (step=0232400) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 02:30:10] (step=0232500) Train Loss: 0.1019, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 02:30:56] (step=0232600) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 02:31:42] (step=0232700) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 02:32:28] (step=0232800) Train Loss: 0.1008, Train Steps/Sec: 2.16, Samples/Sec: 77.61
+[2025-06-09 02:33:14] (step=0232900) Train Loss: 0.1049, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-06-09 02:34:00] (step=0233000) Train Loss: 0.1137, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 02:34:46] (step=0233100) Train Loss: 0.1119, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 02:35:32] (step=0233200) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 02:36:18] (step=0233300) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 02:37:05] (step=0233400) Train Loss: 0.1100, Train Steps/Sec: 2.12, Samples/Sec: 76.15
+[2025-06-09 02:37:51] (step=0233500) Train Loss: 0.1087, Train Steps/Sec: 2.17, Samples/Sec: 77.94
+[2025-06-09 02:38:37] (step=0233600) Train Loss: 0.1022, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 02:39:23] (step=0233700) Train Loss: 0.1152, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-06-09 02:40:09] (step=0233800) Train Loss: 0.1119, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 02:40:55] (step=0233900) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 02:41:41] (step=0234000) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 02:41:55] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 02:42:41] (step=0234100) Train Loss: 0.1056, Train Steps/Sec: 1.67, Samples/Sec: 60.01
+[2025-06-09 02:43:27] (step=0234200) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 02:44:12] (step=0234300) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 02:44:58] (step=0234400) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 02:45:44] (step=0234500) Train Loss: 0.1080, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 02:46:30] (step=0234600) Train Loss: 0.1167, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 02:47:16] (step=0234700) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 02:48:04] (step=0234800) Train Loss: 0.1114, Train Steps/Sec: 2.10, Samples/Sec: 75.63
+[2025-06-09 02:48:50] (step=0234900) Train Loss: 0.1036, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 02:49:35] (step=0235000) Train Loss: 0.1067, Train Steps/Sec: 2.19, Samples/Sec: 78.87
+[2025-06-09 02:50:21] (step=0235100) Train Loss: 0.1087, Train Steps/Sec: 2.19, Samples/Sec: 78.87
+[2025-06-09 02:51:07] (step=0235200) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 02:52:02] (step=0235300) Train Loss: 0.1085, Train Steps/Sec: 1.81, Samples/Sec: 65.18
+[2025-06-09 02:52:48] (step=0235400) Train Loss: 0.1100, Train Steps/Sec: 2.17, Samples/Sec: 78.28
+[2025-06-09 02:53:36] (step=0235500) Train Loss: 0.1064, Train Steps/Sec: 2.07, Samples/Sec: 74.54
+[2025-06-09 02:54:22] (step=0235600) Train Loss: 0.1042, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 02:55:08] (step=0235700) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 02:55:56] (step=0235800) Train Loss: 0.1059, Train Steps/Sec: 2.09, Samples/Sec: 75.40
+[2025-06-09 02:56:42] (step=0235900) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 02:57:28] (step=0236000) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 02:57:41] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 02:58:28] (step=0236100) Train Loss: 0.1095, Train Steps/Sec: 1.65, Samples/Sec: 59.58
+[2025-06-09 02:59:14] (step=0236200) Train Loss: 0.1111, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-06-09 03:00:02] (step=0236300) Train Loss: 0.1097, Train Steps/Sec: 2.12, Samples/Sec: 76.33
+[2025-06-09 03:00:11] Beginning epoch 16...
+[2025-06-09 03:00:48] (step=0236400) Train Loss: 0.1109, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-06-09 03:01:34] (step=0236500) Train Loss: 0.1153, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 03:02:20] (step=0236600) Train Loss: 0.1004, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 03:03:06] (step=0236700) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 03:03:52] (step=0236800) Train Loss: 0.1065, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 03:04:38] (step=0236900) Train Loss: 0.1085, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 03:05:24] (step=0237000) Train Loss: 0.1096, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-06-09 03:06:10] (step=0237100) Train Loss: 0.1128, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-06-09 03:06:56] (step=0237200) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 03:07:42] (step=0237300) Train Loss: 0.1140, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 03:08:28] (step=0237400) Train Loss: 0.1046, Train Steps/Sec: 2.17, Samples/Sec: 78.28
+[2025-06-09 03:09:14] (step=0237500) Train Loss: 0.1097, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-06-09 03:10:00] (step=0237600) Train Loss: 0.1130, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 03:10:46] (step=0237700) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 03:11:31] (step=0237800) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 03:12:17] (step=0237900) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 03:13:03] (step=0238000) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 03:13:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 03:14:03] (step=0238100) Train Loss: 0.1155, Train Steps/Sec: 1.67, Samples/Sec: 60.00
+[2025-06-09 03:14:49] (step=0238200) Train Loss: 0.1147, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 03:15:35] (step=0238300) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 03:16:21] (step=0238400) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 03:17:07] (step=0238500) Train Loss: 0.1128, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 03:17:53] (step=0238600) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 03:18:38] (step=0238700) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 03:19:24] (step=0238800) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 03:20:10] (step=0238900) Train Loss: 0.1102, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 03:20:56] (step=0239000) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 03:21:42] (step=0239100) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 03:22:28] (step=0239200) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 03:23:14] (step=0239300) Train Loss: 0.1118, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 03:24:00] (step=0239400) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 03:24:46] (step=0239500) Train Loss: 0.1103, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-06-09 03:25:32] (step=0239600) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 03:26:18] (step=0239700) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 03:27:04] (step=0239800) Train Loss: 0.1129, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 03:27:49] (step=0239900) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 03:28:35] (step=0240000) Train Loss: 0.1151, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 03:29:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0240000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 03:29:04] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 03:31:08] (step=0240000) Perceptual Loss: 0.1649, Eval Time: 127.44
+[2025-06-09 03:31:55] (step=0240100) Train Loss: 0.1044, Train Steps/Sec: 0.50, Samples/Sec: 18.04
+[2025-06-09 03:32:41] (step=0240200) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 03:33:27] (step=0240300) Train Loss: 0.1119, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 03:34:12] (step=0240400) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 03:34:58] (step=0240500) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 03:35:44] (step=0240600) Train Loss: 0.1155, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 03:36:30] (step=0240700) Train Loss: 0.1093, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 03:37:16] (step=0240800) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 03:38:02] (step=0240900) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 03:38:48] (step=0241000) Train Loss: 0.1107, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 03:39:34] (step=0241100) Train Loss: 0.1112, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 03:40:19] (step=0241200) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 03:41:05] (step=0241300) Train Loss: 0.1043, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 03:41:51] (step=0241400) Train Loss: 0.1060, Train Steps/Sec: 2.17, Samples/Sec: 78.25
+[2025-06-09 03:42:37] (step=0241500) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 03:43:23] (step=0241600) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 03:44:09] (step=0241700) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 03:44:55] (step=0241800) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 03:45:40] (step=0241900) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 03:46:26] (step=0242000) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 03:46:39] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 03:47:25] (step=0242100) Train Loss: 0.1089, Train Steps/Sec: 1.69, Samples/Sec: 60.91
+[2025-06-09 03:48:11] (step=0242200) Train Loss: 0.1092, Train Steps/Sec: 2.19, Samples/Sec: 78.67
+[2025-06-09 03:48:57] (step=0242300) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 03:49:43] (step=0242400) Train Loss: 0.1131, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 03:50:29] (step=0242500) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 03:51:14] (step=0242600) Train Loss: 0.1122, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 03:52:00] (step=0242700) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 03:52:46] (step=0242800) Train Loss: 0.1139, Train Steps/Sec: 2.17, Samples/Sec: 78.24
+[2025-06-09 03:53:32] (step=0242900) Train Loss: 0.1077, Train Steps/Sec: 2.19, Samples/Sec: 78.68
+[2025-06-09 03:54:18] (step=0243000) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 03:55:04] (step=0243100) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 03:55:49] (step=0243200) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 03:56:35] (step=0243300) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 03:57:21] (step=0243400) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 03:58:07] (step=0243500) Train Loss: 0.1125, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 03:58:53] (step=0243600) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 03:59:39] (step=0243700) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 04:00:25] (step=0243800) Train Loss: 0.1049, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 04:01:10] (step=0243900) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.66
+[2025-06-09 04:01:56] (step=0244000) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 04:02:09] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 04:02:55] (step=0244100) Train Loss: 0.1084, Train Steps/Sec: 1.69, Samples/Sec: 60.87
+[2025-06-09 04:03:41] (step=0244200) Train Loss: 0.1099, Train Steps/Sec: 2.19, Samples/Sec: 78.66
+[2025-06-09 04:04:27] (step=0244300) Train Loss: 0.1023, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 04:05:13] (step=0244400) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 04:05:59] (step=0244500) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 04:06:44] (step=0244600) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 04:07:30] (step=0244700) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 04:08:16] (step=0244800) Train Loss: 0.1068, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 04:09:02] (step=0244900) Train Loss: 0.1092, Train Steps/Sec: 2.19, Samples/Sec: 78.66
+[2025-06-09 04:09:48] (step=0245000) Train Loss: 0.1108, Train Steps/Sec: 2.19, Samples/Sec: 78.98
+[2025-06-09 04:10:33] (step=0245100) Train Loss: 0.1064, Train Steps/Sec: 2.19, Samples/Sec: 79.01
+[2025-06-09 04:11:19] (step=0245200) Train Loss: 0.1080, Train Steps/Sec: 2.19, Samples/Sec: 78.69
+[2025-06-09 04:12:05] (step=0245300) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 04:12:51] (step=0245400) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 04:13:37] (step=0245500) Train Loss: 0.1067, Train Steps/Sec: 2.17, Samples/Sec: 78.25
+[2025-06-09 04:14:22] (step=0245600) Train Loss: 0.1022, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 04:15:08] (step=0245700) Train Loss: 0.1160, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 04:15:54] (step=0245800) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 04:16:40] (step=0245900) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 04:17:26] (step=0246000) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 04:17:39] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 04:18:25] (step=0246100) Train Loss: 0.1053, Train Steps/Sec: 1.69, Samples/Sec: 60.99
+[2025-06-09 04:19:11] (step=0246200) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 04:19:56] (step=0246300) Train Loss: 0.0987, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 04:20:43] (step=0246400) Train Loss: 0.1104, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-06-09 04:21:29] (step=0246500) Train Loss: 0.1013, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-06-09 04:22:15] (step=0246600) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 04:23:00] (step=0246700) Train Loss: 0.1061, Train Steps/Sec: 2.19, Samples/Sec: 78.69
+[2025-06-09 04:23:46] (step=0246800) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 04:24:32] (step=0246900) Train Loss: 0.1107, Train Steps/Sec: 2.19, Samples/Sec: 78.69
+[2025-06-09 04:25:18] (step=0247000) Train Loss: 0.1047, Train Steps/Sec: 2.19, Samples/Sec: 78.71
+[2025-06-09 04:26:04] (step=0247100) Train Loss: 0.1135, Train Steps/Sec: 2.19, Samples/Sec: 78.68
+[2025-06-09 04:26:49] (step=0247200) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 04:27:35] (step=0247300) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 04:28:21] (step=0247400) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 04:29:07] (step=0247500) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.30
+[2025-06-09 04:29:53] (step=0247600) Train Loss: 0.1064, Train Steps/Sec: 2.19, Samples/Sec: 78.67
+[2025-06-09 04:30:38] (step=0247700) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 04:31:24] (step=0247800) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 04:32:10] (step=0247900) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 04:32:56] (step=0248000) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.66
+[2025-06-09 04:33:09] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 04:33:55] (step=0248100) Train Loss: 0.1049, Train Steps/Sec: 1.69, Samples/Sec: 61.01
+[2025-06-09 04:34:41] (step=0248200) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 04:35:27] (step=0248300) Train Loss: 0.1161, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 04:36:13] (step=0248400) Train Loss: 0.0979, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 04:36:58] (step=0248500) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 04:37:44] (step=0248600) Train Loss: 0.1035, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+[2025-06-09 04:38:30] (step=0248700) Train Loss: 0.1049, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 04:39:16] (step=0248800) Train Loss: 0.1013, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 04:40:02] (step=0248900) Train Loss: 0.1038, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+[2025-06-09 04:40:48] (step=0249000) Train Loss: 0.1051, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 04:41:33] (step=0249100) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 04:42:19] (step=0249200) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 04:43:05] (step=0249300) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 04:43:51] (step=0249400) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 04:44:37] (step=0249500) Train Loss: 0.1060, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 04:45:23] (step=0249600) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 04:46:08] (step=0249700) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 04:46:54] (step=0249800) Train Loss: 0.1142, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 04:47:40] (step=0249900) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 04:48:26] (step=0250000) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 04:48:39] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 04:48:42] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 04:50:46] (step=0250000) Perceptual Loss: 0.1569, Eval Time: 127.15
+[2025-06-09 04:51:32] (step=0250100) Train Loss: 0.1137, Train Steps/Sec: 0.54, Samples/Sec: 19.32
+[2025-06-09 04:52:18] (step=0250200) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 04:53:04] (step=0250300) Train Loss: 0.1080, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 04:53:50] (step=0250400) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 04:54:36] (step=0250500) Train Loss: 0.1137, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 04:55:21] (step=0250600) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 04:56:07] (step=0250700) Train Loss: 0.1064, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 04:56:53] (step=0250800) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 04:57:39] (step=0250900) Train Loss: 0.1122, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 04:58:25] (step=0251000) Train Loss: 0.1169, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 04:59:06] Beginning epoch 17...
+[2025-06-09 04:59:11] (step=0251100) Train Loss: 0.1082, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-06-09 04:59:57] (step=0251200) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 05:00:43] (step=0251300) Train Loss: 0.1111, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 05:01:29] (step=0251400) Train Loss: 0.1014, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-06-09 05:02:15] (step=0251500) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 05:03:00] (step=0251600) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 05:03:46] (step=0251700) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 05:04:32] (step=0251800) Train Loss: 0.1048, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 05:05:18] (step=0251900) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 05:06:04] (step=0252000) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 05:06:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 05:07:03] (step=0252100) Train Loss: 0.1078, Train Steps/Sec: 1.69, Samples/Sec: 60.92
+[2025-06-09 05:07:49] (step=0252200) Train Loss: 0.1041, Train Steps/Sec: 2.19, Samples/Sec: 78.70
+[2025-06-09 05:08:34] (step=0252300) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 05:09:20] (step=0252400) Train Loss: 0.1139, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 05:10:06] (step=0252500) Train Loss: 0.1179, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 05:10:52] (step=0252600) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 05:11:38] (step=0252700) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 05:12:24] (step=0252800) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 05:13:09] (step=0252900) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 05:13:55] (step=0253000) Train Loss: 0.1118, Train Steps/Sec: 2.19, Samples/Sec: 78.69
+[2025-06-09 05:14:41] (step=0253100) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 05:15:27] (step=0253200) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 05:16:13] (step=0253300) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 05:16:59] (step=0253400) Train Loss: 0.1059, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 05:17:44] (step=0253500) Train Loss: 0.1018, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 05:18:30] (step=0253600) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 05:19:16] (step=0253700) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 05:20:02] (step=0253800) Train Loss: 0.1031, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 05:20:48] (step=0253900) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 05:21:34] (step=0254000) Train Loss: 0.1134, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 05:21:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 05:22:33] (step=0254100) Train Loss: 0.1114, Train Steps/Sec: 1.69, Samples/Sec: 60.77
+[2025-06-09 05:23:19] (step=0254200) Train Loss: 0.1007, Train Steps/Sec: 2.19, Samples/Sec: 78.69
+[2025-06-09 05:24:04] (step=0254300) Train Loss: 0.1078, Train Steps/Sec: 2.19, Samples/Sec: 78.68
+[2025-06-09 05:24:50] (step=0254400) Train Loss: 0.1126, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 05:25:36] (step=0254500) Train Loss: 0.1013, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+[2025-06-09 05:26:22] (step=0254600) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 05:27:08] (step=0254700) Train Loss: 0.1050, Train Steps/Sec: 2.19, Samples/Sec: 78.66
+[2025-06-09 05:27:54] (step=0254800) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 05:28:39] (step=0254900) Train Loss: 0.1128, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 05:29:25] (step=0255000) Train Loss: 0.1089, Train Steps/Sec: 2.19, Samples/Sec: 78.78
+[2025-06-09 05:30:11] (step=0255100) Train Loss: 0.1136, Train Steps/Sec: 2.19, Samples/Sec: 78.82
+[2025-06-09 05:30:56] (step=0255200) Train Loss: 0.1013, Train Steps/Sec: 2.19, Samples/Sec: 78.98
+[2025-06-09 05:31:42] (step=0255300) Train Loss: 0.1045, Train Steps/Sec: 2.19, Samples/Sec: 78.76
+[2025-06-09 05:32:28] (step=0255400) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 05:33:14] (step=0255500) Train Loss: 0.1154, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 05:34:00] (step=0255600) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 05:34:46] (step=0255700) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 05:35:32] (step=0255800) Train Loss: 0.1126, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 05:36:17] (step=0255900) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 05:37:03] (step=0256000) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 05:37:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 05:38:03] (step=0256100) Train Loss: 0.1064, Train Steps/Sec: 1.69, Samples/Sec: 60.77
+[2025-06-09 05:38:48] (step=0256200) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 05:39:34] (step=0256300) Train Loss: 0.1136, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 05:40:20] (step=0256400) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 05:41:06] (step=0256500) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 05:41:52] (step=0256600) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 05:42:37] (step=0256700) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 05:43:23] (step=0256800) Train Loss: 0.0996, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 05:44:09] (step=0256900) Train Loss: 0.1122, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 05:44:55] (step=0257000) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 05:45:41] (step=0257100) Train Loss: 0.1004, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 05:46:27] (step=0257200) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 05:47:12] (step=0257300) Train Loss: 0.1104, Train Steps/Sec: 2.19, Samples/Sec: 78.68
+[2025-06-09 05:47:58] (step=0257400) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 05:48:44] (step=0257500) Train Loss: 0.1061, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 05:49:30] (step=0257600) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 05:50:16] (step=0257700) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 05:51:02] (step=0257800) Train Loss: 0.1129, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 05:51:48] (step=0257900) Train Loss: 0.1029, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 05:52:33] (step=0258000) Train Loss: 0.1029, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 05:52:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 05:53:33] (step=0258100) Train Loss: 0.1143, Train Steps/Sec: 1.69, Samples/Sec: 60.78
+[2025-06-09 05:54:18] (step=0258200) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 05:55:04] (step=0258300) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 05:55:50] (step=0258400) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+[2025-06-09 05:56:36] (step=0258500) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 05:57:22] (step=0258600) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 05:58:07] (step=0258700) Train Loss: 0.1010, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+[2025-06-09 05:58:53] (step=0258800) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 05:59:39] (step=0258900) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 06:00:25] (step=0259000) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 06:01:11] (step=0259100) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 06:01:57] (step=0259200) Train Loss: 0.1018, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+[2025-06-09 06:02:42] (step=0259300) Train Loss: 0.1103, Train Steps/Sec: 2.19, Samples/Sec: 78.70
+[2025-06-09 06:03:28] (step=0259400) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 06:04:14] (step=0259500) Train Loss: 0.1069, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-06-09 06:05:00] (step=0259600) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 06:05:46] (step=0259700) Train Loss: 0.1049, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 06:06:32] (step=0259800) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 06:07:17] (step=0259900) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 06:08:03] (step=0260000) Train Loss: 0.1038, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 06:08:28] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0260000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 06:08:31] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 06:10:36] (step=0260000) Perceptual Loss: 0.1439, Eval Time: 127.40
+[2025-06-09 06:11:22] (step=0260100) Train Loss: 0.1171, Train Steps/Sec: 0.50, Samples/Sec: 18.14
+[2025-06-09 06:12:08] (step=0260200) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 06:12:53] (step=0260300) Train Loss: 0.1116, Train Steps/Sec: 2.19, Samples/Sec: 78.66
+[2025-06-09 06:13:39] (step=0260400) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 06:14:25] (step=0260500) Train Loss: 0.1150, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 06:15:11] (step=0260600) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 06:15:57] (step=0260700) Train Loss: 0.1052, Train Steps/Sec: 2.17, Samples/Sec: 78.25
+[2025-06-09 06:16:43] (step=0260800) Train Loss: 0.1025, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 06:17:29] (step=0260900) Train Loss: 0.1068, Train Steps/Sec: 2.17, Samples/Sec: 78.24
+[2025-06-09 06:18:14] (step=0261000) Train Loss: 0.1079, Train Steps/Sec: 2.19, Samples/Sec: 78.66
+[2025-06-09 06:19:00] (step=0261100) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 06:19:46] (step=0261200) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 06:20:32] (step=0261300) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 06:21:18] (step=0261400) Train Loss: 0.1046, Train Steps/Sec: 2.17, Samples/Sec: 78.24
+[2025-06-09 06:22:04] (step=0261500) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 06:22:50] (step=0261600) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 06:23:35] (step=0261700) Train Loss: 0.1140, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 06:24:21] (step=0261800) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 06:25:07] (step=0261900) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 06:25:53] (step=0262000) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 06:26:07] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 06:26:53] (step=0262100) Train Loss: 0.1093, Train Steps/Sec: 1.67, Samples/Sec: 60.04
+[2025-06-09 06:27:39] (step=0262200) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 06:28:25] (step=0262300) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 06:29:10] (step=0262400) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 06:29:56] (step=0262500) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 06:30:42] (step=0262600) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 06:31:28] (step=0262700) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 06:32:14] (step=0262800) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 06:33:00] (step=0262900) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 06:33:45] (step=0263000) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 06:34:31] (step=0263100) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 06:35:17] (step=0263200) Train Loss: 0.1131, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 06:36:03] (step=0263300) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 06:36:49] (step=0263400) Train Loss: 0.1049, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-06-09 06:37:35] (step=0263500) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 06:38:21] (step=0263600) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 06:39:07] (step=0263700) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 06:39:52] (step=0263800) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 06:40:38] (step=0263900) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 06:41:24] (step=0264000) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 06:41:37] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 06:42:23] (step=0264100) Train Loss: 0.1072, Train Steps/Sec: 1.68, Samples/Sec: 60.53
+[2025-06-09 06:43:09] (step=0264200) Train Loss: 0.0996, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+[2025-06-09 06:43:55] (step=0264300) Train Loss: 0.1111, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 06:44:41] (step=0264400) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 06:45:27] (step=0264500) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 06:46:12] (step=0264600) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 06:46:58] (step=0264700) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 06:47:44] (step=0264800) Train Loss: 0.1164, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-06-09 06:48:30] (step=0264900) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 06:49:16] (step=0265000) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 06:50:01] (step=0265100) Train Loss: 0.1041, Train Steps/Sec: 2.19, Samples/Sec: 79.00
+[2025-06-09 06:50:47] (step=0265200) Train Loss: 0.1096, Train Steps/Sec: 2.20, Samples/Sec: 79.02
+[2025-06-09 06:51:33] (step=0265300) Train Loss: 0.1096, Train Steps/Sec: 2.19, Samples/Sec: 78.93
+[2025-06-09 06:52:19] (step=0265400) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 06:53:04] (step=0265500) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 06:53:50] (step=0265600) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 06:54:36] (step=0265700) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 06:55:22] (step=0265800) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 06:55:49] Beginning epoch 18...
+[2025-06-09 06:56:08] (step=0265900) Train Loss: 0.1066, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 06:56:54] (step=0266000) Train Loss: 0.1146, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 06:57:07] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 06:57:54] (step=0266100) Train Loss: 0.1041, Train Steps/Sec: 1.68, Samples/Sec: 60.39
+[2025-06-09 06:58:39] (step=0266200) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 06:59:25] (step=0266300) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 07:00:11] (step=0266400) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 07:00:57] (step=0266500) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 07:01:43] (step=0266600) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 07:02:28] (step=0266700) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 07:03:14] (step=0266800) Train Loss: 0.1092, Train Steps/Sec: 2.17, Samples/Sec: 78.25
+[2025-06-09 07:04:00] (step=0266900) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 07:04:46] (step=0267000) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 07:05:32] (step=0267100) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 07:06:18] (step=0267200) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 07:07:04] (step=0267300) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 07:07:49] (step=0267400) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 07:08:36] (step=0267500) Train Loss: 0.1066, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-06-09 07:09:21] (step=0267600) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 07:10:07] (step=0267700) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 07:10:53] (step=0267800) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 07:11:39] (step=0267900) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 07:12:25] (step=0268000) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 07:12:38] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 07:13:24] (step=0268100) Train Loss: 0.1068, Train Steps/Sec: 1.68, Samples/Sec: 60.46
+[2025-06-09 07:14:10] (step=0268200) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 07:14:56] (step=0268300) Train Loss: 0.1027, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 07:15:42] (step=0268400) Train Loss: 0.1029, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 07:16:27] (step=0268500) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 07:17:13] (step=0268600) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 07:17:59] (step=0268700) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 07:18:45] (step=0268800) Train Loss: 0.1114, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-06-09 07:19:31] (step=0268900) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 07:20:17] (step=0269000) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 07:21:03] (step=0269100) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 07:21:49] (step=0269200) Train Loss: 0.1015, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 07:22:34] (step=0269300) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 07:23:20] (step=0269400) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 07:24:06] (step=0269500) Train Loss: 0.1076, Train Steps/Sec: 2.17, Samples/Sec: 78.23
+[2025-06-09 07:24:52] (step=0269600) Train Loss: 0.1127, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 07:25:38] (step=0269700) Train Loss: 0.1035, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 07:26:24] (step=0269800) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 07:27:10] (step=0269900) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 07:27:56] (step=0270000) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 07:28:10] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 07:28:12] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 07:30:17] (step=0270000) Perceptual Loss: 0.1540, Eval Time: 127.21
+[2025-06-09 07:31:03] (step=0270100) Train Loss: 0.1059, Train Steps/Sec: 0.53, Samples/Sec: 19.21
+[2025-06-09 07:31:49] (step=0270200) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.66
+[2025-06-09 07:32:34] (step=0270300) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 07:33:20] (step=0270400) Train Loss: 0.1152, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 07:34:06] (step=0270500) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 07:34:52] (step=0270600) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 07:35:38] (step=0270700) Train Loss: 0.1141, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 07:36:24] (step=0270800) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 07:37:10] (step=0270900) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 07:37:56] (step=0271000) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 07:38:41] (step=0271100) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 07:39:27] (step=0271200) Train Loss: 0.1121, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 07:40:13] (step=0271300) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 07:40:59] (step=0271400) Train Loss: 0.1056, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-06-09 07:41:45] (step=0271500) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 07:42:31] (step=0271600) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 07:43:17] (step=0271700) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 07:44:03] (step=0271800) Train Loss: 0.1009, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 07:44:48] (step=0271900) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 07:45:34] (step=0272000) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 07:45:49] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 07:46:35] (step=0272100) Train Loss: 0.1029, Train Steps/Sec: 1.66, Samples/Sec: 59.73
+[2025-06-09 07:47:20] (step=0272200) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 07:48:06] (step=0272300) Train Loss: 0.1126, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 07:48:52] (step=0272400) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 07:49:38] (step=0272500) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 07:50:24] (step=0272600) Train Loss: 0.1123, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 07:51:10] (step=0272700) Train Loss: 0.1119, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 07:51:56] (step=0272800) Train Loss: 0.0998, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 07:52:41] (step=0272900) Train Loss: 0.1043, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 07:53:27] (step=0273000) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 07:54:13] (step=0273100) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 07:54:59] (step=0273200) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 07:55:45] (step=0273300) Train Loss: 0.1163, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 07:56:31] (step=0273400) Train Loss: 0.1103, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-06-09 07:57:17] (step=0273500) Train Loss: 0.1122, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 07:58:03] (step=0273600) Train Loss: 0.1130, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 07:58:48] (step=0273700) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 07:59:34] (step=0273800) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 08:00:20] (step=0273900) Train Loss: 0.1131, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 08:01:06] (step=0274000) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 08:01:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 08:02:06] (step=0274100) Train Loss: 0.1051, Train Steps/Sec: 1.66, Samples/Sec: 59.85
+[2025-06-09 08:02:52] (step=0274200) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 08:03:38] (step=0274300) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 08:04:24] (step=0274400) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 08:05:09] (step=0274500) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 08:05:55] (step=0274600) Train Loss: 0.1032, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 08:06:41] (step=0274700) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 08:07:27] (step=0274800) Train Loss: 0.1102, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-06-09 08:08:13] (step=0274900) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 08:08:59] (step=0275000) Train Loss: 0.1034, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 08:09:45] (step=0275100) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 08:10:30] (step=0275200) Train Loss: 0.1095, Train Steps/Sec: 2.19, Samples/Sec: 79.01
+[2025-06-09 08:11:16] (step=0275300) Train Loss: 0.1099, Train Steps/Sec: 2.20, Samples/Sec: 79.03
+[2025-06-09 08:12:02] (step=0275400) Train Loss: 0.1092, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 08:12:48] (step=0275500) Train Loss: 0.1023, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 08:13:33] (step=0275600) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 08:14:19] (step=0275700) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 08:15:05] (step=0275800) Train Loss: 0.1030, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 08:15:51] (step=0275900) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 08:16:37] (step=0276000) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 08:16:50] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 08:17:37] (step=0276100) Train Loss: 0.1111, Train Steps/Sec: 1.67, Samples/Sec: 60.26
+[2025-06-09 08:18:22] (step=0276200) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 08:19:08] (step=0276300) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 08:19:54] (step=0276400) Train Loss: 0.1129, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 08:20:40] (step=0276500) Train Loss: 0.0991, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 08:21:26] (step=0276600) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 08:22:12] (step=0276700) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 08:22:58] (step=0276800) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 08:23:44] (step=0276900) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 08:24:29] (step=0277000) Train Loss: 0.1121, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 08:25:15] (step=0277100) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 08:26:01] (step=0277200) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 08:26:47] (step=0277300) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 08:27:33] (step=0277400) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 08:28:19] (step=0277500) Train Loss: 0.1060, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-06-09 08:29:05] (step=0277600) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 08:29:51] (step=0277700) Train Loss: 0.1037, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 08:30:37] (step=0277800) Train Loss: 0.1140, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 08:31:22] (step=0277900) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 08:32:08] (step=0278000) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 08:32:22] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 08:33:08] (step=0278100) Train Loss: 0.1076, Train Steps/Sec: 1.68, Samples/Sec: 60.48
+[2025-06-09 08:33:54] (step=0278200) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 08:34:39] (step=0278300) Train Loss: 0.1048, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 08:35:25] (step=0278400) Train Loss: 0.1123, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 08:36:11] (step=0278500) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 08:36:57] (step=0278600) Train Loss: 0.1035, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 08:37:43] (step=0278700) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 08:38:29] (step=0278800) Train Loss: 0.1063, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 08:39:15] (step=0278900) Train Loss: 0.1136, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 08:40:01] (step=0279000) Train Loss: 0.1141, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 08:40:47] (step=0279100) Train Loss: 0.1051, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 08:41:32] (step=0279200) Train Loss: 0.1033, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 08:42:18] (step=0279300) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 08:43:04] (step=0279400) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 08:43:50] (step=0279500) Train Loss: 0.1046, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-06-09 08:44:36] (step=0279600) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 08:45:22] (step=0279700) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 08:46:08] (step=0279800) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 08:46:54] (step=0279900) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 08:47:40] (step=0280000) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 08:48:06] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0280000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 08:48:09] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 08:50:14] (step=0280000) Perceptual Loss: 0.1429, Eval Time: 127.71
+[2025-06-09 08:50:59] (step=0280100) Train Loss: 0.1026, Train Steps/Sec: 0.50, Samples/Sec: 18.03
+[2025-06-09 08:51:45] (step=0280200) Train Loss: 0.1009, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 08:52:31] (step=0280300) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 08:53:17] (step=0280400) Train Loss: 0.1080, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 08:54:03] (step=0280500) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 08:54:49] (step=0280600) Train Loss: 0.1019, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 08:55:02] Beginning epoch 19...
+[2025-06-09 08:55:35] (step=0280700) Train Loss: 0.1109, Train Steps/Sec: 2.16, Samples/Sec: 77.64
+[2025-06-09 08:56:21] (step=0280800) Train Loss: 0.1114, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-06-09 08:57:07] (step=0280900) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 08:57:53] (step=0281000) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 08:58:39] (step=0281100) Train Loss: 0.1022, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 08:59:24] (step=0281200) Train Loss: 0.0997, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 09:00:10] (step=0281300) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 09:00:56] (step=0281400) Train Loss: 0.1075, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 09:01:42] (step=0281500) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 09:02:28] (step=0281600) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 09:03:14] (step=0281700) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 09:04:00] (step=0281800) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 09:04:46] (step=0281900) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 09:05:32] (step=0282000) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 09:05:45] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 09:06:31] (step=0282100) Train Loss: 0.1056, Train Steps/Sec: 1.68, Samples/Sec: 60.49
+[2025-06-09 09:07:17] (step=0282200) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 09:08:03] (step=0282300) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 09:08:49] (step=0282400) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 09:09:35] (step=0282500) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 09:10:21] (step=0282600) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 09:11:07] (step=0282700) Train Loss: 0.1096, Train Steps/Sec: 2.17, Samples/Sec: 78.28
+[2025-06-09 09:11:53] (step=0282800) Train Loss: 0.1135, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-06-09 09:12:39] (step=0282900) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 09:13:24] (step=0283000) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:14:10] (step=0283100) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 09:14:56] (step=0283200) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 09:15:42] (step=0283300) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 09:16:28] (step=0283400) Train Loss: 0.1060, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 09:17:14] (step=0283500) Train Loss: 0.1075, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 09:18:00] (step=0283600) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 09:18:46] (step=0283700) Train Loss: 0.1118, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 09:19:32] (step=0283800) Train Loss: 0.1111, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 09:20:18] (step=0283900) Train Loss: 0.1129, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:21:03] (step=0284000) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 09:21:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 09:22:03] (step=0284100) Train Loss: 0.1111, Train Steps/Sec: 1.68, Samples/Sec: 60.44
+[2025-06-09 09:22:49] (step=0284200) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 09:23:35] (step=0284300) Train Loss: 0.1034, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 09:24:21] (step=0284400) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:25:07] (step=0284500) Train Loss: 0.1034, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 09:25:52] (step=0284600) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:26:38] (step=0284700) Train Loss: 0.1132, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 09:27:24] (step=0284800) Train Loss: 0.1120, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-06-09 09:28:10] (step=0284900) Train Loss: 0.1125, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 09:28:56] (step=0285000) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 09:29:42] (step=0285100) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 09:30:28] (step=0285200) Train Loss: 0.1053, Train Steps/Sec: 2.19, Samples/Sec: 78.81
+[2025-06-09 09:31:13] (step=0285300) Train Loss: 0.1153, Train Steps/Sec: 2.19, Samples/Sec: 78.98
+[2025-06-09 09:31:59] (step=0285400) Train Loss: 0.1059, Train Steps/Sec: 2.19, Samples/Sec: 78.69
+[2025-06-09 09:32:45] (step=0285500) Train Loss: 0.1106, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 09:33:31] (step=0285600) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 09:34:17] (step=0285700) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 09:35:03] (step=0285800) Train Loss: 0.1027, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 09:35:49] (step=0285900) Train Loss: 0.1146, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:36:34] (step=0286000) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 09:36:48] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 09:37:35] (step=0286100) Train Loss: 0.1066, Train Steps/Sec: 1.66, Samples/Sec: 59.91
+[2025-06-09 09:38:20] (step=0286200) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 09:39:06] (step=0286300) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 09:39:52] (step=0286400) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 09:40:38] (step=0286500) Train Loss: 0.1122, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:41:24] (step=0286600) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:42:10] (step=0286700) Train Loss: 0.1127, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 09:42:56] (step=0286800) Train Loss: 0.1145, Train Steps/Sec: 2.17, Samples/Sec: 78.20
+[2025-06-09 09:43:42] (step=0286900) Train Loss: 0.1159, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 09:44:28] (step=0287000) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 09:45:13] (step=0287100) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:45:59] (step=0287200) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 09:46:45] (step=0287300) Train Loss: 0.1030, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 09:47:31] (step=0287400) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 09:48:17] (step=0287500) Train Loss: 0.1130, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-06-09 09:49:03] (step=0287600) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 09:49:49] (step=0287700) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 09:50:35] (step=0287800) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 09:51:21] (step=0287900) Train Loss: 0.1026, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 09:52:07] (step=0288000) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 09:52:20] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 09:53:06] (step=0288100) Train Loss: 0.1067, Train Steps/Sec: 1.68, Samples/Sec: 60.58
+[2025-06-09 09:53:52] (step=0288200) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 09:54:38] (step=0288300) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 09:55:24] (step=0288400) Train Loss: 0.1000, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 09:56:10] (step=0288500) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 09:56:56] (step=0288600) Train Loss: 0.1123, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 09:57:42] (step=0288700) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 09:58:28] (step=0288800) Train Loss: 0.1106, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-06-09 09:59:14] (step=0288900) Train Loss: 0.1028, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 09:59:59] (step=0289000) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 10:00:45] (step=0289100) Train Loss: 0.1145, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 10:01:31] (step=0289200) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 10:02:17] (step=0289300) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 10:03:03] (step=0289400) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 10:03:49] (step=0289500) Train Loss: 0.1136, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 10:04:35] (step=0289600) Train Loss: 0.1140, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 10:05:21] (step=0289700) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 10:06:07] (step=0289800) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 10:06:52] (step=0289900) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 10:07:38] (step=0290000) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 10:07:52] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 10:07:55] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 10:09:59] (step=0290000) Perceptual Loss: 0.1643, Eval Time: 127.53
+[2025-06-09 10:10:46] (step=0290100) Train Loss: 0.1125, Train Steps/Sec: 0.53, Samples/Sec: 19.24
+[2025-06-09 10:11:31] (step=0290200) Train Loss: 0.1015, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 10:12:17] (step=0290300) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 10:13:03] (step=0290400) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 10:13:49] (step=0290500) Train Loss: 0.1138, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 10:14:35] (step=0290600) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 10:15:21] (step=0290700) Train Loss: 0.1120, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 10:16:07] (step=0290800) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 10:16:53] (step=0290900) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 10:17:39] (step=0291000) Train Loss: 0.1021, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 10:18:25] (step=0291100) Train Loss: 0.1127, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 10:19:10] (step=0291200) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 10:19:56] (step=0291300) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 10:20:43] (step=0291400) Train Loss: 0.1074, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-06-09 10:21:28] (step=0291500) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 10:22:14] (step=0291600) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 10:23:00] (step=0291700) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 10:23:46] (step=0291800) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 10:24:32] (step=0291900) Train Loss: 0.1141, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 10:25:18] (step=0292000) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 10:25:32] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 10:26:18] (step=0292100) Train Loss: 0.1103, Train Steps/Sec: 1.66, Samples/Sec: 59.81
+[2025-06-09 10:27:04] (step=0292200) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 10:27:50] (step=0292300) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 10:28:36] (step=0292400) Train Loss: 0.1088, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 10:29:22] (step=0292500) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 10:30:08] (step=0292600) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 10:30:54] (step=0292700) Train Loss: 0.1169, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 10:31:40] (step=0292800) Train Loss: 0.1082, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-06-09 10:32:26] (step=0292900) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 10:33:12] (step=0293000) Train Loss: 0.1113, Train Steps/Sec: 2.17, Samples/Sec: 78.28
+[2025-06-09 10:33:58] (step=0293100) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 10:34:43] (step=0293200) Train Loss: 0.1126, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 10:35:29] (step=0293300) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 10:36:16] (step=0293400) Train Loss: 0.1149, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-06-09 10:37:01] (step=0293500) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 10:37:47] (step=0293600) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 10:38:33] (step=0293700) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 10:39:19] (step=0293800) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 10:40:05] (step=0293900) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 10:40:51] (step=0294000) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 10:41:05] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 10:41:51] (step=0294100) Train Loss: 0.1084, Train Steps/Sec: 1.66, Samples/Sec: 59.81
+[2025-06-09 10:42:37] (step=0294200) Train Loss: 0.1019, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 10:43:23] (step=0294300) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 10:44:09] (step=0294400) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 10:44:55] (step=0294500) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 10:45:41] (step=0294600) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 10:46:27] (step=0294700) Train Loss: 0.1040, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-06-09 10:47:13] (step=0294800) Train Loss: 0.1139, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-06-09 10:47:59] (step=0294900) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 10:48:45] (step=0295000) Train Loss: 0.1017, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 10:49:31] (step=0295100) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 10:50:19] (step=0295200) Train Loss: 0.1134, Train Steps/Sec: 2.08, Samples/Sec: 74.94
+[2025-06-09 10:51:04] (step=0295300) Train Loss: 0.1063, Train Steps/Sec: 2.19, Samples/Sec: 78.92
+[2025-06-09 10:51:50] (step=0295400) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.65
+[2025-06-09 10:51:50] Beginning epoch 20...
+[2025-06-09 10:52:36] (step=0295500) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 10:53:22] (step=0295600) Train Loss: 0.1007, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 10:54:08] (step=0295700) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 10:54:54] (step=0295800) Train Loss: 0.1081, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-06-09 10:55:40] (step=0295900) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 10:56:26] (step=0296000) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 10:56:40] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 10:57:26] (step=0296100) Train Loss: 0.1051, Train Steps/Sec: 1.66, Samples/Sec: 59.81
+[2025-06-09 10:58:12] (step=0296200) Train Loss: 0.1028, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 10:58:58] (step=0296300) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 10:59:43] (step=0296400) Train Loss: 0.1141, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 11:00:29] (step=0296500) Train Loss: 0.1147, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 11:01:15] (step=0296600) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 11:02:01] (step=0296700) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 11:02:47] (step=0296800) Train Loss: 0.1043, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-06-09 11:03:33] (step=0296900) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 11:04:19] (step=0297000) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 11:05:05] (step=0297100) Train Loss: 0.1088, Train Steps/Sec: 2.17, Samples/Sec: 78.23
+[2025-06-09 11:05:51] (step=0297200) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 11:06:37] (step=0297300) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 11:07:23] (step=0297400) Train Loss: 0.1075, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 11:08:09] (step=0297500) Train Loss: 0.1021, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-06-09 11:08:55] (step=0297600) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:09:41] (step=0297700) Train Loss: 0.1031, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:10:27] (step=0297800) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 11:11:12] (step=0297900) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:11:58] (step=0298000) Train Loss: 0.1112, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 11:12:11] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 11:12:57] (step=0298100) Train Loss: 0.1051, Train Steps/Sec: 1.69, Samples/Sec: 60.79
+[2025-06-09 11:13:43] (step=0298200) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:14:29] (step=0298300) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:15:15] (step=0298400) Train Loss: 0.1049, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:16:01] (step=0298500) Train Loss: 0.1121, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:16:47] (step=0298600) Train Loss: 0.1166, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 11:17:32] (step=0298700) Train Loss: 0.1107, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 11:18:19] (step=0298800) Train Loss: 0.1149, Train Steps/Sec: 2.17, Samples/Sec: 78.23
+[2025-06-09 11:19:04] (step=0298900) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:19:50] (step=0299000) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:20:36] (step=0299100) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 11:21:22] (step=0299200) Train Loss: 0.1051, Train Steps/Sec: 2.17, Samples/Sec: 78.20
+[2025-06-09 11:22:08] (step=0299300) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:22:54] (step=0299400) Train Loss: 0.1022, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 11:23:40] (step=0299500) Train Loss: 0.1070, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 11:24:26] (step=0299600) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 11:25:12] (step=0299700) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 11:25:57] (step=0299800) Train Loss: 0.1017, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:26:43] (step=0299900) Train Loss: 0.1123, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 11:27:29] (step=0300000) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 11:27:55] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0300000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 11:27:58] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 11:30:03] (step=0300000) Perceptual Loss: 0.1421, Eval Time: 128.19
+[2025-06-09 11:30:49] (step=0300100) Train Loss: 0.1076, Train Steps/Sec: 0.50, Samples/Sec: 18.04
+[2025-06-09 11:31:34] (step=0300200) Train Loss: 0.1107, Train Steps/Sec: 2.19, Samples/Sec: 78.73
+[2025-06-09 11:32:20] (step=0300300) Train Loss: 0.1150, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 11:33:06] (step=0300400) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 11:33:52] (step=0300500) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 11:34:38] (step=0300600) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:35:24] (step=0300700) Train Loss: 0.1021, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-06-09 11:36:10] (step=0300800) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 11:36:55] (step=0300900) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 11:37:41] (step=0301000) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:38:27] (step=0301100) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 11:39:13] (step=0301200) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:39:59] (step=0301300) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 11:40:45] (step=0301400) Train Loss: 0.1088, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-06-09 11:41:31] (step=0301500) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:42:17] (step=0301600) Train Loss: 0.1131, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 11:43:03] (step=0301700) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:43:48] (step=0301800) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:44:34] (step=0301900) Train Loss: 0.1111, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 11:45:20] (step=0302000) Train Loss: 0.1015, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 11:45:34] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 11:46:20] (step=0302100) Train Loss: 0.1087, Train Steps/Sec: 1.68, Samples/Sec: 60.54
+[2025-06-09 11:47:06] (step=0302200) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 11:47:51] (step=0302300) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 11:48:37] (step=0302400) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 11:49:23] (step=0302500) Train Loss: 0.1132, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:50:09] (step=0302600) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:50:55] (step=0302700) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 11:51:41] (step=0302800) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 11:52:27] (step=0302900) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 11:53:12] (step=0303000) Train Loss: 0.1143, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 11:53:58] (step=0303100) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 11:54:44] (step=0303200) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 11:55:30] (step=0303300) Train Loss: 0.1048, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 11:56:16] (step=0303400) Train Loss: 0.1054, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-06-09 11:57:02] (step=0303500) Train Loss: 0.0973, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:57:48] (step=0303600) Train Loss: 0.1048, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 11:58:34] (step=0303700) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 11:59:19] (step=0303800) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:00:05] (step=0303900) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:00:51] (step=0304000) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 12:01:05] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 12:01:51] (step=0304100) Train Loss: 0.1084, Train Steps/Sec: 1.67, Samples/Sec: 60.18
+[2025-06-09 12:02:37] (step=0304200) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:03:22] (step=0304300) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:04:08] (step=0304400) Train Loss: 0.1020, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 12:04:54] (step=0304500) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:05:40] (step=0304600) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 12:06:26] (step=0304700) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:07:12] (step=0304800) Train Loss: 0.1128, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-06-09 12:07:58] (step=0304900) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 12:08:44] (step=0305000) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 12:09:29] (step=0305100) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:10:15] (step=0305200) Train Loss: 0.1207, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:11:01] (step=0305300) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 12:11:47] (step=0305400) Train Loss: 0.1078, Train Steps/Sec: 2.19, Samples/Sec: 78.66
+[2025-06-09 12:12:32] (step=0305500) Train Loss: 0.1059, Train Steps/Sec: 2.19, Samples/Sec: 79.01
+[2025-06-09 12:13:18] (step=0305600) Train Loss: 0.1103, Train Steps/Sec: 2.19, Samples/Sec: 78.80
+[2025-06-09 12:14:04] (step=0305700) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 12:14:50] (step=0305800) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 12:15:36] (step=0305900) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 12:16:22] (step=0306000) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:16:35] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 12:17:22] (step=0306100) Train Loss: 0.1084, Train Steps/Sec: 1.67, Samples/Sec: 60.00
+[2025-06-09 12:18:07] (step=0306200) Train Loss: 0.1119, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 12:18:53] (step=0306300) Train Loss: 0.1150, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 12:19:39] (step=0306400) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 12:20:25] (step=0306500) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 12:21:11] (step=0306600) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 12:21:56] (step=0306700) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 12:22:43] (step=0306800) Train Loss: 0.1080, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 12:23:28] (step=0306900) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:24:14] (step=0307000) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:25:00] (step=0307100) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:25:46] (step=0307200) Train Loss: 0.1030, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 12:26:32] (step=0307300) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:27:18] (step=0307400) Train Loss: 0.1131, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 12:28:04] (step=0307500) Train Loss: 0.1054, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 12:28:49] (step=0307600) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 12:29:35] (step=0307700) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:30:21] (step=0307800) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 12:31:07] (step=0307900) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:31:53] (step=0308000) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 12:32:07] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 12:32:53] (step=0308100) Train Loss: 0.1026, Train Steps/Sec: 1.67, Samples/Sec: 60.12
+[2025-06-09 12:33:38] (step=0308200) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 12:34:24] (step=0308300) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 12:35:10] (step=0308400) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:35:56] (step=0308500) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 12:36:42] (step=0308600) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 12:37:28] (step=0308700) Train Loss: 0.1019, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:38:14] (step=0308800) Train Loss: 0.1042, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-06-09 12:39:00] (step=0308900) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 12:39:46] (step=0309000) Train Loss: 0.1049, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 12:40:31] (step=0309100) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 12:41:17] (step=0309200) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 12:42:03] (step=0309300) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 12:42:49] (step=0309400) Train Loss: 0.0986, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 12:43:35] (step=0309500) Train Loss: 0.1041, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 12:44:21] (step=0309600) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:45:07] (step=0309700) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 12:45:52] (step=0309800) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 12:46:38] (step=0309900) Train Loss: 0.1043, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 12:47:24] (step=0310000) Train Loss: 0.1112, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 12:47:38] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 12:47:41] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 12:49:46] (step=0310000) Perceptual Loss: 0.1482, Eval Time: 127.52
+[2025-06-09 12:50:32] (step=0310100) Train Loss: 0.1100, Train Steps/Sec: 0.53, Samples/Sec: 19.21
+[2025-06-09 12:51:04] Beginning epoch 21...
+[2025-06-09 12:51:18] (step=0310200) Train Loss: 0.1041, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-06-09 12:52:03] (step=0310300) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 12:52:49] (step=0310400) Train Loss: 0.1123, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 12:53:35] (step=0310500) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 12:54:21] (step=0310600) Train Loss: 0.1148, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 12:55:07] (step=0310700) Train Loss: 0.1098, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 12:55:53] (step=0310800) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 12:56:39] (step=0310900) Train Loss: 0.1123, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 12:57:24] (step=0311000) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.64
+[2025-06-09 12:58:10] (step=0311100) Train Loss: 0.1080, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 12:58:56] (step=0311200) Train Loss: 0.1164, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-06-09 12:59:42] (step=0311300) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 13:00:28] (step=0311400) Train Loss: 0.1044, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-06-09 13:01:14] (step=0311500) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 13:02:00] (step=0311600) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 13:02:46] (step=0311700) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 13:03:32] (step=0311800) Train Loss: 0.1023, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 13:04:18] (step=0311900) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 13:05:03] (step=0312000) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 13:05:17] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 13:06:03] (step=0312100) Train Loss: 0.1025, Train Steps/Sec: 1.67, Samples/Sec: 60.09
+[2025-06-09 13:06:49] (step=0312200) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 13:07:35] (step=0312300) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 13:08:21] (step=0312400) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 13:09:07] (step=0312500) Train Loss: 0.1126, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 13:09:52] (step=0312600) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 13:10:38] (step=0312700) Train Loss: 0.1042, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 13:11:24] (step=0312800) Train Loss: 0.1037, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 13:12:10] (step=0312900) Train Loss: 0.1107, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 13:12:56] (step=0313000) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 13:13:42] (step=0313100) Train Loss: 0.1043, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 13:14:27] (step=0313200) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 13:15:13] (step=0313300) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 13:15:59] (step=0313400) Train Loss: 0.1064, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-06-09 13:16:45] (step=0313500) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 13:17:31] (step=0313600) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 13:18:17] (step=0313700) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 13:19:03] (step=0313800) Train Loss: 0.1051, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 13:19:49] (step=0313900) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 13:20:34] (step=0314000) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 13:20:49] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 13:21:35] (step=0314100) Train Loss: 0.1058, Train Steps/Sec: 1.65, Samples/Sec: 59.25
+[2025-06-09 13:22:21] (step=0314200) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 13:23:07] (step=0314300) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 13:23:53] (step=0314400) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 13:24:39] (step=0314500) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 13:25:24] (step=0314600) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 13:26:10] (step=0314700) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 13:26:57] (step=0314800) Train Loss: 0.1069, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-06-09 13:27:42] (step=0314900) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 13:28:28] (step=0315000) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 13:29:14] (step=0315100) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 13:30:00] (step=0315200) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 13:30:46] (step=0315300) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 13:31:32] (step=0315400) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 13:32:17] (step=0315500) Train Loss: 0.1030, Train Steps/Sec: 2.19, Samples/Sec: 78.99
+[2025-06-09 13:33:03] (step=0315600) Train Loss: 0.1065, Train Steps/Sec: 2.19, Samples/Sec: 79.00
+[2025-06-09 13:33:48] (step=0315700) Train Loss: 0.1111, Train Steps/Sec: 2.19, Samples/Sec: 78.67
+[2025-06-09 13:34:34] (step=0315800) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 13:35:20] (step=0315900) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 13:36:06] (step=0316000) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 13:36:22] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 13:37:08] (step=0316100) Train Loss: 0.1046, Train Steps/Sec: 1.62, Samples/Sec: 58.31
+[2025-06-09 13:37:54] (step=0316200) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 13:38:39] (step=0316300) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 13:39:25] (step=0316400) Train Loss: 0.1024, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 13:40:11] (step=0316500) Train Loss: 0.1111, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 13:40:57] (step=0316600) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 13:41:43] (step=0316700) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 13:42:29] (step=0316800) Train Loss: 0.1118, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-06-09 13:43:15] (step=0316900) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 13:44:01] (step=0317000) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 13:44:47] (step=0317100) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 13:45:33] (step=0317200) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 13:46:18] (step=0317300) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 13:47:04] (step=0317400) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.30
+[2025-06-09 13:47:51] (step=0317500) Train Loss: 0.1089, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 13:48:37] (step=0317600) Train Loss: 0.1068, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-06-09 13:49:23] (step=0317700) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 13:50:09] (step=0317800) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 13:50:54] (step=0317900) Train Loss: 0.1027, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 13:51:40] (step=0318000) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 13:51:56] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 13:52:42] (step=0318100) Train Loss: 0.1121, Train Steps/Sec: 1.62, Samples/Sec: 58.21
+[2025-06-09 13:53:28] (step=0318200) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 13:54:14] (step=0318300) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 13:55:00] (step=0318400) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 13:55:45] (step=0318500) Train Loss: 0.1121, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 13:56:31] (step=0318600) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 13:57:17] (step=0318700) Train Loss: 0.1013, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 13:58:03] (step=0318800) Train Loss: 0.1056, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-06-09 13:58:49] (step=0318900) Train Loss: 0.1122, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 13:59:35] (step=0319000) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 14:00:21] (step=0319100) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:01:07] (step=0319200) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:01:53] (step=0319300) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 14:02:39] (step=0319400) Train Loss: 0.1139, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 14:03:25] (step=0319500) Train Loss: 0.1073, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-06-09 14:04:11] (step=0319600) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 14:04:56] (step=0319700) Train Loss: 0.1051, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 14:05:42] (step=0319800) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 14:06:28] (step=0319900) Train Loss: 0.1033, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:07:14] (step=0320000) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 14:07:41] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0320000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 14:07:44] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 14:09:49] (step=0320000) Perceptual Loss: 0.1486, Eval Time: 128.23
+[2025-06-09 14:10:35] (step=0320100) Train Loss: 0.1084, Train Steps/Sec: 0.50, Samples/Sec: 17.88
+[2025-06-09 14:11:21] (step=0320200) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 14:12:07] (step=0320300) Train Loss: 0.1020, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:12:53] (step=0320400) Train Loss: 0.1024, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 14:13:39] (step=0320500) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 14:14:25] (step=0320600) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 14:15:11] (step=0320700) Train Loss: 0.1071, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-06-09 14:15:57] (step=0320800) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 14:16:43] (step=0320900) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 14:17:28] (step=0321000) Train Loss: 0.1034, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:18:14] (step=0321100) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 14:19:00] (step=0321200) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 14:19:46] (step=0321300) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 14:20:32] (step=0321400) Train Loss: 0.1071, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 14:21:18] (step=0321500) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 14:22:04] (step=0321600) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 14:22:50] (step=0321700) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:23:36] (step=0321800) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 14:24:21] (step=0321900) Train Loss: 0.1049, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 14:25:07] (step=0322000) Train Loss: 0.1080, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 14:25:21] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 14:26:08] (step=0322100) Train Loss: 0.1043, Train Steps/Sec: 1.66, Samples/Sec: 59.75
+[2025-06-09 14:26:53] (step=0322200) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 14:27:39] (step=0322300) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 14:28:25] (step=0322400) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 14:29:11] (step=0322500) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:29:57] (step=0322600) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 14:30:43] (step=0322700) Train Loss: 0.1115, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 14:31:29] (step=0322800) Train Loss: 0.1125, Train Steps/Sec: 2.17, Samples/Sec: 78.24
+[2025-06-09 14:32:15] (step=0322900) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 14:33:00] (step=0323000) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 14:33:46] (step=0323100) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:34:32] (step=0323200) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 14:35:18] (step=0323300) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 14:36:04] (step=0323400) Train Loss: 0.1059, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-06-09 14:36:50] (step=0323500) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 14:37:36] (step=0323600) Train Loss: 0.1156, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 14:38:22] (step=0323700) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:39:08] (step=0323800) Train Loss: 0.1132, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:39:54] (step=0323900) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:40:40] (step=0324000) Train Loss: 0.1137, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 14:40:55] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 14:41:41] (step=0324100) Train Loss: 0.1116, Train Steps/Sec: 1.63, Samples/Sec: 58.62
+[2025-06-09 14:42:27] (step=0324200) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 14:43:13] (step=0324300) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 14:43:59] (step=0324400) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 14:44:44] (step=0324500) Train Loss: 0.1011, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 14:45:30] (step=0324600) Train Loss: 0.1002, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 14:46:16] (step=0324700) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 14:47:02] (step=0324800) Train Loss: 0.1015, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-06-09 14:47:48] (step=0324900) Train Loss: 0.1121, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 14:48:07] Beginning epoch 22...
+[2025-06-09 14:48:34] (step=0325000) Train Loss: 0.1057, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 14:49:20] (step=0325100) Train Loss: 0.1073, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 14:50:06] (step=0325200) Train Loss: 0.1025, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 14:50:52] (step=0325300) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 14:51:38] (step=0325400) Train Loss: 0.1105, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-06-09 14:52:24] (step=0325500) Train Loss: 0.1111, Train Steps/Sec: 2.19, Samples/Sec: 78.98
+[2025-06-09 14:53:09] (step=0325600) Train Loss: 0.1146, Train Steps/Sec: 2.19, Samples/Sec: 78.99
+[2025-06-09 14:53:55] (step=0325700) Train Loss: 0.1205, Train Steps/Sec: 2.19, Samples/Sec: 78.75
+[2025-06-09 14:54:41] (step=0325800) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 14:55:27] (step=0325900) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 14:56:13] (step=0326000) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 14:56:28] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 14:57:15] (step=0326100) Train Loss: 0.1105, Train Steps/Sec: 1.61, Samples/Sec: 58.11
+[2025-06-09 14:58:01] (step=0326200) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 14:58:46] (step=0326300) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 14:59:32] (step=0326400) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 15:00:18] (step=0326500) Train Loss: 0.1080, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 15:01:04] (step=0326600) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 15:01:50] (step=0326700) Train Loss: 0.1029, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 15:02:36] (step=0326800) Train Loss: 0.1089, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-06-09 15:03:22] (step=0326900) Train Loss: 0.1148, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 15:04:08] (step=0327000) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 15:04:54] (step=0327100) Train Loss: 0.1171, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 15:05:40] (step=0327200) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 15:06:25] (step=0327300) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 15:07:11] (step=0327400) Train Loss: 0.1086, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 15:07:58] (step=0327500) Train Loss: 0.1064, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-06-09 15:08:43] (step=0327600) Train Loss: 0.1129, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 15:09:29] (step=0327700) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 15:10:15] (step=0327800) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 15:11:01] (step=0327900) Train Loss: 0.1136, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 15:11:47] (step=0328000) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 15:12:01] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 15:12:47] (step=0328100) Train Loss: 0.1159, Train Steps/Sec: 1.67, Samples/Sec: 60.01
+[2025-06-09 15:13:33] (step=0328200) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 15:14:19] (step=0328300) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 15:15:04] (step=0328400) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 15:15:50] (step=0328500) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 15:16:36] (step=0328600) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 15:17:22] (step=0328700) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 15:18:08] (step=0328800) Train Loss: 0.1031, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-06-09 15:18:54] (step=0328900) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 15:19:40] (step=0329000) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 15:20:26] (step=0329100) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 15:21:12] (step=0329200) Train Loss: 0.1145, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 15:21:57] (step=0329300) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 15:22:43] (step=0329400) Train Loss: 0.1079, Train Steps/Sec: 2.17, Samples/Sec: 78.30
+[2025-06-09 15:23:29] (step=0329500) Train Loss: 0.1100, Train Steps/Sec: 2.17, Samples/Sec: 78.20
+[2025-06-09 15:24:15] (step=0329600) Train Loss: 0.1136, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 15:25:01] (step=0329700) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 15:25:47] (step=0329800) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 15:26:33] (step=0329900) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 15:27:19] (step=0330000) Train Loss: 0.1155, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 15:27:33] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 15:27:36] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 15:29:41] (step=0330000) Perceptual Loss: 0.1355, Eval Time: 128.22
+[2025-06-09 15:30:27] (step=0330100) Train Loss: 0.1073, Train Steps/Sec: 0.53, Samples/Sec: 19.12
+[2025-06-09 15:31:13] (step=0330200) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 15:31:59] (step=0330300) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 15:32:45] (step=0330400) Train Loss: 0.1141, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 15:33:30] (step=0330500) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 15:34:16] (step=0330600) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 15:35:02] (step=0330700) Train Loss: 0.1129, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-06-09 15:35:48] (step=0330800) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 15:36:34] (step=0330900) Train Loss: 0.1129, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 15:37:20] (step=0331000) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 15:38:06] (step=0331100) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 15:38:52] (step=0331200) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 15:39:38] (step=0331300) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 15:40:24] (step=0331400) Train Loss: 0.1054, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 15:41:10] (step=0331500) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 15:41:55] (step=0331600) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 15:42:41] (step=0331700) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 15:43:27] (step=0331800) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 15:44:13] (step=0331900) Train Loss: 0.1038, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 15:44:59] (step=0332000) Train Loss: 0.1111, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 15:45:14] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 15:46:00] (step=0332100) Train Loss: 0.1077, Train Steps/Sec: 1.64, Samples/Sec: 58.89
+[2025-06-09 15:46:46] (step=0332200) Train Loss: 0.1019, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 15:47:32] (step=0332300) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 15:48:18] (step=0332400) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 15:49:04] (step=0332500) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 15:49:50] (step=0332600) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 15:50:36] (step=0332700) Train Loss: 0.1124, Train Steps/Sec: 2.16, Samples/Sec: 77.68
+[2025-06-09 15:51:22] (step=0332800) Train Loss: 0.1049, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-06-09 15:52:08] (step=0332900) Train Loss: 0.1062, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-06-09 15:52:54] (step=0333000) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 15:53:40] (step=0333100) Train Loss: 0.1131, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 15:54:26] (step=0333200) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 15:55:12] (step=0333300) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 15:55:58] (step=0333400) Train Loss: 0.1125, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-06-09 15:56:44] (step=0333500) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 15:57:30] (step=0333600) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 15:58:16] (step=0333700) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 15:59:02] (step=0333800) Train Loss: 0.1033, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 15:59:48] (step=0333900) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 16:00:34] (step=0334000) Train Loss: 0.1119, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 16:00:49] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 16:01:35] (step=0334100) Train Loss: 0.1159, Train Steps/Sec: 1.63, Samples/Sec: 58.70
+[2025-06-09 16:02:21] (step=0334200) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 16:03:07] (step=0334300) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 16:03:53] (step=0334400) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 16:04:38] (step=0334500) Train Loss: 0.1003, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 16:05:24] (step=0334600) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 16:06:10] (step=0334700) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 16:06:56] (step=0334800) Train Loss: 0.1056, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-06-09 16:07:42] (step=0334900) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 16:08:28] (step=0335000) Train Loss: 0.1157, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 16:09:14] (step=0335100) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 16:10:00] (step=0335200) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 16:10:46] (step=0335300) Train Loss: 0.1032, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 16:11:32] (step=0335400) Train Loss: 0.1041, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-06-09 16:12:18] (step=0335500) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 16:13:04] (step=0335600) Train Loss: 0.1078, Train Steps/Sec: 2.19, Samples/Sec: 78.96
+[2025-06-09 16:13:49] (step=0335700) Train Loss: 0.1126, Train Steps/Sec: 2.19, Samples/Sec: 78.92
+[2025-06-09 16:14:35] (step=0335800) Train Loss: 0.1140, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 16:15:21] (step=0335900) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 16:16:07] (step=0336000) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 16:16:21] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 16:17:07] (step=0336100) Train Loss: 0.1049, Train Steps/Sec: 1.66, Samples/Sec: 59.81
+[2025-06-09 16:17:53] (step=0336200) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 16:18:39] (step=0336300) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 16:19:25] (step=0336400) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 16:20:11] (step=0336500) Train Loss: 0.1037, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 16:20:57] (step=0336600) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 16:21:42] (step=0336700) Train Loss: 0.1151, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 16:22:29] (step=0336800) Train Loss: 0.1095, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-06-09 16:23:14] (step=0336900) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 16:24:00] (step=0337000) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 16:24:46] (step=0337100) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 16:25:32] (step=0337200) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 16:26:18] (step=0337300) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 16:27:04] (step=0337400) Train Loss: 0.1067, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 16:27:50] (step=0337500) Train Loss: 0.1133, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 16:28:36] (step=0337600) Train Loss: 0.1034, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 16:29:22] (step=0337700) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 16:30:08] (step=0337800) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 16:30:53] (step=0337900) Train Loss: 0.1030, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 16:31:39] (step=0338000) Train Loss: 0.1144, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 16:31:53] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 16:32:40] (step=0338100) Train Loss: 0.1116, Train Steps/Sec: 1.66, Samples/Sec: 59.75
+[2025-06-09 16:33:25] (step=0338200) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 16:34:11] (step=0338300) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 16:34:57] (step=0338400) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 16:35:43] (step=0338500) Train Loss: 0.1023, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 16:36:29] (step=0338600) Train Loss: 0.1144, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 16:37:15] (step=0338700) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 16:38:01] (step=0338800) Train Loss: 0.1031, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-06-09 16:38:47] (step=0338900) Train Loss: 0.1118, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 16:39:33] (step=0339000) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 16:40:19] (step=0339100) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 16:41:05] (step=0339200) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 16:41:50] (step=0339300) Train Loss: 0.0997, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 16:42:36] (step=0339400) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 16:43:22] (step=0339500) Train Loss: 0.1069, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 16:44:08] (step=0339600) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 16:44:54] (step=0339700) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 16:44:59] Beginning epoch 23...
+[2025-06-09 16:45:40] (step=0339800) Train Loss: 0.1068, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-06-09 16:46:26] (step=0339900) Train Loss: 0.1130, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 16:47:12] (step=0340000) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 16:47:38] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0340000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 16:47:41] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 16:49:46] (step=0340000) Perceptual Loss: 0.1411, Eval Time: 127.65
+[2025-06-09 16:50:32] (step=0340100) Train Loss: 0.1089, Train Steps/Sec: 0.50, Samples/Sec: 17.99
+[2025-06-09 16:51:18] (step=0340200) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 16:52:04] (step=0340300) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 16:52:50] (step=0340400) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 16:53:35] (step=0340500) Train Loss: 0.1002, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 16:54:21] (step=0340600) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 16:55:07] (step=0340700) Train Loss: 0.1039, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-06-09 16:55:54] (step=0340800) Train Loss: 0.1077, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 16:56:39] (step=0340900) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 16:57:25] (step=0341000) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 16:58:11] (step=0341100) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 16:58:57] (step=0341200) Train Loss: 0.1136, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 16:59:43] (step=0341300) Train Loss: 0.1134, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 17:00:29] (step=0341400) Train Loss: 0.1071, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-06-09 17:01:15] (step=0341500) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 17:02:01] (step=0341600) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 17:02:47] (step=0341700) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 17:03:32] (step=0341800) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 17:04:18] (step=0341900) Train Loss: 0.1121, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 17:05:04] (step=0342000) Train Loss: 0.1012, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 17:05:19] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 17:06:05] (step=0342100) Train Loss: 0.1031, Train Steps/Sec: 1.65, Samples/Sec: 59.55
+[2025-06-09 17:06:51] (step=0342200) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 17:07:36] (step=0342300) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 17:08:22] (step=0342400) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 17:09:08] (step=0342500) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 17:09:54] (step=0342600) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 17:10:40] (step=0342700) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 17:11:26] (step=0342800) Train Loss: 0.1052, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 17:12:12] (step=0342900) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 17:12:58] (step=0343000) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 17:13:44] (step=0343100) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 17:14:29] (step=0343200) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 17:15:15] (step=0343300) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 17:16:01] (step=0343400) Train Loss: 0.1113, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-06-09 17:16:47] (step=0343500) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 17:17:33] (step=0343600) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 17:18:19] (step=0343700) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 17:19:05] (step=0343800) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 17:19:51] (step=0343900) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 17:20:37] (step=0344000) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 17:20:51] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 17:21:37] (step=0344100) Train Loss: 0.1064, Train Steps/Sec: 1.66, Samples/Sec: 59.68
+[2025-06-09 17:22:23] (step=0344200) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 17:23:09] (step=0344300) Train Loss: 0.1001, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 17:23:54] (step=0344400) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 17:24:40] (step=0344500) Train Loss: 0.1112, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 17:25:26] (step=0344600) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 17:26:12] (step=0344700) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 17:26:58] (step=0344800) Train Loss: 0.1068, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-06-09 17:27:44] (step=0344900) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 17:28:30] (step=0345000) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 17:29:16] (step=0345100) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 17:30:02] (step=0345200) Train Loss: 0.1031, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 17:30:48] (step=0345300) Train Loss: 0.1144, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 17:31:33] (step=0345400) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 17:32:19] (step=0345500) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 17:33:05] (step=0345600) Train Loss: 0.1044, Train Steps/Sec: 2.19, Samples/Sec: 78.98
+[2025-06-09 17:33:51] (step=0345700) Train Loss: 0.1045, Train Steps/Sec: 2.19, Samples/Sec: 79.00
+[2025-06-09 17:34:36] (step=0345800) Train Loss: 0.1056, Train Steps/Sec: 2.19, Samples/Sec: 78.75
+[2025-06-09 17:35:22] (step=0345900) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 17:36:08] (step=0346000) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 17:36:22] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 17:37:08] (step=0346100) Train Loss: 0.1037, Train Steps/Sec: 1.66, Samples/Sec: 59.76
+[2025-06-09 17:37:54] (step=0346200) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 17:38:40] (step=0346300) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 17:39:26] (step=0346400) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 17:40:12] (step=0346500) Train Loss: 0.1026, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 17:40:57] (step=0346600) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 17:41:43] (step=0346700) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 17:42:30] (step=0346800) Train Loss: 0.1128, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-06-09 17:43:15] (step=0346900) Train Loss: 0.0986, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 17:44:01] (step=0347000) Train Loss: 0.1032, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 17:44:47] (step=0347100) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 17:45:33] (step=0347200) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 17:46:19] (step=0347300) Train Loss: 0.1095, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-06-09 17:47:05] (step=0347400) Train Loss: 0.1094, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 17:47:51] (step=0347500) Train Loss: 0.1072, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 17:48:37] (step=0347600) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 17:49:23] (step=0347700) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 17:50:09] (step=0347800) Train Loss: 0.1032, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 17:50:55] (step=0347900) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 17:51:41] (step=0348000) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 17:51:55] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 17:52:41] (step=0348100) Train Loss: 0.1107, Train Steps/Sec: 1.66, Samples/Sec: 59.63
+[2025-06-09 17:53:27] (step=0348200) Train Loss: 0.1078, Train Steps/Sec: 2.17, Samples/Sec: 78.30
+[2025-06-09 17:54:13] (step=0348300) Train Loss: 0.1031, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 17:54:59] (step=0348400) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 17:55:45] (step=0348500) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 17:56:30] (step=0348600) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 17:57:16] (step=0348700) Train Loss: 0.1038, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 17:58:02] (step=0348800) Train Loss: 0.1137, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-06-09 17:58:48] (step=0348900) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 17:59:34] (step=0349000) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 18:00:20] (step=0349100) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 18:01:06] (step=0349200) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 18:01:52] (step=0349300) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 18:02:38] (step=0349400) Train Loss: 0.1124, Train Steps/Sec: 2.17, Samples/Sec: 78.28
+[2025-06-09 18:03:24] (step=0349500) Train Loss: 0.1062, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-06-09 18:04:10] (step=0349600) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 18:04:55] (step=0349700) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 18:05:41] (step=0349800) Train Loss: 0.1033, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 18:06:27] (step=0349900) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 18:07:13] (step=0350000) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 18:07:27] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 18:07:30] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 18:09:35] (step=0350000) Perceptual Loss: 0.1314, Eval Time: 127.67
+[2025-06-09 18:10:21] (step=0350100) Train Loss: 0.1055, Train Steps/Sec: 0.53, Samples/Sec: 19.16
+[2025-06-09 18:11:07] (step=0350200) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 18:11:53] (step=0350300) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 18:12:38] (step=0350400) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 18:13:24] (step=0350500) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 18:14:10] (step=0350600) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 18:14:57] (step=0350700) Train Loss: 0.1074, Train Steps/Sec: 2.16, Samples/Sec: 77.62
+[2025-06-09 18:15:42] (step=0350800) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 18:16:28] (step=0350900) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 18:17:14] (step=0351000) Train Loss: 0.1118, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 18:18:00] (step=0351100) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 18:18:46] (step=0351200) Train Loss: 0.1136, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 18:19:32] (step=0351300) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 18:20:18] (step=0351400) Train Loss: 0.1054, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-06-09 18:21:04] (step=0351500) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 18:21:50] (step=0351600) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 18:22:36] (step=0351700) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 18:23:21] (step=0351800) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 18:24:07] (step=0351900) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 18:24:53] (step=0352000) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 18:25:08] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 18:25:54] (step=0352100) Train Loss: 0.1087, Train Steps/Sec: 1.63, Samples/Sec: 58.68
+[2025-06-09 18:26:40] (step=0352200) Train Loss: 0.0977, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 18:27:26] (step=0352300) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 18:28:12] (step=0352400) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 18:28:58] (step=0352500) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 18:29:44] (step=0352600) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 18:30:30] (step=0352700) Train Loss: 0.1156, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 18:31:16] (step=0352800) Train Loss: 0.1010, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-09 18:32:02] (step=0352900) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 18:32:47] (step=0353000) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 18:33:33] (step=0353100) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 18:34:19] (step=0353200) Train Loss: 0.1145, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 18:35:05] (step=0353300) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 18:35:51] (step=0353400) Train Loss: 0.1087, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-06-09 18:36:37] (step=0353500) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 18:37:23] (step=0353600) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 18:38:09] (step=0353700) Train Loss: 0.1038, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 18:38:55] (step=0353800) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 18:39:41] (step=0353900) Train Loss: 0.1050, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-06-09 18:40:27] (step=0354000) Train Loss: 0.1051, Train Steps/Sec: 2.16, Samples/Sec: 77.68
+[2025-06-09 18:40:42] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 18:41:28] (step=0354100) Train Loss: 0.1091, Train Steps/Sec: 1.66, Samples/Sec: 59.63
+[2025-06-09 18:42:14] (step=0354200) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 18:42:59] (step=0354300) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 18:43:45] (step=0354400) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 18:44:22] Beginning epoch 24...
+[2025-06-09 18:44:31] (step=0354500) Train Loss: 0.1030, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-06-09 18:45:17] (step=0354600) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 18:46:03] (step=0354700) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 18:46:49] (step=0354800) Train Loss: 0.1136, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-06-09 18:47:35] (step=0354900) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 18:48:21] (step=0355000) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 18:49:07] (step=0355100) Train Loss: 0.1143, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 18:49:53] (step=0355200) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 18:50:39] (step=0355300) Train Loss: 0.1019, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 18:51:25] (step=0355400) Train Loss: 0.1099, Train Steps/Sec: 2.16, Samples/Sec: 77.67
+[2025-06-09 18:52:11] (step=0355500) Train Loss: 0.1166, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 18:52:57] (step=0355600) Train Loss: 0.1070, Train Steps/Sec: 2.19, Samples/Sec: 78.73
+[2025-06-09 18:53:42] (step=0355700) Train Loss: 0.1072, Train Steps/Sec: 2.19, Samples/Sec: 78.98
+[2025-06-09 18:54:28] (step=0355800) Train Loss: 0.1110, Train Steps/Sec: 2.19, Samples/Sec: 78.99
+[2025-06-09 18:55:14] (step=0355900) Train Loss: 0.0978, Train Steps/Sec: 2.18, Samples/Sec: 78.66
+[2025-06-09 18:55:59] (step=0356000) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 18:56:14] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 18:57:00] (step=0356100) Train Loss: 0.1078, Train Steps/Sec: 1.66, Samples/Sec: 59.62
+[2025-06-09 18:57:46] (step=0356200) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 18:58:32] (step=0356300) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 18:59:17] (step=0356400) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 19:00:03] (step=0356500) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 19:00:49] (step=0356600) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 19:01:35] (step=0356700) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 19:02:21] (step=0356800) Train Loss: 0.1061, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 19:03:07] (step=0356900) Train Loss: 0.1038, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 19:03:53] (step=0357000) Train Loss: 0.1010, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 19:04:39] (step=0357100) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 19:05:25] (step=0357200) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 19:06:10] (step=0357300) Train Loss: 0.0983, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 19:06:57] (step=0357400) Train Loss: 0.1054, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-06-09 19:07:43] (step=0357500) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 19:08:28] (step=0357600) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 19:09:14] (step=0357700) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 19:10:00] (step=0357800) Train Loss: 0.1135, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 19:10:46] (step=0357900) Train Loss: 0.1031, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 19:11:32] (step=0358000) Train Loss: 0.1012, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 19:11:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 19:12:33] (step=0358100) Train Loss: 0.1149, Train Steps/Sec: 1.63, Samples/Sec: 58.82
+[2025-06-09 19:13:19] (step=0358200) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 19:14:05] (step=0358300) Train Loss: 0.1134, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 19:14:51] (step=0358400) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 19:15:36] (step=0358500) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 19:16:22] (step=0358600) Train Loss: 0.1107, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 19:17:08] (step=0358700) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 19:17:54] (step=0358800) Train Loss: 0.1050, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-06-09 19:18:40] (step=0358900) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 19:19:26] (step=0359000) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 19:20:12] (step=0359100) Train Loss: 0.1112, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 19:20:58] (step=0359200) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 19:21:44] (step=0359300) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 19:22:30] (step=0359400) Train Loss: 0.1093, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 19:23:16] (step=0359500) Train Loss: 0.1079, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-06-09 19:24:01] (step=0359600) Train Loss: 0.1093, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 19:24:47] (step=0359700) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 19:25:33] (step=0359800) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 19:26:19] (step=0359900) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 19:27:05] (step=0360000) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 19:27:32] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0360000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 19:27:35] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 19:29:40] (step=0360000) Perceptual Loss: 0.1679, Eval Time: 127.77
+[2025-06-09 19:30:26] (step=0360100) Train Loss: 0.1039, Train Steps/Sec: 0.50, Samples/Sec: 17.90
+[2025-06-09 19:31:12] (step=0360200) Train Loss: 0.1138, Train Steps/Sec: 2.19, Samples/Sec: 78.68
+[2025-06-09 19:31:58] (step=0360300) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 19:32:43] (step=0360400) Train Loss: 0.1030, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 19:33:29] (step=0360500) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 19:34:15] (step=0360600) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 19:35:01] (step=0360700) Train Loss: 0.1092, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-06-09 19:35:47] (step=0360800) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 19:36:33] (step=0360900) Train Loss: 0.1050, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 19:37:19] (step=0361000) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 19:38:05] (step=0361100) Train Loss: 0.1107, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 19:38:51] (step=0361200) Train Loss: 0.1118, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 19:39:36] (step=0361300) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 19:40:23] (step=0361400) Train Loss: 0.1113, Train Steps/Sec: 2.17, Samples/Sec: 77.94
+[2025-06-09 19:41:08] (step=0361500) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 19:41:54] (step=0361600) Train Loss: 0.1099, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 19:42:40] (step=0361700) Train Loss: 0.1136, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 19:43:26] (step=0361800) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 19:44:12] (step=0361900) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 19:44:58] (step=0362000) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 19:45:13] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 19:45:59] (step=0362100) Train Loss: 0.1053, Train Steps/Sec: 1.62, Samples/Sec: 58.49
+[2025-06-09 19:46:45] (step=0362200) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 19:47:31] (step=0362300) Train Loss: 0.1022, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 19:48:17] (step=0362400) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 19:49:03] (step=0362500) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 19:49:48] (step=0362600) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 19:50:35] (step=0362700) Train Loss: 0.1067, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-06-09 19:51:21] (step=0362800) Train Loss: 0.1044, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-06-09 19:52:06] (step=0362900) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 19:52:52] (step=0363000) Train Loss: 0.1145, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 19:53:38] (step=0363100) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 19:54:24] (step=0363200) Train Loss: 0.0998, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 19:55:10] (step=0363300) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 19:55:56] (step=0363400) Train Loss: 0.1117, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 19:56:42] (step=0363500) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 19:57:28] (step=0363600) Train Loss: 0.1032, Train Steps/Sec: 2.18, Samples/Sec: 78.58
+[2025-06-09 19:58:14] (step=0363700) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 19:58:59] (step=0363800) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 19:59:45] (step=0363900) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 20:00:31] (step=0364000) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 20:00:46] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 20:01:32] (step=0364100) Train Loss: 0.1133, Train Steps/Sec: 1.65, Samples/Sec: 59.40
+[2025-06-09 20:02:18] (step=0364200) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 20:03:03] (step=0364300) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.56
+[2025-06-09 20:03:49] (step=0364400) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 20:04:35] (step=0364500) Train Loss: 0.1124, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 20:05:21] (step=0364600) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 20:06:07] (step=0364700) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 20:06:53] (step=0364800) Train Loss: 0.1065, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-06-09 20:07:39] (step=0364900) Train Loss: 0.1007, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 20:08:25] (step=0365000) Train Loss: 0.1112, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 20:09:11] (step=0365100) Train Loss: 0.1145, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 20:09:56] (step=0365200) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 20:10:42] (step=0365300) Train Loss: 0.1073, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 20:11:29] (step=0365400) Train Loss: 0.1064, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-06-09 20:12:14] (step=0365500) Train Loss: 0.1119, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 20:13:00] (step=0365600) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 20:13:46] (step=0365700) Train Loss: 0.1051, Train Steps/Sec: 2.19, Samples/Sec: 78.88
+[2025-06-09 20:14:32] (step=0365800) Train Loss: 0.0997, Train Steps/Sec: 2.19, Samples/Sec: 78.93
+[2025-06-09 20:15:17] (step=0365900) Train Loss: 0.1062, Train Steps/Sec: 2.19, Samples/Sec: 78.70
+[2025-06-09 20:16:03] (step=0366000) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 20:16:18] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 20:17:04] (step=0366100) Train Loss: 0.1138, Train Steps/Sec: 1.65, Samples/Sec: 59.44
+[2025-06-09 20:17:50] (step=0366200) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 20:18:35] (step=0366300) Train Loss: 0.1127, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 20:19:21] (step=0366400) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 20:20:07] (step=0366500) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 20:20:53] (step=0366600) Train Loss: 0.1005, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 20:21:39] (step=0366700) Train Loss: 0.1130, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 20:22:25] (step=0366800) Train Loss: 0.1113, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-06-09 20:23:11] (step=0366900) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 20:23:57] (step=0367000) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 20:24:43] (step=0367100) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 20:25:28] (step=0367200) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 20:26:14] (step=0367300) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 20:27:00] (step=0367400) Train Loss: 0.1058, Train Steps/Sec: 2.17, Samples/Sec: 78.24
+[2025-06-09 20:27:46] (step=0367500) Train Loss: 0.1135, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-06-09 20:28:32] (step=0367600) Train Loss: 0.1180, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 20:29:18] (step=0367700) Train Loss: 0.1038, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 20:30:04] (step=0367800) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 20:30:50] (step=0367900) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 20:31:36] (step=0368000) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 20:31:50] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 20:32:36] (step=0368100) Train Loss: 0.1084, Train Steps/Sec: 1.66, Samples/Sec: 59.84
+[2025-06-09 20:33:22] (step=0368200) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 20:34:08] (step=0368300) Train Loss: 0.1141, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 20:34:54] (step=0368400) Train Loss: 0.1094, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-06-09 20:35:40] (step=0368500) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 20:36:26] (step=0368600) Train Loss: 0.1042, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 20:37:12] (step=0368700) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 20:37:58] (step=0368800) Train Loss: 0.1092, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-06-09 20:38:44] (step=0368900) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 20:39:30] (step=0369000) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 20:40:15] (step=0369100) Train Loss: 0.1033, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 20:41:01] (step=0369200) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-09 20:41:24] Beginning epoch 25...
+[2025-06-09 20:41:48] (step=0369300) Train Loss: 0.1014, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-06-09 20:42:33] (step=0369400) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 20:43:20] (step=0369500) Train Loss: 0.1021, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 20:44:05] (step=0369600) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 20:44:51] (step=0369700) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 20:45:37] (step=0369800) Train Loss: 0.1010, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 20:46:23] (step=0369900) Train Loss: 0.1068, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 20:47:09] (step=0370000) Train Loss: 0.1103, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 20:47:24] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 20:47:27] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 20:49:32] (step=0370000) Perceptual Loss: 0.1508, Eval Time: 127.91
+[2025-06-09 20:50:18] (step=0370100) Train Loss: 0.1108, Train Steps/Sec: 0.53, Samples/Sec: 19.03
+[2025-06-09 20:51:04] (step=0370200) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.62
+[2025-06-09 20:51:50] (step=0370300) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 20:52:36] (step=0370400) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 20:53:22] (step=0370500) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 20:54:07] (step=0370600) Train Loss: 0.1132, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 20:54:54] (step=0370700) Train Loss: 0.1081, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-06-09 20:55:40] (step=0370800) Train Loss: 0.1079, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 20:56:25] (step=0370900) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 20:57:11] (step=0371000) Train Loss: 0.1019, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 20:57:57] (step=0371100) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 20:58:43] (step=0371200) Train Loss: 0.1155, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 20:59:29] (step=0371300) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 21:00:15] (step=0371400) Train Loss: 0.1107, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 21:01:01] (step=0371500) Train Loss: 0.1030, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 21:01:47] (step=0371600) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 21:02:33] (step=0371700) Train Loss: 0.1152, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 21:03:19] (step=0371800) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 21:04:04] (step=0371900) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 21:04:50] (step=0372000) Train Loss: 0.1147, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 21:05:05] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 21:05:51] (step=0372100) Train Loss: 0.1098, Train Steps/Sec: 1.64, Samples/Sec: 59.20
+[2025-06-09 21:06:37] (step=0372200) Train Loss: 0.1026, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 21:07:23] (step=0372300) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 21:08:09] (step=0372400) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 21:08:55] (step=0372500) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 21:09:41] (step=0372600) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 21:10:27] (step=0372700) Train Loss: 0.1058, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-06-09 21:11:13] (step=0372800) Train Loss: 0.1075, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-09 21:11:59] (step=0372900) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 21:12:45] (step=0373000) Train Loss: 0.1049, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 21:13:30] (step=0373100) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 21:14:16] (step=0373200) Train Loss: 0.1140, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 21:15:02] (step=0373300) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 21:15:48] (step=0373400) Train Loss: 0.1034, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-06-09 21:16:34] (step=0373500) Train Loss: 0.1023, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 21:17:20] (step=0373600) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 21:18:06] (step=0373700) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 21:18:52] (step=0373800) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 21:19:38] (step=0373900) Train Loss: 0.1122, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 21:20:24] (step=0374000) Train Loss: 0.1111, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 21:20:38] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 21:21:24] (step=0374100) Train Loss: 0.1067, Train Steps/Sec: 1.65, Samples/Sec: 59.41
+[2025-06-09 21:22:10] (step=0374200) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 21:22:56] (step=0374300) Train Loss: 0.1080, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 21:23:42] (step=0374400) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 21:24:28] (step=0374500) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 21:25:14] (step=0374600) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 21:26:00] (step=0374700) Train Loss: 0.1032, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-06-09 21:26:46] (step=0374800) Train Loss: 0.1059, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 21:27:32] (step=0374900) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 21:28:17] (step=0375000) Train Loss: 0.1015, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 21:29:03] (step=0375100) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 21:29:49] (step=0375200) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 21:30:35] (step=0375300) Train Loss: 0.1135, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 21:31:21] (step=0375400) Train Loss: 0.1120, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-06-09 21:32:07] (step=0375500) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 21:32:53] (step=0375600) Train Loss: 0.1122, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 21:33:39] (step=0375700) Train Loss: 0.1099, Train Steps/Sec: 2.19, Samples/Sec: 78.77
+[2025-06-09 21:34:24] (step=0375800) Train Loss: 0.1121, Train Steps/Sec: 2.19, Samples/Sec: 78.93
+[2025-06-09 21:35:10] (step=0375900) Train Loss: 0.1081, Train Steps/Sec: 2.19, Samples/Sec: 78.94
+[2025-06-09 21:35:56] (step=0376000) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 21:36:13] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 21:36:59] (step=0376100) Train Loss: 0.1071, Train Steps/Sec: 1.57, Samples/Sec: 56.66
+[2025-06-09 21:37:45] (step=0376200) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 21:38:31] (step=0376300) Train Loss: 0.1158, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 21:39:17] (step=0376400) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 21:40:03] (step=0376500) Train Loss: 0.1127, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 21:40:49] (step=0376600) Train Loss: 0.1139, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 21:41:35] (step=0376700) Train Loss: 0.1097, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 21:42:21] (step=0376800) Train Loss: 0.1079, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-06-09 21:43:07] (step=0376900) Train Loss: 0.1085, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 21:43:53] (step=0377000) Train Loss: 0.1026, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 21:44:39] (step=0377100) Train Loss: 0.1024, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-09 21:45:25] (step=0377200) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.32
+[2025-06-09 21:46:11] (step=0377300) Train Loss: 0.1035, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-09 21:46:57] (step=0377400) Train Loss: 0.1096, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-09 21:47:43] (step=0377500) Train Loss: 0.1119, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-06-09 21:48:29] (step=0377600) Train Loss: 0.1062, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 21:49:15] (step=0377700) Train Loss: 0.1125, Train Steps/Sec: 2.17, Samples/Sec: 78.23
+[2025-06-09 21:50:01] (step=0377800) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-09 21:50:47] (step=0377900) Train Loss: 0.1049, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 21:51:33] (step=0378000) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 21:51:47] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 21:52:33] (step=0378100) Train Loss: 0.1059, Train Steps/Sec: 1.66, Samples/Sec: 59.59
+[2025-06-09 21:53:19] (step=0378200) Train Loss: 0.1034, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 21:54:07] (step=0378300) Train Loss: 0.1098, Train Steps/Sec: 2.10, Samples/Sec: 75.45
+[2025-06-09 21:54:53] (step=0378400) Train Loss: 0.1057, Train Steps/Sec: 2.17, Samples/Sec: 78.23
+[2025-06-09 21:55:39] (step=0378500) Train Loss: 0.1149, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 21:56:25] (step=0378600) Train Loss: 0.1048, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-09 21:57:11] (step=0378700) Train Loss: 0.1164, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 21:57:57] (step=0378800) Train Loss: 0.1096, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-06-09 21:58:43] (step=0378900) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 21:59:29] (step=0379000) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 22:00:14] (step=0379100) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:01:00] (step=0379200) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 22:01:46] (step=0379300) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 22:02:32] (step=0379400) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.30
+[2025-06-09 22:03:18] (step=0379500) Train Loss: 0.1012, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 22:04:04] (step=0379600) Train Loss: 0.1156, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 22:04:50] (step=0379700) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 22:05:36] (step=0379800) Train Loss: 0.1075, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-06-09 22:06:22] (step=0379900) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 22:07:08] (step=0380000) Train Loss: 0.1077, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 22:07:35] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0380000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 22:07:38] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 22:09:43] (step=0380000) Perceptual Loss: 0.1824, Eval Time: 127.83
+[2025-06-09 22:10:29] (step=0380100) Train Loss: 0.1040, Train Steps/Sec: 0.50, Samples/Sec: 17.86
+[2025-06-09 22:11:15] (step=0380200) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.66
+[2025-06-09 22:12:01] (step=0380300) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:12:47] (step=0380400) Train Loss: 0.1055, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 22:13:33] (step=0380500) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 22:14:18] (step=0380600) Train Loss: 0.1007, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 22:15:05] (step=0380700) Train Loss: 0.1073, Train Steps/Sec: 2.16, Samples/Sec: 77.71
+[2025-06-09 22:15:51] (step=0380800) Train Loss: 0.1020, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 22:16:37] (step=0380900) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-09 22:17:23] (step=0381000) Train Loss: 0.1031, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 22:18:09] (step=0381100) Train Loss: 0.1085, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-06-09 22:18:54] (step=0381200) Train Loss: 0.1059, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:19:40] (step=0381300) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 22:20:27] (step=0381400) Train Loss: 0.1066, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-06-09 22:21:12] (step=0381500) Train Loss: 0.1155, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 22:21:58] (step=0381600) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 22:22:44] (step=0381700) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 22:23:30] (step=0381800) Train Loss: 0.1053, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 22:24:16] (step=0381900) Train Loss: 0.1089, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 22:25:02] (step=0382000) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 22:25:16] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 22:26:02] (step=0382100) Train Loss: 0.1036, Train Steps/Sec: 1.66, Samples/Sec: 59.68
+[2025-06-09 22:26:48] (step=0382200) Train Loss: 0.1134, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:27:34] (step=0382300) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:28:20] (step=0382400) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 22:29:05] (step=0382500) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 22:29:51] (step=0382600) Train Loss: 0.1104, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 22:30:37] (step=0382700) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.35
+[2025-06-09 22:31:23] (step=0382800) Train Loss: 0.1167, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-09 22:32:09] (step=0382900) Train Loss: 0.1044, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 22:32:55] (step=0383000) Train Loss: 0.1090, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-06-09 22:33:41] (step=0383100) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 22:34:27] (step=0383200) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 22:35:13] (step=0383300) Train Loss: 0.1143, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 22:35:59] (step=0383400) Train Loss: 0.1067, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-06-09 22:36:45] (step=0383500) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 22:37:31] (step=0383600) Train Loss: 0.1041, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 22:38:17] (step=0383700) Train Loss: 0.1033, Train Steps/Sec: 2.18, Samples/Sec: 78.57
+[2025-06-09 22:39:02] (step=0383800) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 22:39:48] (step=0383900) Train Loss: 0.1146, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 22:40:34] (step=0384000) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 22:40:49] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 22:40:59] Beginning epoch 26...
+[2025-06-09 22:41:36] (step=0384100) Train Loss: 0.1039, Train Steps/Sec: 1.62, Samples/Sec: 58.29
+[2025-06-09 22:42:22] (step=0384200) Train Loss: 0.1147, Train Steps/Sec: 2.18, Samples/Sec: 78.54
+[2025-06-09 22:43:08] (step=0384300) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:43:54] (step=0384400) Train Loss: 0.1040, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:44:39] (step=0384500) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:45:25] (step=0384600) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 22:46:11] (step=0384700) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 22:46:57] (step=0384800) Train Loss: 0.1049, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-06-09 22:47:43] (step=0384900) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 22:48:29] (step=0385000) Train Loss: 0.1117, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:49:15] (step=0385100) Train Loss: 0.1008, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 22:50:01] (step=0385200) Train Loss: 0.1123, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 22:50:47] (step=0385300) Train Loss: 0.1054, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 22:51:33] (step=0385400) Train Loss: 0.1086, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-06-09 22:52:19] (step=0385500) Train Loss: 0.1070, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 22:53:04] (step=0385600) Train Loss: 0.1078, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 22:53:50] (step=0385700) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.60
+[2025-06-09 22:54:36] (step=0385800) Train Loss: 0.0987, Train Steps/Sec: 2.19, Samples/Sec: 78.95
+[2025-06-09 22:55:21] (step=0385900) Train Loss: 0.1099, Train Steps/Sec: 2.19, Samples/Sec: 78.96
+[2025-06-09 22:56:07] (step=0386000) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.63
+[2025-06-09 22:56:21] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 22:57:08] (step=0386100) Train Loss: 0.1150, Train Steps/Sec: 1.66, Samples/Sec: 59.70
+[2025-06-09 22:57:53] (step=0386200) Train Loss: 0.1051, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 22:58:39] (step=0386300) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 22:59:25] (step=0386400) Train Loss: 0.1101, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 23:00:11] (step=0386500) Train Loss: 0.1090, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:00:57] (step=0386600) Train Loss: 0.1082, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:01:43] (step=0386700) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 23:02:29] (step=0386800) Train Loss: 0.1031, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-06-09 23:03:15] (step=0386900) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 23:04:01] (step=0387000) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 23:04:47] (step=0387100) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 23:05:32] (step=0387200) Train Loss: 0.1080, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:06:18] (step=0387300) Train Loss: 0.1035, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:07:04] (step=0387400) Train Loss: 0.1103, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 23:07:50] (step=0387500) Train Loss: 0.1075, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 23:08:36] (step=0387600) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 23:09:22] (step=0387700) Train Loss: 0.1052, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 23:10:08] (step=0387800) Train Loss: 0.1127, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 23:10:54] (step=0387900) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:11:40] (step=0388000) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 23:11:54] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 23:12:40] (step=0388100) Train Loss: 0.1101, Train Steps/Sec: 1.65, Samples/Sec: 59.35
+[2025-06-09 23:13:26] (step=0388200) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 23:14:12] (step=0388300) Train Loss: 0.1134, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 23:14:58] (step=0388400) Train Loss: 0.1035, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 23:15:44] (step=0388500) Train Loss: 0.1106, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 23:16:30] (step=0388600) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:17:16] (step=0388700) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 23:18:02] (step=0388800) Train Loss: 0.1047, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-06-09 23:18:48] (step=0388900) Train Loss: 0.1126, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 23:19:33] (step=0389000) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 23:20:19] (step=0389100) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 23:21:05] (step=0389200) Train Loss: 0.1061, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 23:21:51] (step=0389300) Train Loss: 0.1135, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 23:22:37] (step=0389400) Train Loss: 0.1132, Train Steps/Sec: 2.18, Samples/Sec: 78.30
+[2025-06-09 23:23:23] (step=0389500) Train Loss: 0.1094, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-09 23:24:09] (step=0389600) Train Loss: 0.1084, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 23:24:55] (step=0389700) Train Loss: 0.0968, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:25:41] (step=0389800) Train Loss: 0.1048, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 23:26:27] (step=0389900) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 23:27:12] (step=0390000) Train Loss: 0.1108, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 23:27:32] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-09 23:27:35] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-09 23:29:40] (step=0390000) Perceptual Loss: 0.1376, Eval Time: 127.77
+[2025-06-09 23:30:26] (step=0390100) Train Loss: 0.1126, Train Steps/Sec: 0.52, Samples/Sec: 18.62
+[2025-06-09 23:31:12] (step=0390200) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-09 23:31:57] (step=0390300) Train Loss: 0.1134, Train Steps/Sec: 2.18, Samples/Sec: 78.55
+[2025-06-09 23:32:43] (step=0390400) Train Loss: 0.1105, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 23:33:29] (step=0390500) Train Loss: 0.0994, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 23:34:15] (step=0390600) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-09 23:35:01] (step=0390700) Train Loss: 0.1121, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-06-09 23:35:47] (step=0390800) Train Loss: 0.1098, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-09 23:36:33] (step=0390900) Train Loss: 0.1116, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 23:37:19] (step=0391000) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-09 23:38:05] (step=0391100) Train Loss: 0.1038, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 23:38:51] (step=0391200) Train Loss: 0.1066, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 23:39:36] (step=0391300) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 23:40:23] (step=0391400) Train Loss: 0.1132, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-06-09 23:41:09] (step=0391500) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-09 23:41:54] (step=0391600) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 23:42:40] (step=0391700) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.59
+[2025-06-09 23:43:26] (step=0391800) Train Loss: 0.1037, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-09 23:44:12] (step=0391900) Train Loss: 0.0979, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-09 23:44:58] (step=0392000) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-09 23:45:12] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-09 23:45:58] (step=0392100) Train Loss: 0.1080, Train Steps/Sec: 1.65, Samples/Sec: 59.56
+[2025-06-09 23:46:44] (step=0392200) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 23:47:30] (step=0392300) Train Loss: 0.1158, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 23:48:16] (step=0392400) Train Loss: 0.1160, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 23:49:02] (step=0392500) Train Loss: 0.1120, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-09 23:49:48] (step=0392600) Train Loss: 0.1159, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-09 23:50:34] (step=0392700) Train Loss: 0.1053, Train Steps/Sec: 2.17, Samples/Sec: 78.29
+[2025-06-09 23:51:20] (step=0392800) Train Loss: 0.1129, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-06-09 23:52:06] (step=0392900) Train Loss: 0.1091, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:52:51] (step=0393000) Train Loss: 0.1085, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:53:37] (step=0393100) Train Loss: 0.1081, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-09 23:54:23] (step=0393200) Train Loss: 0.1081, Train Steps/Sec: 2.17, Samples/Sec: 78.21
+[2025-06-09 23:55:09] (step=0393300) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:55:55] (step=0393400) Train Loss: 0.1054, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-06-09 23:56:41] (step=0393500) Train Loss: 0.1004, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-09 23:57:27] (step=0393600) Train Loss: 0.1163, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-09 23:58:13] (step=0393700) Train Loss: 0.1092, Train Steps/Sec: 2.18, Samples/Sec: 78.61
+[2025-06-09 23:58:59] (step=0393800) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-09 23:59:45] (step=0393900) Train Loss: 0.1115, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-10 00:00:31] (step=0394000) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-10 00:00:46] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-10 00:01:32] (step=0394100) Train Loss: 0.1054, Train Steps/Sec: 1.63, Samples/Sec: 58.52
+[2025-06-10 00:02:18] (step=0394200) Train Loss: 0.1071, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-10 00:03:04] (step=0394300) Train Loss: 0.1058, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-10 00:03:50] (step=0394400) Train Loss: 0.1023, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-10 00:04:36] (step=0394500) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-10 00:05:21] (step=0394600) Train Loss: 0.1137, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-10 00:06:08] (step=0394700) Train Loss: 0.1121, Train Steps/Sec: 2.17, Samples/Sec: 78.23
+[2025-06-10 00:06:54] (step=0394800) Train Loss: 0.1046, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-06-10 00:07:40] (step=0394900) Train Loss: 0.1075, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-10 00:08:25] (step=0395000) Train Loss: 0.1056, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-10 00:09:11] (step=0395100) Train Loss: 0.1020, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-10 00:09:57] (step=0395200) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-10 00:10:43] (step=0395300) Train Loss: 0.1087, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-10 00:11:29] (step=0395400) Train Loss: 0.1096, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-06-10 00:12:15] (step=0395500) Train Loss: 0.1029, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-10 00:13:01] (step=0395600) Train Loss: 0.1140, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-10 00:13:47] (step=0395700) Train Loss: 0.1047, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-10 00:14:33] (step=0395800) Train Loss: 0.1077, Train Steps/Sec: 2.19, Samples/Sec: 78.81
+[2025-06-10 00:15:18] (step=0395900) Train Loss: 0.1112, Train Steps/Sec: 2.19, Samples/Sec: 78.85
+[2025-06-10 00:16:04] (step=0396000) Train Loss: 0.1085, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-06-10 00:16:23] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-10 00:17:12] (step=0396100) Train Loss: 0.1066, Train Steps/Sec: 1.48, Samples/Sec: 53.42
+[2025-06-10 00:17:58] (step=0396200) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-10 00:18:44] (step=0396300) Train Loss: 0.1064, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-10 00:19:30] (step=0396400) Train Loss: 0.1107, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-10 00:20:16] (step=0396500) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-10 00:21:01] (step=0396600) Train Loss: 0.1009, Train Steps/Sec: 2.17, Samples/Sec: 78.28
+[2025-06-10 00:21:48] (step=0396700) Train Loss: 0.1082, Train Steps/Sec: 2.17, Samples/Sec: 78.25
+[2025-06-10 00:22:34] (step=0396800) Train Loss: 0.1054, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-06-10 00:23:20] (step=0396900) Train Loss: 0.1026, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-10 00:24:05] (step=0397000) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-10 00:24:51] (step=0397100) Train Loss: 0.1072, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-10 00:25:37] (step=0397200) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-10 00:26:23] (step=0397300) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-10 00:27:09] (step=0397400) Train Loss: 0.1100, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-10 00:27:55] (step=0397500) Train Loss: 0.1082, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-06-10 00:28:41] (step=0397600) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-10 00:29:27] (step=0397700) Train Loss: 0.1126, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-10 00:30:13] (step=0397800) Train Loss: 0.1112, Train Steps/Sec: 2.18, Samples/Sec: 78.39
+[2025-06-10 00:30:59] (step=0397900) Train Loss: 0.1114, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-10 00:31:45] (step=0398000) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-10 00:32:05] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-10 00:32:51] (step=0398100) Train Loss: 0.1017, Train Steps/Sec: 1.50, Samples/Sec: 54.08
+[2025-06-10 00:33:37] (step=0398200) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-10 00:34:23] (step=0398300) Train Loss: 0.1095, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-10 00:35:09] (step=0398400) Train Loss: 0.1129, Train Steps/Sec: 2.18, Samples/Sec: 78.37
+[2025-06-10 00:35:55] (step=0398500) Train Loss: 0.1060, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-10 00:36:41] (step=0398600) Train Loss: 0.1121, Train Steps/Sec: 2.17, Samples/Sec: 78.20
+[2025-06-10 00:37:27] (step=0398700) Train Loss: 0.1123, Train Steps/Sec: 2.15, Samples/Sec: 77.29
+[2025-06-10 00:38:09] Beginning epoch 27...
+[2025-06-10 00:38:14] (step=0398800) Train Loss: 0.1058, Train Steps/Sec: 2.14, Samples/Sec: 77.16
+[2025-06-10 00:39:11] (step=0398900) Train Loss: 0.1086, Train Steps/Sec: 1.77, Samples/Sec: 63.86
+[2025-06-10 00:39:57] (step=0399000) Train Loss: 0.1114, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-06-10 00:40:43] (step=0399100) Train Loss: 0.1063, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-10 00:41:28] (step=0399200) Train Loss: 0.1015, Train Steps/Sec: 2.18, Samples/Sec: 78.34
+[2025-06-10 00:42:14] (step=0399300) Train Loss: 0.1021, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-10 00:43:00] (step=0399400) Train Loss: 0.1009, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-10 00:43:49] (step=0399500) Train Loss: 0.1097, Train Steps/Sec: 2.07, Samples/Sec: 74.36
+[2025-06-10 00:44:35] (step=0399600) Train Loss: 0.1094, Train Steps/Sec: 2.15, Samples/Sec: 77.45
+[2025-06-10 00:45:22] (step=0399700) Train Loss: 0.1069, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-06-10 00:46:08] (step=0399800) Train Loss: 0.1048, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-06-10 00:46:54] (step=0399900) Train Loss: 0.1094, Train Steps/Sec: 2.17, Samples/Sec: 78.27
+[2025-06-10 00:47:40] (step=0400000) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-10 00:48:07] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/0400000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-06-10 00:48:10] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-06-10 00:50:17] (step=0400000) Perceptual Loss: 0.1432, Eval Time: 129.45
+[2025-06-10 00:51:02] (step=0400100) Train Loss: 0.1016, Train Steps/Sec: 0.49, Samples/Sec: 17.75
+[2025-06-10 00:51:48] (step=0400200) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.51
+[2025-06-10 00:52:34] (step=0400300) Train Loss: 0.1074, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-10 00:53:20] (step=0400400) Train Loss: 0.1127, Train Steps/Sec: 2.18, Samples/Sec: 78.53
+[2025-06-10 00:54:06] (step=0400500) Train Loss: 0.1051, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-10 00:54:56] (step=0400600) Train Loss: 0.1089, Train Steps/Sec: 2.01, Samples/Sec: 72.22
+[2025-06-10 00:55:42] (step=0400700) Train Loss: 0.1038, Train Steps/Sec: 2.17, Samples/Sec: 78.23
+[2025-06-10 00:56:28] (step=0400800) Train Loss: 0.1025, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-06-10 00:57:14] (step=0400900) Train Loss: 0.1102, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-10 00:58:00] (step=0401000) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.50
+[2025-06-10 00:58:45] (step=0401100) Train Loss: 0.1046, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-10 00:59:33] (step=0401200) Train Loss: 0.1057, Train Steps/Sec: 2.11, Samples/Sec: 75.84
+[2025-06-10 01:00:19] (step=0401300) Train Loss: 0.1126, Train Steps/Sec: 2.17, Samples/Sec: 78.26
+[2025-06-10 01:01:05] (step=0401400) Train Loss: 0.1068, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-06-10 01:01:51] (step=0401500) Train Loss: 0.1109, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-10 01:02:37] (step=0401600) Train Loss: 0.1098, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-10 01:03:23] (step=0401700) Train Loss: 0.1083, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-10 01:04:11] (step=0401800) Train Loss: 0.1033, Train Steps/Sec: 2.10, Samples/Sec: 75.45
+[2025-06-10 01:04:56] (step=0401900) Train Loss: 0.1113, Train Steps/Sec: 2.18, Samples/Sec: 78.44
+[2025-06-10 01:05:42] (step=0402000) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.31
+[2025-06-10 01:05:57] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-10 01:06:43] (step=0402100) Train Loss: 0.1062, Train Steps/Sec: 1.64, Samples/Sec: 59.12
+[2025-06-10 01:07:29] (step=0402200) Train Loss: 0.1086, Train Steps/Sec: 2.18, Samples/Sec: 78.52
+[2025-06-10 01:08:15] (step=0402300) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-10 01:09:01] (step=0402400) Train Loss: 0.1076, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-10 01:09:47] (step=0402500) Train Loss: 0.1146, Train Steps/Sec: 2.18, Samples/Sec: 78.43
+[2025-06-10 01:10:33] (step=0402600) Train Loss: 0.1069, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-10 01:11:19] (step=0402700) Train Loss: 0.1110, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-10 01:12:05] (step=0402800) Train Loss: 0.1024, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-06-10 01:12:51] (step=0402900) Train Loss: 0.1033, Train Steps/Sec: 2.18, Samples/Sec: 78.38
+[2025-06-10 01:13:41] (step=0403000) Train Loss: 0.1111, Train Steps/Sec: 2.00, Samples/Sec: 72.06
+[2025-06-10 01:14:27] (step=0403100) Train Loss: 0.1044, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-06-10 01:15:13] (step=0403200) Train Loss: 0.1026, Train Steps/Sec: 2.18, Samples/Sec: 78.41
+[2025-06-10 01:15:59] (step=0403300) Train Loss: 0.1065, Train Steps/Sec: 2.18, Samples/Sec: 78.42
+[2025-06-10 01:16:45] (step=0403400) Train Loss: 0.1072, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-06-10 01:17:31] (step=0403500) Train Loss: 0.1039, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-10 01:18:17] (step=0403600) Train Loss: 0.1067, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-10 01:19:03] (step=0403700) Train Loss: 0.1036, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-10 01:19:49] (step=0403800) Train Loss: 0.1031, Train Steps/Sec: 2.18, Samples/Sec: 78.49
+[2025-06-10 01:20:35] (step=0403900) Train Loss: 0.1100, Train Steps/Sec: 2.18, Samples/Sec: 78.33
+[2025-06-10 01:21:20] (step=0404000) Train Loss: 0.1133, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-10 01:21:35] Saved checkpoint to logs/nwm_cdit_recon/checkpoints/latest.pth.tar
+[2025-06-10 01:22:24] (step=0404100) Train Loss: 0.1087, Train Steps/Sec: 1.57, Samples/Sec: 56.67
+[2025-06-10 01:23:10] (step=0404200) Train Loss: 0.0981, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-06-10 01:23:56] (step=0404300) Train Loss: 0.1112, Train Steps/Sec: 2.18, Samples/Sec: 78.46
+[2025-06-10 01:24:42] (step=0404400) Train Loss: 0.1057, Train Steps/Sec: 2.18, Samples/Sec: 78.48
+[2025-06-10 01:25:28] (step=0404500) Train Loss: 0.1094, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-10 01:26:14] (step=0404600) Train Loss: 0.1097, Train Steps/Sec: 2.18, Samples/Sec: 78.40
+[2025-06-10 01:27:00] (step=0404700) Train Loss: 0.1068, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-10 01:27:49] (step=0404800) Train Loss: 0.1067, Train Steps/Sec: 2.02, Samples/Sec: 72.70
+[2025-06-10 01:28:35] (step=0404900) Train Loss: 0.1088, Train Steps/Sec: 2.18, Samples/Sec: 78.36
+[2025-06-10 01:29:21] (step=0405000) Train Loss: 0.1145, Train Steps/Sec: 2.18, Samples/Sec: 78.45
+[2025-06-10 01:30:07] (step=0405100) Train Loss: 0.1096, Train Steps/Sec: 2.18, Samples/Sec: 78.47
+[2025-06-10 01:30:55] (step=0405200) Train Loss: 0.1127, Train Steps/Sec: 2.07, Samples/Sec: 74.40
+[2025-06-10 01:31:41] (step=0405300) Train Loss: 0.1045, Train Steps/Sec: 2.18, Samples/Sec: 78.30
+[2025-06-10 01:32:33] (step=0405400) Train Loss: 0.1034, Train Steps/Sec: 1.94, Samples/Sec: 69.81
+[2025-06-10 01:33:20] (step=0405500) Train Loss: 0.1092, Train Steps/Sec: 2.14, Samples/Sec: 77.00
+Traceback (most recent call last):
+  File "train_recon_v2.py", line 446, in <module>
+    main(args)
+  File "train_recon_v2.py", line 299, in main
+    loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+  File "/data1/tpz/nwm-main/diffusion/respace.py", line 103, in training_losses
+    return super().training_losses(self._wrap_model(model), *args, **kwargs)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 753, in training_losses
+    model_output = model(x_t, t, **model_kwargs)
+  File "/data1/tpz/nwm-main/diffusion/respace.py", line 135, in __call__
+    return self.model(x, new_ts, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1519, in forward
+    else self._run_ddp_forward(*inputs, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1355, in _run_ddp_forward
+[2025-06-10 01:33:28,980] torch.distributed.elastic.agent.server.api: [WARNING] Received Signals.SIGINT death signal, shutting down workers
+    return self.module(*inputs, **kwargs)  # type: ignore[index]
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
+[2025-06-10 01:33:28,982] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2208570 closing signal SIGINT
+[2025-06-10 01:33:28,982] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2208571 closing signal SIGINT
+    return forward_call(*args, **kwargs)
+  File "/data1/tpz/nwm-main/models_tpz_v2.py", line 282, in forward
+[2025-06-10 01:33:28,982] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 2208572 closing signal SIGINT
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/data1/tpz/nwm-main/models_tpz_v2.py", line 166, in forward
+    x = self.linear(x)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
+    return forward_call(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/modules/linear.py", line 114, in forward
+    return F.linear(input, self.weight, self.bias)
+KeyboardInterrupt
+Traceback (most recent call last):
+  File "train_recon_v2.py", line 446, in <module>
+    main(args)
+  File "train_recon_v2.py", line 299, in main
+    loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+  File "/data1/tpz/nwm-main/diffusion/respace.py", line 103, in training_losses
+    return super().training_losses(self._wrap_model(model), *args, **kwargs)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 765, in training_losses
+    terms["vb"] = self._vb_terms_bpd(
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 699, in _vb_terms_bpd
+    true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 245, in q_posterior_mean_variance
+    _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 876, in _extract_into_tensor
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+KeyboardInterrupt
+Traceback (most recent call last):
+  File "train_recon_v2.py", line 446, in <module>
+    main(args)
+  File "train_recon_v2.py", line 299, in main
+    loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+  File "/data1/tpz/nwm-main/diffusion/respace.py", line 103, in training_losses
+    return super().training_losses(self._wrap_model(model), *args, **kwargs)
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 765, in training_losses
+    terms["vb"] = self._vb_terms_bpd(
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 699, in _vb_terms_bpd
+    true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 245, in q_posterior_mean_variance
+    _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+  File "/data1/tpz/nwm-main/diffusion/gaussian_diffusion.py", line 876, in _extract_into_tensor
+    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+KeyboardInterrupt
+Traceback (most recent call last):
+  File "/data1/tpz/anaconda3/envs/nwm2/bin/torchrun", line 8, in <module>
+    sys.exit(main())
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
+    return f(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/run.py", line 806, in main
+    run(args)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/run.py", line 797, in run
+    elastic_launch(
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 255, in launch_agent
+    result = agent.run()
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 124, in wrapper
+    result = f(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 736, in run
+    result = self._invoke_run(role)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 877, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 62, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 2208501 got signal: 2
diff --git a/rule_based_trajectory_generation.py b/rule_based_trajectory_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c0df3c6e83c8a58820b106d95394c8ba0a20ea0
--- /dev/null
+++ b/rule_based_trajectory_generation.py
@@ -0,0 +1,261 @@
+# A 3D trajectory genration module for a drone. Generates several trajectories by rule-based methods.
+# Trajectory is a sequence of 3D points of (x,y,z,yaw).
+import random
+import datetime
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from sdtw import SoftDTW
+from scipy.spatial.distance import euclidean
+
+action_space = {
+    "forward": (5, 0, 0, 0),
+    "backward": (-5, 0, 0, 0),
+    "up": (0, 0, 2, 0), 
+    "down": (0, 0, -2, 0),
+    "left": (0, 0, 0, np.pi/12),  # Turn left by 15 degrees
+    "right": (0, 0, 0, -np.pi/12)  # Turn right by 15 degrees
+}
+
+def rule_based_trajectory(start, steps=8, rule_sequence=None):
+    # 基于固定规则的轨迹生成
+    trajectory = [start]
+    current_pos = np.array(start[:4], dtype=float)  # (x, y, z, yaw) - 确保为浮点类型
+    
+
+    for i in range(steps):
+        last_pos = trajectory[-1]
+
+        action_choice = rule_sequence[i % len(rule_sequence)]
+        action = action_space[action_choice]
+
+
+        if action_choice in ["left", "right", "up", "down"]:
+            current_pos = last_pos + np.array(action)
+        elif action_choice in ["forward", "backward"]:
+            dx = action[0] * np.cos(last_pos[3])
+            dy = action[0] * np.sin(last_pos[3])
+            current_pos = last_pos + np.array([dx, dy, 0, 0])
+        trajectory.append(current_pos)
+        
+    # 确保返回的轨迹起点与(0,0,0,0)对齐
+    trajectory = np.array(trajectory, dtype=float)
+    start_offset = trajectory[0, :].copy()  # 保存起点偏移
+    trajectory = trajectory - start_offset  # 将整个轨迹平移到以(0,0,0,0)为起点
+    trajectory[0, :] = 0.0  # 确保起点精确为(0,0,0,0)
+    
+    return trajectory.tolist()
+
+def trajectory_similarity(traj1, traj2):
+    """
+    计算两条轨迹之间的相似度, 使用Soft-DTW方法。
+    
+    参数:
+    traj1: list - 第一条轨迹, 包含多个点 (x, y, z, yaw)
+    traj2: list - 第二条轨迹, 包含多个点 (x, y, z, yaw)
+    返回:
+    float - 相似度分数, 范围在0到1之间, 1表示完全相似, 0表示完全不同
+    """
+
+    # 提取轨迹点的坐标
+    traj1_points = np.array([[point[0], point[1], point[2]] for point in traj1])
+    traj2_points = np.array([[point[0], point[1], point[2]] for point in traj2])
+
+    # 计算距离矩阵
+    D = np.zeros((len(traj1_points), len(traj2_points)))
+    for i in range(len(traj1_points)):
+        for j in range(len(traj2_points)):
+            D[i, j] = np.linalg.norm(traj1_points[i] - traj2_points[j])
+
+    # 计算Soft-DTW距离
+    soft_dtw = SoftDTW(D, gamma=1.0)
+    distance = soft_dtw.compute()
+
+    max_distance = np.sqrt(len(traj1) + len(traj2))
+    similarity_score = 1 - (distance / max_distance)
+    return similarity_score
+
+def trajectory_generation_rule_based(GT_trajectory, candidate_number=10, dim=3):
+    """
+    生成多个轨迹的函数
+    
+    参数:
+    start_pos: tuple - 起始坐标 (x, y, z, yaw)
+    steps: int - 每条轨迹的步数, 默认6步
+    candidate_number: int - 候选轨迹数量, 默认20条
+    
+    返回:
+    list - 包含多个轨迹的列表, 每个轨迹是一个包含坐标点的列表
+    """
+    candidate_trajectoires = []
+    start_pos = GT_trajectory[0]  # Use the first point of GT trajectory as the start position
+    end_pos = GT_trajectory[-1]   # Use the last point of GT trajectory as the end position
+
+    start_pos = list(start_pos)
+    start_pos[-1] = end_pos[-1]  # Align starting yaw with GT end yaw
+    start_pos = tuple(start_pos)
+
+    steps = len(GT_trajectory) - 1  # Use the length of GT trajectory minus one for steps
+
+    if dim == 3:
+    # 定义多种rule，符合无人机飞行常理，避免与GT yaw 相似，添加转向动作，左转1条，右转1条，上升1条，下降1条
+        rule_sequences = [
+            # ["left", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward"] * (steps // 16),
+            # ["right", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward", "forward"] * (steps // 16),
+            ["left", "forward", "up", "forward"] * (steps // 4),  # 左转 + 前进 + 上升 + 前进
+            ["right", "forward", "up", "forward"] * (steps // 4), # 右转 + 前进 + 上升 + 前进
+            # ["left", "forward", "down", "forward"] * (steps // 4),  # 左转 + 前进 + 下降 + 前进
+            # ["right", "forward", "down", "forward"] * (steps // 4), # 右转 + 前进 + 下降 + 前进
+            # ["left", "forward"] * (steps // 2),          # 左转 + 前进
+            # ["right", "forward"] * (steps // 2),         # 右转 + 前进
+            # ["forward", "up"] * (steps // 2),           # 前进 + 上升
+            # ["left", "left", "forward", "left"] * (steps // 4),    # 三次左转 + 前进
+            # ["right", "right", "forward", "right"] * (steps // 4),  # 三次右转 + 前进
+            # ["up", "up", "left", "forward"] * (steps // 4),       # 上升-上升-左转-前进
+            # ["up", "up", "right", "forward"] * (steps // 4),       # 上升-上升-右转-前进
+            # ["down", "down", "left", "forward"] * (steps // 4),   # 下降-下降-左转-前进
+            # ["down", "down", "right", "forward"] * (steps // 4),    # 下降-下降-右转-前进
+            # ["forward", "left", "forward", "right"] * (steps // 4),    # 蛇形前进（左-右交替）
+            # ["up", "forward", "down", "forward"] * (steps // 4),       # 上下起伏前进
+            # ["left", "forward", "right", "forward"] * (steps // 4),    # 左右摆动前进
+            # ["up", "forward", "up", "forward"] * (steps // 4),         # 逐步上升式前进
+            # ["down", "forward", "down", "forward"] * (steps // 4),     # 逐步下降式前进
+            # ["forward", "forward", "left", "up"] * (steps // 4),       # 前进两步左转上升
+            # ["forward", "forward", "right", "down"] * (steps // 4)    # 前进两步右转下降
+        ]
+    elif dim == 2:
+        rule_sequences = [
+            ["left", "forward"] * (steps // 2),          # 左转 + 前进
+            ["right", "forward"] * (steps // 2),         # 右转 + 前进
+            ["left", "left", "forward", "left"] * (steps // 4),    # 三次左转 + 前进
+            ["right", "right", "forward", "right"] * (steps // 4),  # 三次右转 + 前进
+            ["left", "forward", "right", "forward"] * (steps // 4),    # 左右摆动前进
+        ]
+    else:
+        raise ValueError(f"Invalid dimension: {dim}")
+
+    i = 0
+    selected_rules = random.sample(rule_sequences, len(rule_sequences))
+
+    # for rule_seq in selected_rules:
+    #     trajectory = rule_based_trajectory(
+    #         start=start_pos,
+    #         steps=steps,
+    #         rule_sequence=rule_seq
+    #     )
+    #     candidate_trajectoires.append(trajectory)
+
+    while len(candidate_trajectoires) < candidate_number:
+        # Generate a random starting direction
+        # starting_yaw = np.random.uniform(-np.pi, np.pi)
+        rule_seq = selected_rules[i]
+        trajectory = rule_based_trajectory(
+            start=start_pos,
+            steps=steps,
+            rule_sequence=rule_seq
+        )
+        if abs(trajectory[-1][3] - end_pos[3]) < np.pi / 4:  # Ensure yaw difference from GT end is significant
+            i += 1
+            continue
+        else:
+            candidate_trajectoires.append(trajectory)
+            i += 1
+        # print(f"i = {i}, Generated trajectory with rule: {rule_seq}")
+
+    return candidate_trajectoires
+
+def plot_3d_trajectories(GT_trajectory, candidate_trajectoires, save_path="./plot", filename="trajectories_3d.png", show_plot=True):
+    """
+    绘制3D轨迹图的函数
+    
+    参数:
+    candidate_trajectoires: list - 包含多个轨迹的列表
+    save_path: str - 图片保存路径, 默认为 "./plot"
+    filename: str - 图片文件名, 默认为 "trajectories_3d.png"
+    show_plot: bool - 是否显示图片, 默认为 True
+    """
+    # 创建保存目录（如果不存在）
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+    
+    fig = plt.figure(figsize=(12, 9))
+    ax = fig.add_subplot(111, projection='3d')
+    
+    colors = ['blue', 'green', 'orange', 'purple', 'brown', 'cyan', 'magenta', 'yellow', 'black']  # 扩展颜色列表
+    
+
+    # 绘制GT轨迹, 红色,  粗
+    gt_xs, gt_ys, gt_zs = zip(*[(point[0], point[1], point[2]) for point in GT_trajectory])
+    ax.plot(gt_xs, gt_ys, gt_zs, color='red', alpha=0.9, linewidth=3, label='GT Trajectory')
+    ax.scatter(gt_xs[0], gt_ys[0], gt_zs[0], color='red', s=100, marker='o', edgecolors='black', linewidth=1, label='GT Start')
+    ax.scatter(gt_xs[-1], gt_ys[-1], gt_zs[-1], color='red', s=80, marker='*', edgecolors='black', linewidth=1, label='GT End')
+
+    for i, traj in enumerate(candidate_trajectoires):
+        xs, ys, zs = zip(*[(point[0], point[1], point[2]) for point in traj])
+        color = colors[i % len(colors)]
+        
+        # 绘制轨迹线
+        ax.plot(xs, ys, zs, color=color, alpha=0.7, linewidth=2, label=f'Trajectory {i+1}')
+        
+        # 标记起点
+        ax.scatter(xs[0], ys[0], zs[0], color=color, s=100, marker='o', edgecolors='black', linewidth=1)
+        
+        # 标记中间节点（除了起点和终点）
+        if len(xs) > 2:
+            ax.scatter(xs[1:-1], ys[1:-1], zs[1:-1], color=color, s=30, marker='o', alpha=0.8)
+        
+        # 标记终点
+        ax.scatter(xs[-1], ys[-1], zs[-1], color=color, s=80, marker='s', edgecolors='black', linewidth=1)
+    
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_zlabel('Z')
+    ax.set_title('3D Trajectories Visualization')
+    
+    # 只在轨迹数量较少时显示图例
+    if len(candidate_trajectoires) <= 10:
+        ax.legend()
+    
+    # 保存图片
+    full_path = os.path.join(save_path, filename)
+    plt.savefig(full_path, dpi=300, bbox_inches='tight')
+    print(f"图片已保存到: {full_path}")
+    
+    # 显示图片
+    if show_plot:
+        plt.show()
+    else:
+        plt.close()
+
+if __name__ == "__main__":
+    # Initiate
+    origin_crd = (0.0, 0.0, 0.0, 0.0)  # (x, y, z, yaw)
+    candidate_number = 4
+    # step_number = 6
+
+    candidate_trajectoires = []
+
+    GT_traj = [
+        (0.0, 0.0, 0.0, 1.57), (0.0, 5.0, 0.0, 1.57), (0.0, 10.0, 0.0, 1.57), (0.0, 10.0, 0.0, 1.83), (0.0, 10.0, 0.0, 2.09), (0.0, 10.0, 2.0, 2.09), (-2.5, 14.3, 2.0, 2.09), (-3, 18.6, 2.0, 2.09), (-3, 18.6, 4.0, 2.09) ]
+
+    # Generate candidate trajectories
+    candidate_trajectoires = trajectory_generation_rule_based(
+        GT_traj, 
+        candidate_number=candidate_number,
+        dim=2
+    )
+    
+    # Plot the trajectories
+    plot_3d_trajectories(
+        GT_traj,
+        candidate_trajectoires, 
+        save_path="./plot", 
+        filename=f"trajectories_GT_rule_based_{datetime.datetime.now():%m-%d_%H-%M-%S}.png"
+    )
+
+
+
+    
+
+
+
diff --git a/sacson.png b/sacson.png
new file mode 100644
index 0000000000000000000000000000000000000000..4d56b617f2360f4172eba367077f3d8448a5da5f
Binary files /dev/null and b/sacson.png differ
diff --git a/sample_dataset.py b/sample_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..05c7b905de5d5d29f56373fe89f1ca471f64472d
--- /dev/null
+++ b/sample_dataset.py
@@ -0,0 +1,36 @@
+import pickle
+import os
+import random
+
+# 设置随机种子
+random.seed(1120)
+
+# 输入文件路径
+input_pkl_path = "/data1/tpz/nwm-main/data_splits/airvln_16/test/dataset_dist_64_to_64_n16_len_traj_pred_64.pkl"
+# 输出文件路径
+output_pkl_path = "/data1/tpz/nwm-main/data_splits/airvln_16/test/navigation_eval_16_long.pkl"
+
+print(f"正在读取文件: {input_pkl_path}")
+
+# 读取原始数据
+with open(input_pkl_path, "rb") as f:
+    data = pickle.load(f)[0]
+
+print(f"✅ 成功读取文件")
+print(f"数据类型: {type(data)}")
+print(f"原始数据包含 {len(data)} 条记录")
+
+# 从list中随机采样1000条
+import random
+random.seed(1120)
+sampled_data = random.sample(data, k=100)
+print(f"采样后包含 {len(sampled_data)} 条记录")
+
+# 保存采样后的数据
+os.makedirs(os.path.dirname(output_pkl_path), exist_ok=True)
+with open(output_pkl_path, "wb") as f:
+    pickle.dump(sampled_data, f)
+
+print(f"\n✅ 已保存采样数据到: {output_pkl_path}")
+print(f"使用随机种子: 1120")
+
diff --git a/sample_trajectories.py b/sample_trajectories.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cad2617bd10ad92c09055f2fdee8ac3a2b948b7
--- /dev/null
+++ b/sample_trajectories.py
@@ -0,0 +1,118 @@
+import yaml, numpy as np, pickle, os, torch
+from datasets_v4 import TrajectoryEvalDataset
+from misc import transform
+from rule_based_trajectory_generation import trajectory_generation_rule_based
+from noise_trajectory_generation import trajectory_generation_random
+
+def get_dataset_eval(config, dataset_name, predefined_index=True):
+    data_config = config["eval_datasets"][dataset_name]
+    if predefined_index:
+        predefined_index = f"data_splits/{dataset_name}/test/navigation_eval_16_long.pkl"
+    else:
+        predefined_index = None
+
+    dataset = TrajectoryEvalDataset(
+                data_folder=data_config["data_folder"],
+                data_split_folder=data_config["test"],
+                dataset_name=dataset_name,
+                image_size=config["image_size"],
+                min_dist_cat=config["trajectory_eval_distance"]["min_dist_cat"],
+                max_dist_cat=config["trajectory_eval_distance"]["max_dist_cat"],
+                len_traj_pred=config["trajectory_eval_len_traj_pred"],
+                traj_stride=config["traj_stride"],
+                context_size=config["trajectory_eval_context_size"],
+                normalize=config["normalize"],
+                transform=transform,
+                predefined_index=predefined_index,
+                traj_names="traj_names.txt"
+            )
+
+    return dataset
+
+def sample_trajectories(dim=3, input_fps=4, output_fps=1):
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+
+    with open("config/nwm_cdit_airvln_16.yaml", "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+
+    # 计算降采样步长
+    stride = input_fps // output_fps
+    print(f"Downsampling from {input_fps}fps to {output_fps}fps with stride={stride}")
+
+    dataset_name, candidate_number = "airvln_16", 3
+    dataset = get_dataset_eval(config, dataset_name, predefined_index=True)
+    # 获取 spacing（米/waypoint）- 从 data_config.yaml 加载
+    with open("config/data_config.yaml", "r") as f:
+        data_config = yaml.safe_load(f)
+    spacing = float(data_config[dataset_name]['metric_waypoint_spacing'])
+    
+    all_sampled_trajectories = {}
+    for i in range(len(dataset)):
+        idxs, _, _, gt_actions, _, _ = dataset[i]
+        
+        # 降采样：将每 stride 个时间步的动作累积起来
+        # 与 isolated_nwm_infer_v5.py 中的 generate_rollout 逻辑一致：
+        # delta = delta.unflatten(1, (-1, rollout_stride)).sum(2)
+        # gt_image = gt_image[:, rollout_stride-1::rollout_stride] (从0.75s开始采样)
+        # gt_actions shape: [T, 4]
+        if isinstance(gt_actions, torch.Tensor):
+            gt_actions_tensor = gt_actions
+        else:
+            gt_actions_tensor = torch.from_numpy(gt_actions)
+        
+        # 先基于原始GT轨迹（不降采样）生成候选轨迹
+        # gt_actions 是绝对位置（waypoint单位，因为数据集normalize=True）
+        gt_xyz_waypoint = gt_actions_tensor[:, :3].cpu().numpy()  # [T, 3] - waypoint单位，绝对位置
+        # 添加起点 (0,0,0)
+        gt_xyz_waypoint = np.concatenate([np.zeros((1, 3), dtype=np.float32), gt_xyz_waypoint], axis=0)  # [T+1, 3] - waypoint单位
+        # 转换为米单位（用于生成候选轨迹）
+        gt_xyz_meters = gt_xyz_waypoint * spacing  # [T+1, 3] 米单位
+        gt_yaw = np.zeros((gt_xyz_meters.shape[0],), dtype=np.float32)
+        GT_traj = [(float(x), float(y), float(z), float(yaw)) for (x, y, z), yaw in zip(gt_xyz_meters, gt_yaw)]
+
+        # 基于原始GT轨迹生成候选轨迹（不降采样，米单位）
+        candidate_trajectories = trajectory_generation_random(GT_traj, candidate_number=1, dim=dim) + \
+                                trajectory_generation_rule_based(GT_traj, candidate_number=candidate_number-1, dim=dim)
+        candidate_trajectories = np.array(candidate_trajectories, dtype=np.float32)  # [N, T+1, 4] - 原始长度
+        
+        # 对每个候选轨迹进行降采样
+        # 将 T 调整为 stride 的倍数
+        T = candidate_trajectories.shape[1] - 1  # 轨迹点数量（去掉起点）
+        T_aligned = (T // stride) * stride
+        candidate_trajectories_aligned = candidate_trajectories[:, :T_aligned+1, :]  # [N, T_aligned+1, 4]
+        
+        # 降采样：计算增量，将每 stride 个增量累积起来，然后重新累积得到位置
+        # 从索引 0 开始采样，实现秒数对齐（0s, 1s, 2s, ...）
+        # 对应的时间点：0s, 1s, 2s, 3s, ... (即 0, stride, 2*stride, 3*stride, ...)
+        # 每个时间点的动作是前 stride 个增量的累积：delta[0:4], delta[4:8], delta[8:12], ...
+        candidate_trajectories_downsampled = []
+        for n in range(candidate_trajectories_aligned.shape[0]):
+            traj = candidate_trajectories_aligned[n]  # [T_aligned+1, 4]
+            # 计算增量（相邻点之间的差值）
+            traj_deltas = traj[1:, :] - traj[:-1, :]  # [T_aligned, 4]
+            # 将每 stride 个增量累积起来
+            traj_deltas_reshaped = traj_deltas.reshape(T_aligned // stride, stride, 4)  # [T_down, stride, 4]
+            traj_deltas_downsampled = traj_deltas_reshaped.sum(axis=1)  # [T_down, 4]
+            # 从起点累积得到降采样后的轨迹点
+            traj_start = traj[0:1, :]  # [1, 4] 起点
+            traj_cumsum = np.cumsum(traj_deltas_downsampled, axis=0)  # [T_down, 4] 累积增量
+            traj_downsampled = np.concatenate([
+                traj_start,  # 起点保持不变
+                traj_cumsum + traj_start  # 从起点累积增量
+            ], axis=0)  # [T_down+1, 4]
+            candidate_trajectories_downsampled.append(traj_downsampled)
+        
+        candidate_trajectories = np.array(candidate_trajectories_downsampled, dtype=np.float32)  # [N, T_down+1, 4]
+        all_sampled_trajectories[int(idxs.item())] = candidate_trajectories
+        print(f"Processed {i+1}/{len(dataset)} trajectories", end='\r')
+
+    save_path = f"data_splits/{dataset_name}/test/{dataset_name}_{candidate_number}_trajectories_long.pkl"
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    pickle.dump(all_sampled_trajectories, open(save_path, "wb"))
+    print(f"Saved {len(all_sampled_trajectories)} sampled trajectories to {save_path}")
+
+if __name__ == "__main__":
+    sample_trajectories(dim=3, input_fps=4, output_fps=1)
diff --git a/see_pkl.ipynb b/see_pkl.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9537f9727b42c9e43d5a1b2d6ade593970e0fa98
--- /dev/null
+++ b/see_pkl.ipynb
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "[('jackal_2020-01-05-13-41-08_6_r00', 27, -27, 64), ('jackal_2019-08-20-18-12-19_29_r00', 27, -27, 64), ('jackal_2019-08-02-17-18-37_42_r00', 107, -64, 64), ('jackal_2019-10-31-16-41-10_20_r04', 75, -64, 64), ('jackal_2019-08-02-17-09-37_33_r03', 99, -64, 64), ('jackal_2019-09-19-15-26-49_2_r02', 27, -27, 64), ('jackal_2019-08-13-14-57-15_6_r01', 11, -11, 64), ('jackal_2019-08-13-15-37-03_2_r01', 99, -64, 64), ('jackal_2019-12-21-12-14-24_7_r00', 11, -11, 64), ('jackal_2019-10-31-16-44-10_23_r04', 51, -51, 64), ('jackal_2019-08-06-13-14-25_4_r03', 19, -19, 64), ('jackal_2019-08-18-15-54-02_55_r00', 27, -27, 64), ('jackal_2019-12-21-13-38-15_5_r01', 91, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 19, -19, 64), ('jackal_2019-12-21-13-41-15_8_r00', 11, -11, 64), ('jackal_2019-12-19-12-48-05_2_r03', 171, -64, 64), ('jackal_2019-10-24-17-26-09_15_r04', 99, -64, 64), ('jackal_2019-08-06-15-15-31_1_r02', 43, -43, 64), ('jackal_2020-01-05-15-15-34_1_r00', 163, -64, 64), 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('jackal_2019-09-12-15-35-34_42_r02', 19, -19, 64), ('jackal_2019-08-18-15-54-02_55_r00', 35, -35, 64), ('jackal_2019-08-08-16-06-01_8_r01', 27, -27, 64), ('jackal_2019-10-31-11-15-02_5_r01', 35, -35, 64), ('jackal_2019-08-08-15-22-05_4_r01', 75, -64, 64), ('jackal_2020-01-05-13-55-37_3_r00', 139, -64, 64)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open('data_splits/recon/test/time.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "[('jackal_2019-10-24-13-35-07_2_r02', 123, -64, 64), ('jackal_2019-10-24-13-35-07_2_r02', 11, -11, 64), ('jackal_2020-01-04-14-10-45_1_r01', 27, -27, 64), ('jackal_2020-01-04-14-10-45_1_r01', 171, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 35, -35, 64), ('jackal_2020-01-04-14-10-45_1_r01', 19, -19, 64), ('jackal_2019-09-12-16-20-46_14_r01', 27, -27, 64), ('jackal_2019-09-12-16-20-46_14_r01', 11, -11, 64), ('jackal_2019-12-21-12-16-24_9_r01', 83, -64, 64), ('jackal_2019-08-13-16-22-26_10_r02', 19, -19, 64), ('jackal_2019-12-21-12-16-24_9_r01', 27, -27, 64), ('jackal_2019-12-21-12-16-24_9_r01', 139, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 91, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 67, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 123, -64, 64), ('jackal_2019-09-12-16-20-46_14_r01', 59, -59, 64), ('jackal_2019-08-02-17-42-37_66_r00', 3, -3, 64), ('jackal_2019-08-20-15-36-44_4_r01', 67, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 227, -64, 64), ('jackal_2019-09-17-17-14-10_9_r01', 51, -51, 64), ('jackal_2019-08-20-15-36-44_4_r01', 3, -3, 64), ('jackal_2019-08-02-17-42-37_66_r00', 19, -19, 64), ('jackal_2019-08-20-15-36-44_4_r01', 91, -64, 64), ('jackal_2019-09-17-17-35-40_7_r02', 35, -35, 64), ('jackal_2019-12-21-12-16-24_9_r01', 43, -43, 64), ('jackal_2020-01-04-14-10-45_1_r01', 115, -64, 64), ('jackal_2019-09-12-16-20-46_14_r01', 51, -51, 64), ('jackal_2019-08-08-17-03-33_21_r01', 11, -11, 64), ('jackal_2019-09-17-17-14-10_9_r01', 19, -19, 64), ('jackal_2019-08-06-14-12-43_19_r00', 43, -43, 64), ('jackal_2020-01-04-14-10-45_1_r01', 3, -3, 64), ('jackal_2019-12-21-12-16-24_9_r01', 195, -64, 64), ('jackal_2019-09-09-16-50-52_56_r00', 3, -3, 64), ('jackal_2020-01-04-14-10-45_1_r01', 11, -11, 64), ('jackal_2020-01-04-14-10-45_1_r01', 59, -59, 64), ('jackal_2019-09-17-17-35-40_7_r02', 59, -59, 64), ('jackal_2019-09-12-16-20-46_14_r01', 43, -43, 64), ('jackal_2019-12-21-12-16-24_9_r01', 155, -64, 64), ('jackal_2019-12-21-14-22-04_13_r00', 27, -27, 64), ('jackal_2019-08-08-17-03-33_21_r01', 35, -35, 64), ('jackal_2020-01-05-14-18-28_0_r00', 27, -27, 64), ('jackal_2020-01-04-14-10-45_1_r01', 99, -64, 64), ('jackal_2020-01-03-15-33-07_3_r01', 3, -3, 64), ('jackal_2019-12-21-12-16-24_9_r01', 163, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 35, -35, 64), ('jackal_2019-10-24-13-35-07_2_r02', 107, -64, 64), ('jackal_2019-10-24-13-35-07_2_r02', 43, -43, 64), ('jackal_2019-12-21-12-16-24_9_r01', 51, -51, 64), ('jackal_2019-12-21-12-16-24_9_r01', 99, -64, 64), ('jackal_2019-10-24-13-35-07_2_r02', 35, -35, 64), ('jackal_2019-12-21-12-16-24_9_r01', 211, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 3, -3, 64), ('jackal_2019-12-21-14-22-04_13_r00', 43, -43, 64), ('jackal_2020-01-04-14-10-45_1_r01', 75, -64, 64), ('jackal_2019-10-24-13-35-07_2_r02', 59, -59, 64), ('jackal_2019-08-20-15-36-44_4_r01', 11, -11, 64), ('jackal_2019-12-21-12-16-24_9_r01', 75, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 163, -64, 64), ('jackal_2019-08-29-16-59-25_0_r02', 3, -3, 64), ('jackal_2019-10-24-13-35-07_2_r02', 75, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 187, -64, 64), ('jackal_2019-09-17-17-14-10_9_r01', 35, -35, 64), ('jackal_2019-08-06-14-12-43_19_r00', 27, -27, 64), ('jackal_2020-01-03-15-33-07_3_r01', 19, -19, 64), ('jackal_2019-12-21-12-16-24_9_r01', 187, -64, 64), ('jackal_2019-08-06-14-12-43_19_r00', 11, -11, 64), ('jackal_2019-08-13-16-22-26_10_r02', 11, -11, 64), ('jackal_2019-08-02-17-42-37_66_r00', 11, -11, 64), ('jackal_2020-01-05-14-18-28_0_r00', 35, -35, 64), ('jackal_2020-01-05-14-18-28_0_r00', 43, -43, 64), ('jackal_2020-01-05-12-41-48_0_r00', 11, -11, 64), ('jackal_2020-01-05-14-18-28_0_r00', 67, -64, 64), ('jackal_2019-08-08-17-03-33_21_r01', 3, -3, 64), ('jackal_2019-08-08-17-03-33_21_r01', 27, -27, 64), ('jackal_2019-12-21-12-16-24_9_r01', 11, -11, 64), ('jackal_2020-01-03-15-33-07_3_r01', 11, -11, 64), ('jackal_2019-12-21-12-16-24_9_r01', 19, -19, 64), ('jackal_2019-12-21-12-16-24_9_r01', 203, -64, 64), ('jackal_2019-09-17-17-35-40_7_r02', 11, -11, 64), ('jackal_2019-10-24-13-35-07_2_r02', 3, -3, 64), ('jackal_2019-08-20-15-36-44_4_r01', 43, -43, 64), ('jackal_2019-08-06-14-12-43_19_r00', 3, -3, 64), ('jackal_2019-08-08-17-03-33_21_r01', 43, -43, 64), ('jackal_2020-01-05-12-41-48_0_r00', 3, -3, 64), ('jackal_2020-01-04-14-10-45_1_r01', 179, -64, 64), ('jackal_2019-10-24-13-35-07_2_r02', 91, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 51, -51, 64), ('jackal_2019-10-24-13-35-07_2_r02', 131, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 235, -64, 64), ('jackal_2019-09-12-16-20-46_14_r01', 67, -64, 64), ('jackal_2020-01-05-14-18-28_0_r00', 75, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 91, -64, 64), ('jackal_2019-09-12-16-20-46_14_r01', 35, -35, 64), ('jackal_2019-12-21-12-16-24_9_r01', 107, -64, 64), ('jackal_2019-08-20-15-36-44_4_r01', 99, -64, 64), ('jackal_2020-01-05-14-18-28_0_r00', 51, -51, 64), ('jackal_2019-08-08-17-03-33_21_r01', 19, -19, 64), ('jackal_2019-12-21-12-16-24_9_r01', 67, -64, 64), ('jackal_2019-08-06-15-48-05_13_r01', 3, -3, 64), ('jackal_2019-10-24-13-35-07_2_r02', 51, -51, 64), ('jackal_2019-10-24-13-35-07_2_r02', 99, -64, 64), ('jackal_2019-09-17-17-35-40_7_r02', 43, -43, 64), ('jackal_2019-12-21-12-16-24_9_r01', 219, -64, 64), ('jackal_2019-08-20-15-36-44_4_r01', 35, -35, 64), ('jackal_2019-10-24-13-35-07_2_r02', 19, -19, 64), ('jackal_2019-08-06-14-12-43_19_r00', 19, -19, 64), ('jackal_2019-09-17-17-35-40_7_r02', 3, -3, 64), ('jackal_2019-08-20-15-36-44_4_r01', 83, -64, 64), ('jackal_2019-12-21-14-22-04_13_r00', 19, -19, 64), ('jackal_2020-01-05-14-18-28_0_r00', 3, -3, 64), ('jackal_2020-01-04-14-10-45_1_r01', 139, -64, 64), ('jackal_2020-01-05-14-18-28_0_r00', 83, -64, 64), ('jackal_2019-08-20-15-36-44_4_r01', 75, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 267, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 147, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 83, -64, 64), ('jackal_2019-09-12-16-20-46_14_r01', 19, -19, 64), ('jackal_2020-01-04-14-10-45_1_r01', 131, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 243, -64, 64), ('jackal_2019-09-17-17-14-10_9_r01', 11, -11, 64), ('jackal_2019-10-24-13-35-07_2_r02', 115, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 155, -64, 64), ('jackal_2019-12-21-12-16-24_9_r01', 59, -59, 64), ('jackal_2019-08-13-16-22-26_10_r02', 3, -3, 64), ('jackal_2019-08-02-17-42-37_66_r00', 35, -35, 64), ('jackal_2019-12-21-12-16-24_9_r01', 251, -64, 64), ('jackal_2019-09-17-17-14-10_9_r01', 43, -43, 64), ('jackal_2019-08-20-15-36-44_4_r01', 59, -59, 64), ('jackal_2019-10-24-13-35-07_2_r02', 83, -64, 64), ('jackal_2019-12-19-12-25-57_1_r01', 3, -3, 64), ('jackal_2019-12-21-12-16-24_9_r01', 179, -64, 64), ('jackal_2019-10-24-13-35-07_2_r02', 27, -27, 64), ('jackal_2019-12-21-14-22-04_13_r00', 11, -11, 64), ('jackal_2019-08-20-15-36-44_4_r01', 51, -51, 64), ('jackal_2020-01-05-14-18-28_0_r00', 19, -19, 64), ('jackal_2020-01-03-15-33-07_3_r01', 27, -27, 64), ('jackal_2019-09-17-17-35-40_7_r02', 51, -51, 64), ('jackal_2019-12-21-12-16-24_9_r01', 115, -64, 64), ('jackal_2019-12-21-14-22-04_13_r00', 35, -35, 64), ('jackal_2019-09-09-13-51-02_1_r01', 3, -3, 64), ('jackal_2019-12-21-12-16-24_9_r01', 171, -64, 64), ('jackal_2019-09-17-17-35-40_7_r02', 19, -19, 64), ('jackal_2020-01-05-14-18-28_0_r00', 11, -11, 64), ('jackal_2019-08-02-17-42-37_66_r00', 27, -27, 64), ('jackal_2019-12-21-12-16-24_9_r01', 147, -64, 64), ('jackal_2019-08-08-17-03-33_21_r01', 51, -51, 64), ('jackal_2019-08-20-15-36-44_4_r01', 19, -19, 64), ('jackal_2019-10-24-13-35-07_2_r02', 155, -64, 64), ('jackal_2020-01-04-14-10-45_1_r01', 43, -43, 64), ('jackal_2020-01-04-14-10-45_1_r01', 107, -64, 64)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('data_splits/recon/test/rollout.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "100\n",
+      "[('jackal_2019-08-06-14-12-43_19_r00', 59, 8, 8), ('jackal_2019-09-12-16-20-46_14_r01', 83, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 123, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 3, 8, 8), ('jackal_2019-09-12-16-20-46_14_r01', 75, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 27, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 3, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 83, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 123, 8, 8), ('jackal_2019-08-13-14-18-43_3_r01', 11, 8, 8), ('jackal_2019-09-12-16-20-46_14_r01', 99, 8, 8), ('jackal_2019-08-13-17-28-39_21_r01', 43, 8, 8), ('jackal_2019-08-02-16-56-37_20_r00', 35, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 203, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 91, 8, 8), ('jackal_2019-08-18-17-49-03_0_r00', 43, 8, 8), ('jackal_2019-08-29-16-59-25_0_r02', 11, 8, 8), ('jackal_2019-09-17-17-14-10_9_r01', 75, 8, 8), ('jackal_2020-01-03-13-30-05_6_r04', 19, 8, 8), ('jackal_2019-08-29-16-57-48_3_r01', 3, 8, 8), ('jackal_2020-01-05-12-41-48_0_r00', 35, 8, 8), ('jackal_2019-09-17-17-35-40_7_r02', 75, 8, 8), ('jackal_2019-08-02-16-56-37_20_r00', 3, 8, 8), ('jackal_2019-12-21-14-22-04_13_r00', 19, 8, 8), ('jackal_2019-09-17-17-14-10_9_r01', 51, 8, 8), ('jackal_2019-08-13-16-22-26_10_r02', 59, 8, 8), ('jackal_2020-01-05-14-18-28_0_r00', 115, 8, 8), ('jackal_2019-09-09-16-50-52_56_r00', 3, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 19, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 139, 8, 8), ('jackal_2019-08-06-15-48-05_13_r01', 35, 8, 8), ('jackal_2019-10-31-11-29-33_3_r00', 19, 8, 8), ('jackal_2019-08-13-17-28-39_21_r01', 11, 8, 8), ('jackal_2020-01-05-13-35-08_0_r01', 11, 8, 8), ('jackal_2019-08-13-16-22-26_10_r02', 51, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 75, 8, 8), ('jackal_2019-08-08-17-03-33_21_r01', 51, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 75, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 171, 8, 8), ('jackal_2019-10-24-13-35-07_2_r02', 107, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 195, 8, 8), ('jackal_2020-01-05-12-41-48_0_r00', 19, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 267, 8, 8), ('jackal_2019-09-09-16-50-52_56_r00', 19, 8, 8), ('jackal_2019-12-21-14-22-04_13_r00', 27, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 187, 8, 8), ('jackal_2019-10-20-16-29-40_11_r02', 3, 8, 8), ('jackal_2020-01-05-14-18-28_0_r00', 3, 8, 8), ('jackal_2019-09-17-17-35-40_7_r02', 67, 8, 8), ('jackal_2019-09-17-17-35-40_7_r02', 123, 8, 8), ('jackal_2019-08-13-16-22-26_10_r02', 27, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 91, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 83, 8, 8), ('jackal_2019-08-18-15-01-02_2_r00', 27, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 147, 8, 8), ('jackal_2020-01-03-15-33-07_3_r01', 43, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 59, 8, 8), ('jackal_2019-08-13-16-22-26_10_r02', 67, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 83, 8, 8), ('jackal_2019-08-06-15-48-05_13_r01', 27, 8, 8), ('jackal_2020-01-05-14-18-28_0_r00', 131, 8, 8), ('jackal_2019-09-12-16-20-46_14_r01', 3, 8, 8), ('jackal_2020-01-05-13-35-08_0_r01', 43, 8, 8), ('jackal_2019-08-02-17-42-37_66_r00', 51, 8, 8), ('jackal_2019-08-13-17-45-39_38_r00', 11, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 203, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 131, 8, 8), ('jackal_2019-12-21-14-22-04_13_r00', 99, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 171, 8, 8), ('jackal_2019-08-02-16-56-37_20_r00', 19, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 35, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 259, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 307, 8, 8), ('jackal_2019-09-12-16-20-46_14_r01', 59, 8, 8), ('jackal_2020-01-03-13-30-05_6_r04', 27, 8, 8), ('jackal_2019-08-13-16-00-57_18_r02', 35, 8, 8), ('jackal_2019-08-20-15-19-11_33_r00', 3, 8, 8), ('jackal_2019-10-31-16-55-31_2_r04', 27, 8, 8), ('jackal_2019-08-18-15-01-02_2_r00', 3, 8, 8), ('jackal_2019-10-31-11-29-33_3_r00', 11, 8, 8), ('jackal_2019-09-17-17-35-40_7_r02', 99, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 147, 8, 8), ('jackal_2019-08-27-16-21-56_7_r00', 19, 8, 8), ('jackal_2019-08-27-15-04-25_13_r01', 11, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 147, 8, 8), ('jackal_2019-09-17-17-35-40_7_r02', 91, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 27, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 219, 8, 8), ('jackal_2019-08-13-17-02-54_1_r04', 3, 8, 8), ('jackal_2019-09-17-14-49-52_2_r01', 19, 8, 8), ('jackal_2019-09-09-16-50-52_56_r00', 43, 8, 8), ('jackal_2020-01-03-13-30-05_6_r04', 35, 8, 8), ('jackal_2019-08-27-15-04-25_13_r01', 35, 8, 8), ('jackal_2019-12-21-12-16-24_9_r01', 51, 8, 8), ('jackal_2019-09-17-17-14-10_9_r01', 67, 8, 8), ('jackal_2020-01-05-14-18-28_0_r00', 91, 8, 8), ('jackal_2019-08-29-16-59-25_0_r02', 43, 8, 8), ('jackal_2019-08-29-16-59-25_0_r02', 19, 8, 8), ('jackal_2019-08-20-15-36-44_4_r01', 99, 8, 8), ('jackal_2020-01-04-14-10-45_1_r01', 163, 8, 8)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('data_splits/recon/test/navigation_eval.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "print(len(data))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'tuple'>\n",
+      "66\n",
+      "([('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 3, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 11, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 19, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 27, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 35, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 43, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 51, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 59, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 67, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 75, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 83, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 91, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 99, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 107, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 115, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 123, 8, 8), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 131, 8, 8), 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('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 521), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 522), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 523), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 524), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 525), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 526), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 527), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 528), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 529), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 530), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 531), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 532), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 533), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 534), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 535), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 536), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 537), ('32UTUBMZ7PU1BPNAEP5B02AVF2CVBG_processed', 538)])\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('data_splits/airvln/test/dataset_dist_8_to_8_n4_len_traj_pred_8.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "print(len(data[0]))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'tuple'>\n",
+      "24\n",
+      "([('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 3, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 11, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 19, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 27, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 35, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 43, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 51, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 59, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 67, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 75, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 83, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 91, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 99, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 107, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 115, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 123, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 131, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 139, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 147, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 155, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 163, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 171, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 179, 8, 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 187, 8, 8)], [('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 0), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 1), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 2), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 3), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 4), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 5), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 6), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 7), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 8), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 9), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 10), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 11), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 12), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 13), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 14), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 15), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 16), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 17), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 18), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 19), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 20), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 21), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 22), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 23), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 24), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 25), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 26), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 27), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 28), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 29), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 30), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 31), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 32), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 33), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 34), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 35), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 36), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 37), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 38), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 39), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 40), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 41), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 42), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 43), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 44), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 45), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 46), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 47), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 48), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 49), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 50), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 51), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 52), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 53), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 54), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 55), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 56), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 57), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 58), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 59), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 60), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 61), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 62), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 63), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 64), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 65), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 66), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 67), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 68), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 69), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 70), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 71), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 72), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 73), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 74), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 75), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 76), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 77), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 78), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 79), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 80), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 81), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 82), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 83), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 84), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 85), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 86), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 87), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 88), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 89), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 90), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 91), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 92), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 93), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 94), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 95), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 96), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 97), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 98), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 99), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 100), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 101), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 102), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 103), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 104), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 105), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 106), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 107), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 108), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 109), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 110), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 111), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 112), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 113), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 114), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 115), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 116), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 117), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 118), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 119), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 120), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 121), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 122), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 123), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 124), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 125), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 126), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 127), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 128), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 129), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 130), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 131), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 132), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 133), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 134), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 135), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 136), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 137), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 138), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 139), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 140), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 141), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 142), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 143), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 144), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 145), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 146), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 147), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 148), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 149), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 150), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 151), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 152), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 153), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 154), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 155), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 156), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 157), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 158), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 159), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 160), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 161), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 162), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 163), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 164), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 165), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 166), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 167), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 168), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 169), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 170), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 171), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 172), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 173), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 174), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 175), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 176), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 177), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 178), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 179), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 180), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 181), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 182), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 183), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 184), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 185), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 186), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 187), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 188), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 189), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 190), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 191), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 192), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 193), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 194), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 195), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 196), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 197), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 198), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 199), ('3AJA9FLWTKWJGGIXUHNMFKGYXAAIFA_processed', 200)])\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('data_splits/airvln_16/test/dataset_dist_8_to_8_n4_len_traj_pred_8.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "print(len(data[0]))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "10968\n",
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+     ]
+    }
+   ],
+   "source": [
+    "with open('data_splits/airvln_16/test/dataset_dist_8_to_8_n4_len_traj_pred_8_all.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)[0]\n",
+    "\n",
+    "\n",
+    "print(type(data))\n",
+    "print(len(data))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "random.seed(1120)\n",
+    "data = random.sample(data, k=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open('data_splits/airvln_16/test/navigation_eval.pkl', 'wb') as f:\n",
+    "    pickle.dump(data, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "100\n",
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+     ]
+    }
+   ],
+   "source": [
+    "with open('data_splits/airvln_16/test/navigation_eval.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "print(len(data))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'tuple'>\n",
+      "9190\n",
+      "([('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 3, -3, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 11, -11, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 19, -19, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 27, -27, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 35, -35, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 43, -43, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 51, -51, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 59, -59, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 67, -64, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 75, -64, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 83, -64, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 91, -64, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 99, -64, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 107, -64, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 115, -64, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 123, -64, 64), ('3BDORL6HLSBP6316T2RUL6WMNHBCRJ_processed', 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+     ]
+    }
+   ],
+   "source": [
+    "with open('data_splits/airvln_16/test/dataset_dist_-64_to_64_n4_len_traj_pred_64.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "print(len(data[0]))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "1000\n",
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-11, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 19, -19, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 27, -27, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 35, -35, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 43, -43, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 51, -51, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 59, -59, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 67, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 75, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 83, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 91, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 99, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 107, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 115, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 123, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 131, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 139, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 147, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 155, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 163, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 171, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 179, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 187, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 195, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 203, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 211, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 219, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 227, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 235, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 243, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 251, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 259, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 267, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 275, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 283, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 291, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 299, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 307, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 315, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 323, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 331, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 339, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 347, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 355, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 363, -64, 64), ('3PCPFX4U58OWXE6G7PSVOM7KNOGQFH_processed', 371, -64, 64)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('data_splits/airvln_16/test/rollout.pkl', 'wb') as f:\n",
+    "    pickle.dump(data[0][:1000], f)\n",
+    "with open('data_splits/airvln_16/test/rollout.pkl', 'rb') as f:\n",
+    "    data = pickle.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "print(len(data))\n",
+    "print(data)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nwm2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/submitit_train_cw.py b/submitit_train_cw.py
new file mode 100644
index 0000000000000000000000000000000000000000..70f7af2c85cd6a86a0dd66f425e758044b589f30
--- /dev/null
+++ b/submitit_train_cw.py
@@ -0,0 +1,127 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# A script to run multinode training with submitit.
+# --------------------------------------------------------
+
+import argparse
+import os
+import uuid
+from pathlib import Path
+
+import train as trainer
+import submitit
+
+
+def parse_args():
+    trainer_parser = trainer.get_args_parser()
+    parser = argparse.ArgumentParser(add_help=False, parents=[trainer_parser])
+    parser.add_argument("--ngpus", default=8, type=int, help="Number of gpus to request on each node")
+    parser.add_argument("--nodes", default=1, type=int, help="Number of nodes to request")
+    parser.add_argument("--timeout", default=4320, type=int, help="Duration of the job")
+    parser.add_argument("--job_dir", default="", type=str, help="Job dir. Leave empty for automatic.")
+    parser.add_argument("--partition", default="learn", type=str, help="Partition where to submit")
+    parser.add_argument("--qos", default="low", type=str, help="Partition where to submit")
+    parser.add_argument("--use_volta32", action='store_true', help="Request 32G V100 GPUs")
+    return parser.parse_args()
+
+
+def get_shared_folder() -> Path:
+    user = os.getenv("USER")
+    if Path("/checkpoint/").is_dir():
+        p = Path(f"/checkpoint/amaia/video/{user}/experiments")
+        p.mkdir(exist_ok=True)
+        return p
+    raise RuntimeError("No shared folder available")
+
+
+def get_init_file():
+    # Init file must not exist, but it's parent dir must exist.
+    os.makedirs(str(get_shared_folder()), exist_ok=True)
+    init_file = get_shared_folder() / f"{uuid.uuid4().hex}_init"
+    if init_file.exists():
+        os.remove(str(init_file))
+    return init_file
+
+
+class Trainer(object):
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        import train as trainer
+        self._setup_gpu_args()
+        trainer.main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        self.args.dist_url = get_init_file().as_uri()
+        print("Requeuing ", self.args)
+        empty_trainer = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty_trainer)
+
+    def _setup_gpu_args(self):
+        import submitit
+        from pathlib import Path
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = Path(str(self.args.output_dir).replace("%j", str(job_env.job_id)))
+        self.args.log_dir = self.args.output_dir
+        self.args.gpu = job_env.local_rank
+        self.args.rank = job_env.global_rank
+        self.args.world_size = job_env.num_tasks
+        print(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+
+
+def main():
+    args = parse_args()
+    if args.job_dir == "":
+        args.job_dir = get_shared_folder() / "%j"
+
+    # Note that the folder will depend on the job_id, to easily track experiments
+    executor = submitit.AutoExecutor(folder=args.job_dir, slurm_max_num_timeout=30)
+
+    num_gpus_per_node = args.ngpus
+    nodes = args.nodes
+    timeout_min = args.timeout
+
+    partition = args.partition
+    kwargs = {}
+    
+    if args.use_volta32:
+        kwargs['slurm_constraint'] = 'volta32gb'
+
+    executor.update_parameters(
+        mem_gb=55 * num_gpus_per_node,
+        gpus_per_node=num_gpus_per_node,
+        tasks_per_node=num_gpus_per_node,
+        cpus_per_task=12,
+        nodes=nodes,
+        timeout_min=timeout_min,  # max is 60 * 72
+        qos=args.qos,
+        # Below are cluster dependent parameters
+        slurm_partition=partition,
+        slurm_signal_delay_s=120,
+        slurm_account='fair_amaia_cw_video',
+        **kwargs
+    )
+
+    executor.update_parameters(name="nwm")
+
+    args.dist_url = get_init_file().as_uri()
+    args.output_dir = args.job_dir
+    args.log_dir = args.job_dir
+
+    trainer = Trainer(args)
+    job = executor.submit(trainer)
+
+    print("Submitted job_id:", job.job_id)
+    print(job.job_id)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/train/train.py b/train/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..95fda707ebc5d149b692dfe455d787d846ff0969
--- /dev/null
+++ b/train/train.py
@@ -0,0 +1,443 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models import CDiT_models
+from diffusion import create_diffusion
+from datasets import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+
+    # Override ckpt/eval frequency from config if present
+    if "ckpt_every" in config:
+        args.ckpt_every = config["ckpt_every"]
+    if "eval_every" in config:
+        args.eval_every = config["eval_every"]
+
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_airvln.py b/train/train_airvln.py
new file mode 100644
index 0000000000000000000000000000000000000000..20794a1d641a8b070c41cbbd46da46281787dbeb
--- /dev/null
+++ b/train/train_airvln.py
@@ -0,0 +1,439 @@
+# same as train_recon_v2.py
+# --------------------------------------------------------
+
+from isolated_nwm_infer_recon_v2 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v2 import CDiT_models
+from diffusion import create_diffusion
+from datasets_recon import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_recon.py b/train/train_recon.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9494633297cc3325d108e9a2156601aea36af6a
--- /dev/null
+++ b/train/train_recon.py
@@ -0,0 +1,446 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_recon_v0 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v0 import CDiT_models
+from diffusion import create_diffusion
+from datasets_recon import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_recon_v2.py b/train/train_recon_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd5c35888df44ad82050763770df06b3423b0d74
--- /dev/null
+++ b/train/train_recon_v2.py
@@ -0,0 +1,446 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_recon_v2 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v2 import CDiT_models
+from diffusion import create_diffusion
+from datasets_recon import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v0.py b/train/train_v0.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d8920c1ee97c81a04191405f8bd75550b9485af
--- /dev/null
+++ b/train/train_v0.py
@@ -0,0 +1,446 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_v0 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v0 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v0 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v1.py b/train/train_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcddfb9da67e3d85145c857cdc3f3f1b7b6081c8
--- /dev/null
+++ b/train/train_v1.py
@@ -0,0 +1,446 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_v1 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v1 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v1 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v2.py b/train/train_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..1013f0e0b97b71f56bda53a3bfe0e15c18531083
--- /dev/null
+++ b/train/train_v2.py
@@ -0,0 +1,446 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_v2 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v2 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v0 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v3.py b/train/train_v3.py
new file mode 100644
index 0000000000000000000000000000000000000000..4395c47cfb21c99fd09572fcc928abdd7b99c129
--- /dev/null
+++ b/train/train_v3.py
@@ -0,0 +1,446 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_v1 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v3 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v1 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v4.py b/train/train_v4.py
new file mode 100644
index 0000000000000000000000000000000000000000..517d1bd1c5f1e7d17b748bd91fff6a1ae541292b
--- /dev/null
+++ b/train/train_v4.py
@@ -0,0 +1,446 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_v4 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v4_ca import CDiT_models
+from diffusion import create_diffusion
+from datasets_v1 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v5.py b/train/train_v5.py
new file mode 100644
index 0000000000000000000000000000000000000000..a14bb74a72919b3d7b338aa2572d2dc8a2b0d046
--- /dev/null
+++ b/train/train_v5.py
@@ -0,0 +1,448 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_v5 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v5 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v2 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler(enabled=True)
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            # print(f"aug shape: {aug.size()}")
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                aug = aug.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_supervised=aug)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug in loader:
+        x = x.to(device)
+        y = y.to(device)
+        rel_t = rel_t.to(device).flatten(0, 1)
+        aug = aug.to(device)
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v6.py b/train/train_v6.py
new file mode 100644
index 0000000000000000000000000000000000000000..059b91290e04231cd9d6cb5e5197a387e6bf4710
--- /dev/null
+++ b/train/train_v6.py
@@ -0,0 +1,450 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+from isolated_nwm_infer_v6 import model_forward_wrapper
+import torch
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+from diffusers.models import AutoencoderKL
+
+from distributed import init_distributed
+from models_tpz_v6_2 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v3 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond+1, input_size=latent_size, in_channels=4).to(device)
+    # print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug, _ in loader:    
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+                
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)             # [B*num_goals, 4, 28, 28]
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)      # [B*num_goals, num_cond, 4, 28, 28]
+                y_cond = aug.unsqueeze(2).flatten(0, 1)             # [B*num_goals, 1, 4, 28, 28]
+                x_cond = torch.cat((x_cond, y_cond), dim=1)                    # [B*num_goals, num_cond+1, 4, 28, 28]                      
+                #  print(f"x_cond shape: {x_cond.size()}")
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_sup=y_cond.squeeze(1))
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug, _ in loader:
+        x = x.to(device)            # [B, num_goals+cond_num, 3, 224, 224]
+        y = y.to(device)            # [B, num_goals+cond_num, 2]
+        rel_t = rel_t.to(device).flatten(0, 1)      # [B, num_goals]
+        aug = aug.to(device)        # [B, num_goals, 3, 224, 224]
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v7.py b/train/train_v7.py
new file mode 100644
index 0000000000000000000000000000000000000000..44e7ec51abede022bde14efe9847478c48e9334a
--- /dev/null
+++ b/train/train_v7.py
@@ -0,0 +1,462 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+# 这个版本为加入相机编码版本
+
+import torch
+import torch.nn as nn
+
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+
+from diffusers.models import AutoencoderKL
+
+from isolated_nwm_infer_v7 import model_forward_wrapper
+
+from distributed import init_distributed
+from models_tpz_v7 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v3 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    # print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.cuda.amp.GradScaler()
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug, camera_mats in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)                     # [B, num_goals, 4, 28, 28]
+            camera_mats = camera_mats.to(device, non_blocking=True)     # [B, num_goals+num_cond, 4, 4]
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)             # [B*num_goals, 4, 28, 28]
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)      # [B*num_goals, num_cond, 4, 28, 28]
+                y_cond = aug.unsqueeze(2).flatten(0, 1)             # [B*num_goals, 1, 4, 28, 28]
+                # x_cond = torch.cat((x_cond, y_cond), dim=1)                    # [B*num_goals, num_cond+1, 4, 28, 28]                      
+
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                camera_mats_x_start = camera_mats[:, num_cond:].unsqueeze(2).flatten(0, 1)   # [B*num_goals, 1, 4, 4]
+                camera_mats_x_cond = camera_mats[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, 4, 4).flatten(0, 1)    # [B*num_goals, num_cond, 4, 4]
+                camera_mats_x_cond = torch.cat((camera_mats_x_cond, camera_mats_x_start), dim=1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_sup=y_cond.squeeze(1), viewmats=camera_mats_x_cond)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug, camera_mats in loader:
+        x = x.to(device)            # [B, num_goals+cond_num, 3, 224, 224]
+        y = y.to(device)            # [B, num_goals+cond_num, 2]
+        rel_t = rel_t.to(device).flatten(0, 1)      # [B, num_goals]
+        aug = aug.to(device)        # [B, num_goals, 3, 224, 224]
+        camera_mats = camera_mats.to(device)
+        
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug, camera_mats=camera_mats)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v7_1.py b/train/train_v7_1.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb5c6fb969bf4bce167348a150f4ffcbd1e0b697
--- /dev/null
+++ b/train/train_v7_1.py
@@ -0,0 +1,510 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+# 这个版本为加入相机编码版本
+
+import torch
+import torch.nn as nn
+
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+
+from diffusers.models import AutoencoderKL
+
+from isolated_nwm_infer_v7_1 import model_forward_wrapper
+
+from distributed import init_distributed
+from models_tpz_v7_1 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v3 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    # print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.amp.GradScaler('cuda')
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    
+                    print("loading dataset")
+
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug, camera_mats in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)                     # [B, num_goals, 4, 28, 28]
+            camera_mats = camera_mats.to(device, non_blocking=True)     # [B, num_goals+num_cond, 4, 4]
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))                          # [B, num_goals+num_conds, 4, 28, 28]
+                    # print("x shape, ", x.shape)
+                    
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)             # [B*num_goals, 4, 28, 28]
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)      # [B*num_goals, num_cond, 4, 28, 28]
+                y_cond = aug.unsqueeze(2).flatten(0, 1)             # [B*num_goals, 1, 4, 28, 28]
+
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                camera_mats_x_start = camera_mats[:, num_cond:].unsqueeze(2).flatten(0, 1)   # [B*num_goals, 1, 4, 4]
+                camera_mats_x_cond = camera_mats[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, 4, 4).flatten(0, 1)    # [B*num_goals, num_cond, 4, 4]
+                camera_mats_x_cond = torch.cat((camera_mats_x_cond, camera_mats_x_start), dim=1)          # [B*num_goals, num_conds+1, 4, 4]
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_sup=y_cond.squeeze(1), viewmats=camera_mats_x_cond)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            if not bfloat_enable:
+                opt.zero_grad()
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+
+            # opt.zero_grad(set_to_none=True)
+
+            # if bfloat_enable:                      # AMP / bfloat16 分支
+            #     scaler.scale(loss).backward()
+            #     # **关键**：unscale 把缩放后的梯度写回 param.grad
+            #     scaler.unscale_(opt)
+            #     # —— 如需梯度裁剪，在 unscale 之后、step 之前做 ——
+            #     if config.get('grad_clip_val', 0) > 0:
+            #         torch.nn.utils.clip_grad_norm_(
+            #             model.parameters(),
+            #             max_norm=config['grad_clip_val']
+            #         )
+            # else:                                  # 纯 FP32 分支
+            #     loss.backward()
+            #     if config.get('grad_clip_val', 0) > 0:
+            #         torch.nn.utils.clip_grad_norm_(
+            #             model.parameters(),
+            #             max_norm=config['grad_clip_val']
+            #         )
+
+            # # ------------------- 4) 梯度探针 ----------------------
+            # keywords = [
+            #     # ".attn.qkv.weight", ".attn.proj.weight",
+            #     # ".mlp.fc1.weight", ".mlp.fc2.weight",
+            #     "adaLN_modulation", "gate", "blocks.0", "x_embedder.proj.weight"
+            # ]
+            # # 打印几层代表性的 grad_norm，看是否非零
+            # with torch.no_grad():
+            #     for n, p in model.module.named_parameters():   # DDP → .module
+            #         if p.grad is None:
+            #             continue
+            #         # 按需修改关键词；建议先看 gate / 第 0 个 block
+            #         if any(k in n for k in
+            #             keywords):
+            #             print(f"{n:<60} grad_norm={p.grad.norm():.3e}")
+            #     # 只跑前 N 步就停止调试
+            #     # if train_steps >= 20:  break
+
+            # ------------------- 5) 参数更新 ----------------------
+            # if bfloat_enable:
+            #     scaler.step(opt)
+            #     scaler.update()
+            # else:
+            #     opt.step()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug, camera_mats in loader:
+        x = x.to(device)            # [B, num_goals+cond_num, 3, 224, 224]
+        y = y.to(device)            # [B, num_goals+cond_num, 2]
+        rel_t = rel_t.to(device).flatten(0, 1)      # [B, num_goals]
+        aug = aug.to(device)        # [B, num_goals, 3, 224, 224]
+        camera_mats = camera_mats.to(device)
+        
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug, camera_mats=camera_mats)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v7_2.py b/train/train_v7_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0f12e7dacdea23672ba2dcb97cefb419ac1dd50
--- /dev/null
+++ b/train/train_v7_2.py
@@ -0,0 +1,465 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+# 这个版本为加入相机编码版本（评估稳定化：rank0-only + DreamSim 单例）
+
+import torch
+import torch.nn as nn
+
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+
+from diffusers.models import AutoencoderKL
+
+from isolated_nwm_infer_v7_2 import model_forward_wrapper
+
+from distributed import init_distributed
+from models_tpz_v7_2 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v3 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    # print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.amp.GradScaler('cuda')
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    
+                    print("loading dataset")
+
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug, camera_mats in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)                     # [B, num_goals, 4, 28, 28]
+            camera_mats = camera_mats.to(device, non_blocking=True)     # [B, num_goals+num_cond, 4, 4]
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))                          # [B, num_goals+num_conds, 4, 28, 28]
+                    # print("x shape, ", x.shape)
+                    
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)             # [B*num_goals, 4, 28, 28]
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)      # [B*num_goals, num_cond, 4, 28, 28]
+                y_cond = aug.unsqueeze(2).flatten(0, 1)             # [B*num_goals, 1, 4, 28, 28]
+
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                camera_mats_x_start = camera_mats[:, num_cond:].unsqueeze(2).flatten(0, 1)   # [B*num_goals, 1, 4, 4]
+                camera_mats_x_cond = camera_mats[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, 4, 4).flatten(0, 1)    # [B*num_goals, num_cond, 4, 4]
+                camera_mats_x_cond = torch.cat((camera_mats_x_cond, camera_mats_x_start), dim=1)          # [B*num_goals, num_conds+1, 4, 4]
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_sup=y_cond.squeeze(1), viewmats=camera_mats_x_cond)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            opt.zero_grad()
+            if not bfloat_enable:
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug, camera_mats in loader:
+        x = x.to(device)            # [B, num_goals+cond_num, 3, 224, 224]
+        y = y.to(device)            # [B, num_goals+cond_num, 2]
+        rel_t = rel_t.to(device).flatten(0, 1)      # [B, num_goals]
+        aug = aug.to(device)        # [B, num_goals, 3, 224, 224]
+        camera_mats = camera_mats.to(device)
+        
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug, camera_mats=camera_mats)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v7_3.py b/train/train_v7_3.py
new file mode 100644
index 0000000000000000000000000000000000000000..d47b76cdc10db53b85bccb9e804afa011b4e7432
--- /dev/null
+++ b/train/train_v7_3.py
@@ -0,0 +1,465 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+# 这个版本为加入相机编码版本
+
+import torch
+import torch.nn as nn
+
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+
+from diffusers.models import AutoencoderKL
+
+from isolated_nwm_infer_v7_3 import model_forward_wrapper
+
+from distributed import init_distributed
+from models_tpz_v7_3 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v3 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    # print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.amp.GradScaler('cuda')
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    
+                    print("loading dataset")
+
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug, camera_mats in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)                     # [B, num_goals, 4, 28, 28]
+            camera_mats = camera_mats.to(device, non_blocking=True)     # [B, num_goals+num_cond, 4, 4]
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))                          # [B, num_goals+num_conds, 4, 28, 28]
+                    # print("x shape, ", x.shape)
+                    
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)             # [B*num_goals, 4, 28, 28]
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)      # [B*num_goals, num_cond, 4, 28, 28]
+                y_cond = aug.unsqueeze(2).flatten(0, 1)             # [B*num_goals, 1, 4, 28, 28]
+
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                camera_mats_x_start = camera_mats[:, num_cond:].unsqueeze(2).flatten(0, 1)   # [B*num_goals, 1, 4, 4]
+                camera_mats_x_cond = camera_mats[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, 4, 4).flatten(0, 1)    # [B*num_goals, num_cond, 4, 4]
+                camera_mats_x_cond = torch.cat((camera_mats_x_cond, camera_mats_x_start), dim=1)          # [B*num_goals, num_conds+1, 4, 4]
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_sup=y_cond.squeeze(1), viewmats=camera_mats_x_cond)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            opt.zero_grad()
+            if not bfloat_enable:
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug, camera_mats in loader:
+        x = x.to(device)            # [B, num_goals+cond_num, 3, 224, 224]
+        y = y.to(device)            # [B, num_goals+cond_num, 2]
+        rel_t = rel_t.to(device).flatten(0, 1)      # [B, num_goals]
+        aug = aug.to(device)        # [B, num_goals, 3, 224, 224]
+        camera_mats = camera_mats.to(device)
+        
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug, camera_mats=camera_mats)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v8.py b/train/train_v8.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb98a144cd239a5846d50a83a1efaf63b9a3d724
--- /dev/null
+++ b/train/train_v8.py
@@ -0,0 +1,506 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+# 这个版本为加入相机编码版本
+
+import torch
+import torch.nn as nn
+
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+
+from diffusers.models import AutoencoderKL
+
+from isolated_nwm_infer_v8 import model_forward_wrapper
+
+from distributed import init_distributed
+from models_tpz_v8 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v3 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond+1, input_size=latent_size, in_channels=4).to(device)
+    # print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.amp.GradScaler('cuda')
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug, camera_mats in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)                     # [B, num_goals, 4, 28, 28]
+            camera_mats = camera_mats.to(device, non_blocking=True)     # [B, num_goals+num_cond, 4, 4]
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)             # [B*num_goals, 4, 28, 28]
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)      # [B*num_goals, num_cond, 4, 28, 28]
+                y_cond = aug.unsqueeze(2).flatten(0, 1)             # [B*num_goals, 1, 4, 28, 28]
+                x_cond = torch.cat((x_cond, y_cond), dim=1)                    # [B*num_goals, num_cond+1, 4, 28, 28]                      
+                #  print(f"x_cond shape: {x_cond.size()}")
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                camera_mats_x_start = camera_mats[:, num_cond:].unsqueeze(2).flatten(0, 1)   # [B*num_goals, 1, 4, 4]
+                camera_mats_x_cond = camera_mats[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, 4, 4).flatten(0, 1)    # [B*num_goals, num_cond, 4, 4]
+                camera_mats_x_cond = torch.cat((camera_mats_x_cond, camera_mats_x_start), dim=1)
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_sup=y_cond.squeeze(1), viewmats=camera_mats_x_cond)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            opt.zero_grad(set_to_none=True)
+
+            if bfloat_enable:                      # AMP / bfloat16 分支
+                scaler.scale(loss).backward()
+                # **关键**：unscale 把缩放后的梯度写回 param.grad
+                scaler.unscale_(opt)
+                # —— 如需梯度裁剪，在 unscale 之后、step 之前做 ——
+                if config.get('grad_clip_val', 0) > 0:
+                    torch.nn.utils.clip_grad_norm_(
+                        model.parameters(),
+                        max_norm=config['grad_clip_val']
+                    )
+            else:                                  # 纯 FP32 分支
+                loss.backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    torch.nn.utils.clip_grad_norm_(
+                        model.parameters(),
+                        max_norm=config['grad_clip_val']
+                    )
+
+            # ------------------- 4) 梯度探针 ----------------------
+            keywords = [
+                # ".attn.qkv.weight", ".attn.proj.weight",
+                # ".mlp.fc1.weight", ".mlp.fc2.weight",
+                "adaLN_modulation", "gate", "blocks.0", "x_embedder.proj.weight"
+            ]
+            # 打印几层代表性的 grad_norm，看是否非零
+            with torch.no_grad():
+                for n, p in model.module.named_parameters():   # DDP → .module
+                    if p.grad is None:
+                        continue
+                    # 按需修改关键词；建议先看 gate / 第 0 个 block
+                    if any(k in n for k in
+                        keywords):
+                        print(f"{n:<60} grad_norm={p.grad.norm():.3e}")
+                # 只跑前 N 步就停止调试
+                # if train_steps >= 20:  break
+
+            # ------------------- 5) 参数更新 ----------------------
+            if bfloat_enable:
+                scaler.step(opt)
+                scaler.update()
+            else:
+                opt.step()
+            # if not bfloat_enable:
+            #     opt.zero_grad()
+            #     loss.backward()
+            #     opt.step()
+            # else:
+            #     scaler.scale(loss).backward()
+            #     if config.get('grad_clip_val', 0) > 0:
+            #         scaler.unscale_(opt)
+            #         torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+            #     scaler.step(opt)
+            #     scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug, camera_mats in loader:
+        x = x.to(device)            # [B, num_goals+cond_num, 3, 224, 224]
+        y = y.to(device)            # [B, num_goals+cond_num, 2]
+        rel_t = rel_t.to(device).flatten(0, 1)      # [B, num_goals]
+        aug = aug.to(device)        # [B, num_goals, 3, 224, 224]
+        camera_mats = camera_mats.to(device)
+        
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug, camera_mats=camera_mats)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v8_2.py b/train/train_v8_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa330a8bf29a043f3e82031450e030dc821c38bb
--- /dev/null
+++ b/train/train_v8_2.py
@@ -0,0 +1,465 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# --------------------------------------------------------
+
+# 这个版本为加入相机编码版本
+
+import torch
+import torch.nn as nn
+
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+
+from diffusers.models import AutoencoderKL
+
+from isolated_nwm_infer_v8_2 import model_forward_wrapper
+
+from distributed import init_distributed
+from models_tpz_v8_2 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v3 import TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    # print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.amp.GradScaler('cuda')
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    
+                    print("loading dataset")
+
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug, camera_mats in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)                     # [B, num_goals, 4, 28, 28]
+            camera_mats = camera_mats.to(device, non_blocking=True)     # [B, num_goals+num_cond, 4, 4]
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))                          # [B, num_goals+num_conds, 4, 28, 28]
+                    # print("x shape, ", x.shape)
+                    
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)             # [B*num_goals, 4, 28, 28]
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)      # [B*num_goals, num_cond, 4, 28, 28]
+                y_cond = aug.unsqueeze(2).flatten(0, 1)             # [B*num_goals, 1, 4, 28, 28]
+
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                camera_mats_x_start = camera_mats[:, num_cond:].unsqueeze(2).flatten(0, 1)   # [B*num_goals, 1, 4, 4]
+                camera_mats_x_cond = camera_mats[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, 4, 4).flatten(0, 1)    # [B*num_goals, num_cond, 4, 4]
+                camera_mats_x_all = torch.cat((camera_mats_x_cond, camera_mats_x_start), dim=1)          # [B*num_goals, num_conds+1, 4, 4]
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_sup=y_cond.squeeze(1), viewmats=camera_mats_x_all)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            opt.zero_grad()
+            if not bfloat_enable:
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+            
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug, camera_mats in loader:
+        x = x.to(device)            # [B, num_goals+cond_num, 3, 224, 224]
+        y = y.to(device)            # [B, num_goals+cond_num, 2]
+        rel_t = rel_t.to(device).flatten(0, 1)      # [B, num_goals]
+        aug = aug.to(device)        # [B, num_goals, 3, 224, 224]
+        camera_mats = camera_mats.to(device)
+        
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug, camera_mats=camera_mats)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
diff --git a/train/train_v9.py b/train/train_v9.py
new file mode 100644
index 0000000000000000000000000000000000000000..adb8157c19aa6fc1c4549a326c0bf6784386b86a
--- /dev/null
+++ b/train/train_v9.py
@@ -0,0 +1,630 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# NoMaD, GNM, ViNT: https://github.com/robodhruv/visualnav-transformer
+# PE-Field: Positional Encoding Field for 3D-aware positional encoding
+# --------------------------------------------------------
+
+# Training script for CDiT v9 with PE-Field positional encoding
+
+import torch
+import torch.nn as nn
+from typing import List
+
+# the first flag below was False when we tested this script but True makes A100 training a lot faster:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import matplotlib
+matplotlib.use('Agg')
+from collections import OrderedDict
+from copy import deepcopy
+from time import time
+import argparse
+import logging
+import os
+import matplotlib.pyplot as plt 
+import yaml
+
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data import DataLoader, ConcatDataset
+from torch.utils.data.distributed import DistributedSampler
+
+from diffusers.models import AutoencoderKL
+
+from isolated_nwm_infer_v9 import model_forward_wrapper
+
+from distributed import init_distributed
+from models_tpz_v9 import CDiT_models
+from diffusion import create_diffusion
+from datasets_v8 import TrainingDatasetV8 as TrainingDataset
+from misc import transform
+
+#################################################################################
+#                             Training Helper Functions                         #
+#################################################################################
+
+def build_pefield_coords(depth: torch.Tensor, K: torch.Tensor, latent_h: int, latent_w: int, 
+                         image_h: int, image_w: int, device: torch.device) -> List[torch.Tensor]:
+    """
+    构建PE-Field的多尺度坐标 (z, u, v) for full-blooded PE-Field.
+    
+    Args:
+        depth: (B, H, W) - 原始图像空间的深度图
+        K: (B, 3, 3) - 相机内参矩阵
+        latent_h, latent_w: latent空间的高度和宽度（例如28, 28）
+        image_h, image_w: 原始图像空间的高度和宽度（例如224, 224）
+        device: torch device
+    
+    Returns:
+        List[torch.Tensor]: 多尺度坐标列表，每个元素形状为 [N_patches, 3]，其中3表示(z, u, v)
+    """
+    import torch.nn.functional as F
+    import numpy as np
+    
+    B = depth.shape[0]
+    
+    # 将depth和K缩放到latent空间
+    depth_latent = F.interpolate(depth.unsqueeze(1), size=(latent_h, latent_w), mode='bilinear', align_corners=False).squeeze(1)
+    
+    # 缩放K到latent空间：fx, fy, cx, cy都需要按比例缩放
+    scale_h = latent_h / image_h
+    scale_w = latent_w / image_w
+    K_latent = K.clone()
+    K_latent[:, 0, 0] *= scale_w  # fx
+    K_latent[:, 1, 1] *= scale_h  # fy
+    K_latent[:, 0, 2] *= scale_w  # cx
+    K_latent[:, 1, 2] *= scale_h  # cy
+    
+    # 构建像素网格
+    y_range = torch.arange(latent_h, device=device, dtype=torch.float32)
+    x_range = torch.arange(latent_w, device=device, dtype=torch.float32)
+    yy, xx = torch.meshgrid(y_range, x_range, indexing='ij')  # [H, W]
+    
+    # 扩展到batch维度
+    yy = yy.unsqueeze(0).expand(B, -1, -1)  # [B, H, W]
+    xx = xx.unsqueeze(0).expand(B, -1, -1)  # [B, H, W]
+    
+    # 从K中提取内参
+    fx = K_latent[:, 0, 0:1].unsqueeze(1)  # [B, 1, 1]
+    fy = K_latent[:, 1, 1:2].unsqueeze(1)  # [B, 1, 1]
+    cx = K_latent[:, 0, 2:3].unsqueeze(1)  # [B, 1, 1]
+    cy = K_latent[:, 1, 2:3].unsqueeze(1)  # [B, 1, 1]
+    
+    # 计算相机坐标系下的3D点
+    z_cam = depth_latent  # [B, H, W]
+    x_cam = (xx - cx) * z_cam / fx  # [B, H, W]
+    y_cam = (yy - cy) * z_cam / fy  # [B, H, W]
+    
+    # 对于训练，我们直接使用当前相机坐标系（简化版，不进行视角变换）
+    # 如果需要视角变换，可以在这里使用camera_mats (T_cw)
+    # 这里简化为：z = depth, u = x像素坐标归一化, v = y像素坐标归一化
+    z = z_cam  # [B, H, W]
+    
+    # 归一化z
+    z_min = z.view(B, -1).min(dim=1, keepdim=True)[0].unsqueeze(-1)  # [B, 1, 1]
+    z_max = z.view(B, -1).max(dim=1, keepdim=True)[0].unsqueeze(-1)  # [B, 1, 1]
+    z_range = z_max - z_min
+    z_range = torch.clamp(z_range, min=1e-6)
+    z_norm = (z - z_min) / z_range  # [B, H, W]
+    z_norm = z_norm + 1.0  # 加1.0，与PE-Field实现一致
+    
+    # u, v 使用归一化的像素坐标（相对于latent空间大小）
+    u = xx / latent_w  # [B, H, W] - 归一化到[0, 1]
+    v = yy / latent_h  # [B, H, W] - 归一化到[0, 1]
+    
+    # 拼接成 (z, u, v) 格式
+    pix_coords = torch.stack([z_norm, u, v], dim=-1)  # [B, H, W, 3]
+    
+    # 多尺度处理：参考PE-Field实现
+    # 定义多尺度分辨率（相对于latent空间）
+    H_news_W_news = [
+        (latent_h // 4, latent_w // 4),  # scale_1: 最小尺度
+        (latent_h // 2, latent_w // 2),  # scale_4: 中等尺度  
+        (latent_h, latent_w),              # scale_16: 最大尺度（原尺度）
+    ]
+    
+    pix_coords_downs_all = []
+    
+    for b in range(B):
+        pix_coords_b = pix_coords[b]  # [H, W, 3]
+        pix_coords_downs_b = []
+        
+        # 转换到 [1, H, W, 3] -> [1, 3, H, W] for interpolation
+        pix_coords_tensor = pix_coords_b.permute(2, 0, 1).unsqueeze(0)  # [1, 3, H, W]
+        
+        grid_level = 0
+        for H_new, W_new in H_news_W_news:
+            # 下采样到对应尺度
+            pix_coords_down = F.interpolate(pix_coords_tensor, size=(H_new, W_new), mode='bilinear', align_corners=False)
+            pix_coords_down = pix_coords_down.squeeze(0).permute(1, 2, 0)  # [H_new, W_new, 3]
+            
+            # 调整u, v坐标以适应新的尺度（保持相对位置）
+            # 这里简化处理，直接使用归一化坐标
+            # 按照PE-Field实现，需要重新归一化到基准尺度
+            pix_coords_down[..., 1] = pix_coords_down[..., 1] / latent_w * H_news_W_news[0][1]  # u
+            pix_coords_down[..., 2] = pix_coords_down[..., 2] / latent_h * H_news_W_news[0][0]  # v
+            
+            # 重排顺序为 (z, v, u) -> (z, u, v) (PE-Field格式)
+            pix_coords_down = pix_coords_down[..., [0, 2, 1]]
+            
+            # 对于grid_level > 0，进行2x2网格分组
+            if grid_level > 0:
+                for _ in range(grid_level):
+                    H_curr, W_curr, C = pix_coords_down.shape
+                    # split_into_2x2_local_grids
+                    if H_curr % 2 == 0 and W_curr % 2 == 0:
+                        pix_coords_down = pix_coords_down.view(H_curr // 2, 2, W_curr // 2, 2, C)
+                        pix_coords_down = pix_coords_down.permute(0, 2, 1, 3, 4)  # [H//2, W//2, 2, 2, 3]
+                        pix_coords_down = pix_coords_down.reshape(H_curr // 2, W_curr // 2, 4 * C)
+            
+            # 展平为 [N_patches, 3] 或 [N_patches, 12] (如果进行了分组)
+            pix_coords_down = pix_coords_down.reshape(-1, pix_coords_down.shape[-1])
+            pix_coords_downs_b.append(pix_coords_down)
+            grid_level += 1
+        
+        pix_coords_downs_all.append(pix_coords_downs_b)
+    
+    # 对于batch处理，我们需要对每个样本分别处理
+    # 但模型期望每个样本有独立的pix_coords_downs列表
+    # 这里我们返回第一个样本的格式作为示例，实际使用时需要按batch处理
+    # 为了简化，我们返回batch中第一个样本的多尺度坐标
+    # 在forward时，每个样本应该有独立的pix_coords_downs
+    
+    return pix_coords_downs_all[0] if B > 0 else []
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        name = name.replace('_orig_mod.', '')
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+    """
+    Trains a new CDiT model with PE-Field positional encoding.
+    """
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    _, rank, device, _ = init_distributed()
+    # rank = dist.get_rank()
+    seed = args.global_seed * dist.get_world_size() + rank
+    torch.manual_seed(seed)
+    print(f"Starting rank={rank}, seed={seed}, world_size={dist.get_world_size()}.")
+    with open("config/eval_config.yaml", "r") as f:
+        default_config = yaml.safe_load(f)
+    config = default_config
+    
+    with open(args.config, "r") as f:
+        user_config = yaml.safe_load(f)
+    config.update(user_config)
+    
+    # Setup an experiment folder:
+    os.makedirs(config['results_dir'], exist_ok=True)  # Make results folder (holds all experiment subfolders)
+    experiment_dir = f"{config['results_dir']}/{config['run_name']}"  # Create an experiment folder
+    checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+    if rank == 0:
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+
+    # Create model:
+    tokenizer = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    latent_size = config['image_size'] // 8
+
+    assert config['image_size'] % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    num_cond = config['context_size']
+    model = CDiT_models[config['model']](context_size=num_cond, input_size=latent_size, in_channels=4).to(device)
+    # print(model)
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    
+    # Setup optimizer (we used default Adam betas=(0.9, 0.999) and a constant learning rate of 1e-4 in our paper):
+    lr = float(config.get('lr', 1e-4))
+    opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0)
+
+    bfloat_enable = bool(hasattr(args, 'bfloat16') and args.bfloat16)
+    if bfloat_enable:
+        scaler = torch.amp.GradScaler('cuda')
+
+    # load existing checkpoint
+    latest_path = os.path.join(checkpoint_dir, "latest.pth.tar")
+    print('Searching for model from ', checkpoint_dir)
+    start_epoch = 0
+    train_steps = 0
+    if os.path.isfile(latest_path) or config.get('from_checkpoint', 0):
+        if os.path.isfile(latest_path) and config.get('from_checkpoint', 0):
+            raise ValueError("Resuming from checkpoint, this might override latest.pth.tar!!")
+        latest_path = latest_path if os.path.isfile(latest_path) else config.get('from_checkpoint', 0)
+        print("Loading model from ", latest_path)
+        latest_checkpoint = torch.load(latest_path, map_location=device, weights_only=False) 
+
+        if "model" in latest_checkpoint:
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['model'].items()}
+            res = model.load_state_dict(model_ckp, strict=True)
+            print("Loading model weights", res)
+
+            model_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['ema'].items()}
+            res = ema.load_state_dict(model_ckp, strict=True)
+            print("Loading EMA model weights", res)
+        else:
+            update_ema(ema, model, decay=0)  # Ensure EMA is initialized with synced weights
+
+        if "opt" in latest_checkpoint:
+            opt_ckp = {k.replace('_orig_mod.', ''):v for k,v in latest_checkpoint['opt'].items()}
+            opt.load_state_dict(opt_ckp)
+            print("Loading optimizer params")
+        
+        if "epoch" in latest_checkpoint:
+            start_epoch = latest_checkpoint['epoch'] + 1
+        
+        if "train_steps" in latest_checkpoint:
+            train_steps = latest_checkpoint["train_steps"]
+        
+        if "scaler" in latest_checkpoint:
+            scaler.load_state_dict(latest_checkpoint["scaler"])
+        
+    # ~40% speedup but might leads to worse performance depending on pytorch version
+    if args.torch_compile:
+        model = torch.compile(model)
+    model = DDP(model, device_ids=[device])
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    logger.info(f"CDiT Parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+    train_dataset = []
+    test_dataset = []
+
+    for dataset_name in config["datasets"]:
+        data_config = config["datasets"][dataset_name]
+        for data_split_type in ["train", "test"]:
+            if data_split_type in data_config:
+                    goals_per_obs = int(data_config["goals_per_obs"])
+                    if data_split_type == 'test':
+                        goals_per_obs = 4 # standardize testing
+                    
+                    if "distance" in data_config:
+                        min_dist_cat=data_config["distance"]["min_dist_cat"]
+                        max_dist_cat=data_config["distance"]["max_dist_cat"]
+                    else:
+                        min_dist_cat=config["distance"]["min_dist_cat"]
+                        max_dist_cat=config["distance"]["max_dist_cat"]
+
+                    if "len_traj_pred" in data_config:
+                        len_traj_pred=data_config["len_traj_pred"]
+                    else:
+                        len_traj_pred=config["len_traj_pred"]
+
+                    dataset = TrainingDataset(
+                        data_folder=data_config["data_folder"],
+                        data_split_folder=data_config[data_split_type],
+                        dataset_name=dataset_name,
+                        image_size=config["image_size"],
+                        min_dist_cat=min_dist_cat,
+                        max_dist_cat=max_dist_cat,
+                        len_traj_pred=len_traj_pred,
+                        context_size=config["context_size"],
+                        normalize=config["normalize"],
+                        goals_per_obs=goals_per_obs,
+                        transform=transform,
+                        predefined_index=None,
+                        traj_stride=1,
+                    )
+                    
+                    print("loading dataset")
+
+                    if data_split_type == "train":
+                        train_dataset.append(dataset)
+                    else:
+                        test_dataset.append(dataset)
+                    print(f"Dataset: {dataset_name} ({data_split_type}), size: {len(dataset)}")
+
+    # combine all the datasets from different robots
+    print(f"Combining {len(train_dataset)} datasets.")
+    train_dataset = ConcatDataset(train_dataset)
+    test_dataset = ConcatDataset(test_dataset)
+
+    sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        train_dataset,
+        batch_size=config['batch_size'],
+        shuffle=False,
+        sampler=sampler,
+        num_workers=config['num_workers'],
+        pin_memory=True,
+        drop_last=True,
+        persistent_workers=True
+    )
+    logger.info(f"Dataset contains {len(train_dataset):,} images")
+
+    # Prepare models for training:
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    log_steps = 0
+    running_loss = 0
+    start_time = time()
+
+    logger.info(f"Training for {args.epochs} epochs...")
+    logger.info("Using full-blooded PE-Field: computing pix_coords_downs from K and depth_curr.")
+    for epoch in range(start_epoch, args.epochs):
+        sampler.set_epoch(epoch)
+        logger.info(f"Beginning epoch {epoch}...")
+
+        for x, y, rel_t, aug, camera_mats, K, depth_curr in loader:
+            x = x.to(device, non_blocking=True)
+            y = y.to(device, non_blocking=True)
+            rel_t = rel_t.to(device, non_blocking=True)
+            aug = aug.to(device, non_blocking=True)                     # [B, num_goals, 4, 28, 28]
+            camera_mats = camera_mats.to(device, non_blocking=True)     # [B, num_goals+num_cond, 4, 4]
+            # PE-Field: Move K and depth_curr to device for full PE-Field implementation
+            K = K.to(device, non_blocking=True)                         # [B, num_goals+num_cond, 3, 3]
+            depth_curr = depth_curr.to(device, non_blocking=True)       # [B, num_goals+num_cond, H, W]
+            
+            with torch.amp.autocast('cuda', enabled=bfloat_enable, dtype=torch.bfloat16):
+                with torch.no_grad():
+                    # Map input images to latent space + normalize latents:
+                    B, T = x.shape[:2]
+                    x = x.flatten(0,1)
+                    x = tokenizer.encode(x).latent_dist.sample().mul_(0.18215)
+                    x = x.unflatten(0, (B, T))                          # [B, num_goals+num_conds, 4, 28, 28]
+                    # print("x shape, ", x.shape)
+                    
+                    # aug same as x
+                    B_aug, T_aug = aug.shape[:2]
+                    aug = aug.flatten(0,1)
+                    aug = tokenizer.encode(aug).latent_dist.sample().mul_(0.18215)
+                    aug = aug.unflatten(0, (B_aug, T_aug))              # [B, num_goals, 4, 28, 28]
+                    # print(f'aug latent shape: {aug.size()}')
+
+                num_goals = T - num_cond
+                x_start = x[:, num_cond:].flatten(0, 1)             # [B*num_goals, 4, 28, 28]
+                x_cond = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)      # [B*num_goals, num_cond, 4, 28, 28]
+                y_cond = aug.unsqueeze(2).flatten(0, 1)             # [B*num_goals, 1, 4, 28, 28]
+
+                y = y.flatten(0, 1)
+                rel_t = rel_t.flatten(0, 1)
+                
+                # PE-Field: Build multi-scale 3D coordinates (z, u, v) for full-blooded PE-Field
+                # K shape: [B, num_goals+num_cond, 3, 3]
+                # depth_curr shape: [B, num_goals+num_cond, H, W]
+                # We need to extract K and depth for current frame (after num_cond)
+                # Note: num_goals is already defined above
+                K_curr = K[:, num_cond:].flatten(0, 1)  # [B*num_goals, 3, 3]
+                depth_curr_frame = depth_curr[:, num_cond:].flatten(0, 1)  # [B*num_goals, H, W]
+                
+                # Get image size from config
+                image_size = config['image_size']  # e.g., 224
+                
+                # Build PE-Field coordinates for full-blooded PE-Field
+                # Since x_start is [B*num_goals, ...], each sample needs its own pix_coords_downs
+                # For now, we generate coords for the first sample and reuse for the batch
+                # (In practice, each sample could have different depth/K, but model expects shared coords per batch)
+                pix_coords_downs = None
+                if K_curr.shape[0] > 0 and depth_curr_frame.shape[0] > 0:
+                    # Build coords for the first sample (representative for the batch)
+                    # The model will apply these coords to all patches in the batch
+                    pix_coords_downs_list = build_pefield_coords(
+                        depth_curr_frame[:1], K_curr[:1], 
+                        latent_size, latent_size, 
+                        image_size, image_size, 
+                        device
+                    )
+                    # Use the multi-scale coordinates for PE-Field (expecting at least 3 scales)
+                    if len(pix_coords_downs_list) >= 3:
+                        pix_coords_downs = pix_coords_downs_list
+                    else:
+                        pix_coords_downs = None
+                        logger.warning("PE-Field: Failed to generate multi-scale coordinates, using None.")
+                
+                t = torch.randint(0, diffusion.num_timesteps, (x_start.shape[0],), device=device)
+                # Note: viewmats and Ks are kept for compatibility but not used in PE-Field version
+                model_kwargs = dict(y=y, x_cond=x_cond, rel_t=rel_t, x_sup=y_cond.squeeze(1), pix_coords_downs=pix_coords_downs)
+                loss_dict = diffusion.training_losses(model, x_start, t, model_kwargs)
+                loss = loss_dict["loss"].mean()
+
+            opt.zero_grad()
+            if not bfloat_enable:
+                loss.backward()
+                opt.step()
+            else:
+                scaler.scale(loss).backward()
+                if config.get('grad_clip_val', 0) > 0:
+                    scaler.unscale_(opt)
+                    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=config['grad_clip_val'])
+                scaler.step(opt)
+                scaler.update()
+
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.detach().item()
+            log_steps += 1
+            train_steps += 1
+
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                samples_per_sec = dist.get_world_size()*x_cond.shape[0]*steps_per_sec
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}, Samples/Sec: {samples_per_sec:.2f}")
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save DiT checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        "opt": opt.state_dict(),
+                        "args": args,
+                        "epoch": epoch,
+                        "train_steps": train_steps
+                    }
+                    if bfloat_enable:
+                        checkpoint.update({"scaler": scaler.state_dict()})
+                    checkpoint_path = f"{checkpoint_dir}/latest.pth.tar"
+                    torch.save(checkpoint, checkpoint_path)
+                    if train_steps % (10*args.ckpt_every) == 0 and train_steps > 0:
+                        checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pth.tar"
+                        torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+            
+            if train_steps % args.eval_every == 0 and train_steps > 0:
+                eval_start_time = time()
+                save_dir = os.path.join(experiment_dir, str(train_steps))
+                sim_score = evaluate(ema, tokenizer, diffusion, test_dataset, rank, config["batch_size"], config["num_workers"], latent_size, device, save_dir, args.global_seed, bfloat_enable, num_cond)
+                dist.barrier()
+                eval_end_time = time()
+                eval_time = eval_end_time - eval_start_time
+                logger.info(f"(step={train_steps:07d}) Perceptual Loss: {sim_score:.4f}, Eval Time: {eval_time:.2f}")
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+@torch.no_grad
+def evaluate(model, vae, diffusion, test_dataloaders, rank, batch_size, num_workers, latent_size, device, save_dir, seed, bfloat_enable, num_cond):
+    sampler = DistributedSampler(
+        test_dataloaders,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=seed
+    )
+    loader = DataLoader(
+        test_dataloaders,
+        batch_size=batch_size,
+        shuffle=False,
+        sampler=sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    from dreamsim import dreamsim
+    eval_model, _ = dreamsim(pretrained=True)
+    score = torch.tensor(0.).to(device)
+    n_samples = torch.tensor(0).to(device)
+
+    # Run for 1 step
+    for x, y, rel_t, aug, camera_mats in loader:
+        x = x.to(device)            # [B, num_goals+cond_num, 3, 224, 224]
+        y = y.to(device)            # [B, num_goals+cond_num, 2]
+        rel_t = rel_t.to(device).flatten(0, 1)      # [B, num_goals]
+        aug = aug.to(device)        # [B, num_goals, 3, 224, 224]
+        camera_mats = camera_mats.to(device)
+        
+        with torch.amp.autocast('cuda', enabled=True, dtype=torch.bfloat16):
+            B, T = x.shape[:2]
+            num_goals = T - num_cond
+            samples = model_forward_wrapper((model, diffusion, vae), x, y, num_timesteps=None, latent_size=latent_size, device=device, num_cond=num_cond, num_goals=num_goals, rel_t=rel_t, x_supervised=aug, camera_mats=camera_mats)
+            x_start_pixels = x[:, num_cond:].flatten(0, 1)
+            x_cond_pixels = x[:, :num_cond].unsqueeze(1).expand(B, num_goals, num_cond, x.shape[2], x.shape[3], x.shape[4]).flatten(0, 1)
+            samples = samples * 0.5 + 0.5
+            x_start_pixels = x_start_pixels * 0.5 + 0.5
+            x_cond_pixels = x_cond_pixels * 0.5 + 0.5
+            
+            res = eval_model(x_start_pixels, samples)
+            score += res.sum()
+            n_samples += len(res)
+        break
+    
+    if rank == 0:
+        os.makedirs(save_dir, exist_ok=True)
+        for i in range(min(samples.shape[0], 10)):
+            _, ax = plt.subplots(1,3,dpi=256)
+            ax[0].imshow((x_cond_pixels[i, -1].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[1].imshow((x_start_pixels[i].permute(1,2,0).cpu().numpy()*255).astype('uint8'))
+            ax[2].imshow((samples[i].permute(1,2,0).cpu().float().numpy()*255).astype('uint8'))
+            plt.savefig(f'{save_dir}/{i}.png')
+            plt.close()
+
+    dist.all_reduce(score)
+    dist.all_reduce(n_samples)
+    sim_score = score/n_samples
+    return sim_score
+
+def get_args_parser():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--epochs", type=int, default=300)
+    # parser.add_argument("--global-batch-size", type=int, default=256)
+    parser.add_argument("--global-seed", type=int, default=0)
+    parser.add_argument("--log-every", type=int, default=100)
+    parser.add_argument("--ckpt-every", type=int, default=2000)
+    parser.add_argument("--eval-every", type=int, default=5000)
+    parser.add_argument("--bfloat16", type=int, default=1)
+    parser.add_argument("--torch-compile", type=int, default=1)
+    return parser
+
+if __name__ == "__main__":
+    args = get_args_parser().parse_args()
+    main(args)
+
diff --git a/trajectory_demo.ipynb b/trajectory_demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d14e303c351b13f87d41e8b255c29f35dd177af6
--- /dev/null
+++ b/trajectory_demo.ipynb
@@ -0,0 +1,542 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ea804a26",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# 三栏轨迹演示（Python Notebook 版）\n",
+    "\n",
+    "此 Notebook 复刻了你上传的 HTML Demo 的核心交互：\n",
+    "- 左：目标帧（`target.png`）\n",
+    "- 中：3D 轨迹按帧播放（顺序播放多条轨迹）\n",
+    "- 右：导航画面（我们的方法 vs. Baseline），并在每条轨迹播放完毕后展示误差数字 3 秒\n",
+    "\n",
+    "### 目录结构（与原 HTML 一致）\n",
+    "```\n",
+    ".\n",
+    "├─ target.png\n",
+    "├─ run_000/\n",
+    "│  ├─ metadata.json   # 包含 frames 列表（每帧 pose 与 png 相对路径）\n",
+    "│  └─ frames/\n",
+    "│     ├─ frame_000.png\n",
+    "│     ├─ frame_001.png\n",
+    "│     └─ ...\n",
+    "├─ run_001/\n",
+    "│  └─ ...\n",
+    "├─ run_002/\n",
+    "│  └─ ...\n",
+    "└─ editor-2/\n",
+    "   ├─ run_006/frames/...\n",
+    "   ├─ run_001/frames/...\n",
+    "   └─ run_003/frames/...\n",
+    "```\n",
+    "> **如果上述真实数据不存在**，本 Notebook 会自动**生成一个可运行的仿真数据集**（三条轨迹与对应的帧图像与 `metadata.json`），以便你直接运行和查看效果。\n",
+    "\n",
+    "### 使用方法\n",
+    "1. 运行 **所有单元格**；若没有你自己的数据，脚本会在当前目录下生成演示数据。\n",
+    "2. 点击“**重新播放**”按钮即可顺序播放 `run_000 → run_001 → run_002`。  \n",
+    "3. 右侧会同步显示 **Method** 与 **Baseline** 的当前帧画面，并在每条轨迹播放完后显示误差数值 3 秒。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "aac7bcfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ======== Editor 结构可视化：工具函数 ========\n",
+    "# 目录结构：\n",
+    "# <BASE_DIR>/editor/run_012/\n",
+    "#   ├─ frames/{init.png, step_000.png, ..., goal.png}\n",
+    "#   ├─ metadata.json\n",
+    "#   └─ candidates/\n",
+    "#       ├─ cand_000/{frames/... , metadata.json, deltas.npy}\n",
+    "#       ├─ cand_001/...\n",
+    "#       └─ summary.json  (可选)\n",
+    "\n",
+    "import os, json, io, time, glob, re\n",
+    "from pathlib import Path\n",
+    "from typing import List, Dict, Tuple, Optional\n",
+    "\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "import numpy as np\n",
+    "import ipywidgets as W\n",
+    "from IPython.display import display, clear_output\n",
+    "\n",
+    "def _read_json(p: Path) -> Optional[dict]:\n",
+    "    try:\n",
+    "        return json.loads(p.read_text('utf-8'))\n",
+    "    except Exception:\n",
+    "        return None\n",
+    "\n",
+    "def _png_bytes(p: Path) -> bytes:\n",
+    "    if not p.exists():\n",
+    "        return b''\n",
+    "    with open(p, 'rb') as f:\n",
+    "        return f.read()\n",
+    "\n",
+    "def _text_width(draw: ImageDraw.ImageDraw, text: str, font=None) -> int:\n",
+    "    # 兼容旧版 Pillow：优先 textlength，退化到 textbbox\n",
+    "    try:\n",
+    "        return int(draw.textlength(text, font=font))\n",
+    "    except Exception:\n",
+    "        box = draw.textbbox((0,0), text, font=font)\n",
+    "        return int(box[2] - box[0])\n",
+    "\n",
+    "def _overlay_box_text(im: Image.Image, text: str):\n",
+    "    im = im.copy().convert(\"RGB\")\n",
+    "    draw = ImageDraw.Draw(im)\n",
+    "    try:\n",
+    "        font = ImageFont.load_default()\n",
+    "    except Exception:\n",
+    "        font = None\n",
+    "    pad = 8\n",
+    "    w = _text_width(draw, text, font)\n",
+    "    h = 14\n",
+    "    x = (im.width - (w + 2*pad)) // 2\n",
+    "    y = 6\n",
+    "    # 半透明黑底（简单起见用不透明，Jupyter 显示效果更稳定）\n",
+    "    draw.rectangle([x, y, x+w+2*pad, y+h+2*pad], fill=(0,0,0))\n",
+    "    draw.text((x+pad, y+pad), text, fill=(255,255,255), font=font)\n",
+    "    return im\n",
+    "\n",
+    "def _compose_side_by_side(a: Image.Image, b: Image.Image) -> Image.Image:\n",
+    "    w = a.width + b.width\n",
+    "    h = max(a.height, b.height)\n",
+    "    canvas = Image.new('RGB', (w, h), (246,247,249))\n",
+    "    canvas.paste(a, (0,0))\n",
+    "    canvas.paste(b, (a.width, 0))\n",
+    "    return canvas\n",
+    "\n",
+    "def _scan_runs(base_dir: Path) -> List[str]:\n",
+    "    editor = base_dir / \"editor\"\n",
+    "    if not editor.exists():\n",
+    "        return []\n",
+    "    runs = sorted([p.name for p in editor.iterdir() if p.is_dir() and re.match(r\"run_\\d{3}$\", p.name)])\n",
+    "    return runs\n",
+    "\n",
+    "def _list_candidates(run_dir: Path) -> List[str]:\n",
+    "    cdir = run_dir / \"candidates\"\n",
+    "    if not cdir.exists():\n",
+    "        return []\n",
+    "    # 优先按 summary.json rank\n",
+    "    summary = _read_json(cdir / \"summary.json\")\n",
+    "    if summary and \"items\" in summary:\n",
+    "        ids = [f\"cand_{it.get('rank', 10**9):03d}\" for it in summary[\"items\"]]\n",
+    "        ids = [cid for cid in ids if (cdir / cid).exists()]\n",
+    "        if ids:\n",
+    "            return sorted(set(ids))\n",
+    "    # 回退：cand_XXX 目录名排序\n",
+    "    cands = sorted([p.name for p in cdir.iterdir() if p.is_dir() and re.match(r\"cand_\\d{3}$\", p.name)])\n",
+    "    return cands\n",
+    "\n",
+    "def _load_meta_and_frames(run_dir: Path) -> Tuple[dict, List[dict]]:\n",
+    "    meta = _read_json(run_dir / \"metadata.json\") or {}\n",
+    "    frames = meta.get(\"frames\", [])\n",
+    "    frames = sorted(frames, key=lambda x: x.get(\"frame\", 0))\n",
+    "    return meta, frames\n",
+    "\n",
+    "def _goal_path(run_dir: Path) -> Path:\n",
+    "    return run_dir / \"frames\" / \"goal.png\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "20569990",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b9d48195ae341e3bacc414ce301c051",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(Text(value='/data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ======== Editor 结构可视化：交互式 UI ========\n",
+    "\n",
+    "BASE_DIR = W.Text(\n",
+    "    value='/data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/',  # 在这里填你的输出根目录（包含 editor/ 的那一层）\n",
+    "    description='BASE_DIR:',\n",
+    "    layout=W.Layout(width='60%')\n",
+    ")\n",
+    "BTN_SCAN = W.Button(description='扫描 runs', button_style='info')\n",
+    "RUN = W.Dropdown(options=[], description='run_id:')\n",
+    "VIEW = W.ToggleButtons(options=[('final','final'), ('candidate','candidate')],\n",
+    "                       value='final', description='视图:')\n",
+    "CAND = W.Dropdown(options=[], description='candidate:')\n",
+    "FPS = W.IntSlider(value=2, min=1, max=30, step=1, description='FPS:')\n",
+    "BTN_PLAY = W.Button(description='播放', button_style='success')\n",
+    "BTN_STOP = W.Button(description='停止', button_style='warning')\n",
+    "LBL = W.Label('就绪')\n",
+    "IMG = W.Image(format='png')\n",
+    "OUT = W.Output()\n",
+    "\n",
+    "ui_top = W.HBox([BASE_DIR, BTN_SCAN, LBL])\n",
+    "ui_mid = W.HBox([RUN, VIEW, CAND, FPS, BTN_PLAY, BTN_STOP])\n",
+    "ui = W.VBox([ui_top, ui_mid, IMG, OUT])\n",
+    "display(ui)\n",
+    "\n",
+    "state = {\"playing\": False}\n",
+    "\n",
+    "def _refresh_candidates(*args):\n",
+    "    base = Path(BASE_DIR.value).expanduser().resolve()\n",
+    "    run_id = RUN.value\n",
+    "    if not run_id:\n",
+    "        CAND.options = []\n",
+    "        return\n",
+    "    run_dir = base / \"editor\" / run_id\n",
+    "    cands = _list_candidates(run_dir)\n",
+    "    CAND.options = cands\n",
+    "    if cands:\n",
+    "        CAND.value = cands[0]\n",
+    "    # 只有 candidate 视图时显示候选下拉\n",
+    "    CAND.layout.display = 'none' if VIEW.value == 'final' else 'flex'\n",
+    "\n",
+    "def _refresh_runs(_=None):\n",
+    "    base = Path(BASE_DIR.value).expanduser().resolve()\n",
+    "    runs = _scan_runs(base)\n",
+    "    if not runs:\n",
+    "        RUN.options = []\n",
+    "        LBL.value = f\"未找到 {base}/editor/run_XXX/\"\n",
+    "        IMG.value = b''\n",
+    "        return\n",
+    "    RUN.options = runs\n",
+    "    RUN.value = runs[0]\n",
+    "    LBL.value = f\"发现 {len(runs)} 个 run\"\n",
+    "    _refresh_candidates()\n",
+    "    _render_once()\n",
+    "\n",
+    "def _render_once():\n",
+    "    base = Path(BASE_DIR.value).expanduser().resolve()\n",
+    "    if not RUN.value:\n",
+    "        return\n",
+    "    run_dir = base / \"editor\" / RUN.value\n",
+    "    if VIEW.value == 'candidate':\n",
+    "        run_dir = run_dir / \"candidates\" / CAND.value\n",
+    "\n",
+    "    meta, frames = _load_meta_and_frames(run_dir)\n",
+    "    fps = int(meta.get(\"fps\", FPS.value) or FPS.value)\n",
+    "\n",
+    "    if not frames:\n",
+    "        LBL.value = \"metadata.json 中无 frames\"\n",
+    "        IMG.value = b''\n",
+    "        return\n",
+    "\n",
+    "    # 预览第一帧（左：当前帧，右：goal.png）\n",
+    "    f = frames[0]\n",
+    "    png = run_dir / f.get(\"png\", \"\")\n",
+    "    cur = Image.open(png).convert('RGB') if png.exists() else Image.new('RGB', (224,224), (220,220,220))\n",
+    "    if \"loss\" in f:\n",
+    "        cur = _overlay_box_text(cur, f\"loss={f['loss']:.4f}\")\n",
+    "    goal = _goal_path(run_dir)\n",
+    "    if goal.exists():\n",
+    "        goal_im = Image.open(goal).convert('RGB')\n",
+    "        cur = _compose_side_by_side(cur, goal_im)\n",
+    "    bio = io.BytesIO(); cur.save(bio, format='PNG'); IMG.value = bio.getvalue()\n",
+    "    LBL.value = f\"预览：{RUN.value} / {VIEW.value}{' / ' + CAND.value if VIEW.value=='candidate' else ''} | FPS={fps}\"\n",
+    "\n",
+    "def _play(_=None):\n",
+    "    state[\"playing\"] = True\n",
+    "    base = Path(BASE_DIR.value).expanduser().resolve()\n",
+    "    if not RUN.value:\n",
+    "        return\n",
+    "    run_dir = base / \"editor\" / RUN.value\n",
+    "    if VIEW.value == 'candidate':\n",
+    "        run_dir = run_dir / \"candidates\" / CAND.value\n",
+    "\n",
+    "    meta, frames = _load_meta_and_frames(run_dir)\n",
+    "    fps = int(meta.get(\"fps\", FPS.value) or FPS.value)\n",
+    "\n",
+    "    for i, f in enumerate(frames):\n",
+    "        if not state[\"playing\"]:\n",
+    "            break\n",
+    "        png = run_dir / f.get(\"png\",\"\")\n",
+    "        cur = Image.open(png).convert('RGB') if png.exists() else Image.new('RGB',(224,224),(220,220,220))\n",
+    "        if \"loss\" in f:\n",
+    "            cur = _overlay_box_text(cur, f\"loss={f['loss']:.4f}\")\n",
+    "        goal = _goal_path(run_dir)\n",
+    "        if goal.exists():\n",
+    "            goal_im = Image.open(goal).convert('RGB')\n",
+    "            cur = _compose_side_by_side(cur, goal_im)\n",
+    "        bio = io.BytesIO(); cur.save(bio, format='PNG'); IMG.value = bio.getvalue()\n",
+    "        LBL.value = f\"播放：{RUN.value} / {VIEW.value}{' / ' + CAND.value if VIEW.value=='candidate' else ''} | frame {i+1}/{len(frames)} | FPS={fps}\"\n",
+    "        time.sleep(1.0/max(1,fps))\n",
+    "    state[\"playing\"] = False\n",
+    "    LBL.value = \"播放结束\"\n",
+    "\n",
+    "def _stop(_=None):\n",
+    "    state[\"playing\"] = False\n",
+    "    LBL.value = \"已停止\"\n",
+    "\n",
+    "# 事件绑定\n",
+    "BTN_SCAN.on_click(_refresh_runs)\n",
+    "RUN.observe(lambda _: _render_once(), names='value')\n",
+    "VIEW.observe(_refresh_candidates, names='value')\n",
+    "CAND.observe(lambda _: _render_once(), names='value')\n",
+    "FPS.observe(lambda _: _render_once(), names='value')\n",
+    "BTN_PLAY.on_click(_play)\n",
+    "BTN_STOP.on_click(_stop)\n",
+    "\n",
+    "# 初次尝试扫描并预览\n",
+    "_refresh_runs()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "198886f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e47a3f2a954e4914bd5ab297288d90bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(Text(value='', description='GT .npy(可选):', layout=Layout(width='60%')), IntText(…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ======== 3D 轨迹可视化（GT / Final / Candidate） ========\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D  # noqa: F401\n",
+    "import ipywidgets as W\n",
+    "from IPython.display import display, clear_output\n",
+    "\n",
+    "# 控件：可选 GT 源、是否画所有候选、是否画 final/candidate/gt\n",
+    "GT_FILE = W.Text(value='', description='GT .npy(可选):', layout=W.Layout(width='60%'))\n",
+    "IDX_IN_GT = W.IntText(value=-1, description='GT行索引:', tooltip='当GT文件是[N,T,4]时, 默认用当前run_index; 设为>=0可手动指定行')\n",
+    "SHOW_GT = W.Checkbox(value=True, description='显示 GT')\n",
+    "SHOW_FINAL = W.Checkbox(value=True, description='显示 Final')\n",
+    "SHOW_CAND = W.Checkbox(value=True, description='显示 Candidate')\n",
+    "ALL_CANDS = W.Checkbox(value=False, description='所有 Candidate')\n",
+    "\n",
+    "BTN_PLOT3D = W.Button(description='绘制3D轨迹', button_style='success')\n",
+    "LBL3D = W.Label('就绪')\n",
+    "OUT3D = W.Output()\n",
+    "\n",
+    "ui3_top = W.HBox([GT_FILE, IDX_IN_GT])\n",
+    "ui3_mid = W.HBox([SHOW_GT, SHOW_FINAL, SHOW_CAND, ALL_CANDS, BTN_PLOT3D, LBL3D])\n",
+    "ui3 = W.VBox([ui3_top, ui3_mid, OUT3D])\n",
+    "display(ui3)\n",
+    "\n",
+    "def _deltas_from_metadata(run_dir: Path):\n",
+    "    \"\"\"从 metadata.json 的 frames[].delta 读取并拼出 [T,4]；若缺失返回 None\"\"\"\n",
+    "    meta, frames = _load_meta_and_frames(run_dir)\n",
+    "    deltas = []\n",
+    "    for f in frames:\n",
+    "        d = f.get('delta')\n",
+    "        if not d:\n",
+    "            continue\n",
+    "        dx = d.get('dx', 0.0); dy = d.get('dy', 0.0); dz = d.get('dz', 0.0); dth = d.get('dtheta', 0.0)\n",
+    "        # INIT 通常是全0，可忽略；但即便包含也问题不大\n",
+    "        if dx==dy==dz==dth==0.0 and f.get('action','INIT')=='INIT':\n",
+    "            continue\n",
+    "        deltas.append([dx, dy, dz, dth])\n",
+    "    if len(deltas)==0:\n",
+    "        return None\n",
+    "    return np.array(deltas, dtype=np.float32)\n",
+    "\n",
+    "def _deltas_from_candidate(run_dir: Path):\n",
+    "    \"\"\"优先 deltas.npy；没有则从 metadata.json 读取\"\"\"\n",
+    "    npy = run_dir / 'deltas.npy'\n",
+    "    if npy.exists():\n",
+    "        arr = np.load(npy)\n",
+    "        # 兼容 (1,T,4)\n",
+    "        if arr.ndim == 3 and arr.shape[0] == 1:\n",
+    "            arr = arr[0]\n",
+    "        return arr.astype(np.float32)\n",
+    "    return _deltas_from_metadata(run_dir)\n",
+    "\n",
+    "def _deltas_to_xyz(deltas: np.ndarray) -> np.ndarray:\n",
+    "    \"\"\"[T,4] -> [T+1,3]，从(0,0,0)起累计 xyz\"\"\"\n",
+    "    xyz = np.zeros((1,3), dtype=np.float32)\n",
+    "    if deltas is not None and deltas.size > 0:\n",
+    "        steps = np.cumsum(deltas[:, :3], axis=0)  # 只累计 xyz\n",
+    "        xyz = np.vstack([xyz, steps])\n",
+    "    return xyz\n",
+    "\n",
+    "def _load_gt_xyz(base: Path, run_id: str, gt_file: str, idx_in_gt: int) -> np.ndarray:\n",
+    "    \"\"\"从可选的 GT .npy 读取 [T,4] 或 [N,T,4]，转为 xyz；找不到则返回 None\"\"\"\n",
+    "    if not gt_file:\n",
+    "        return None\n",
+    "    p = Path(gt_file).expanduser().resolve()\n",
+    "    if not p.exists():\n",
+    "        LBL3D.value = f\"GT文件不存在: {p}\"\n",
+    "        return None\n",
+    "    arr = np.load(p, allow_pickle=True)\n",
+    "    # 推断当前 run_index\n",
+    "    # run_id 形如 run_012 → 12\n",
+    "    try:\n",
+    "        sid = int(run_id.split('_')[-1])\n",
+    "    except Exception:\n",
+    "        sid = 0\n",
+    "    if idx_in_gt >= 0:\n",
+    "        sel = idx_in_gt\n",
+    "    else:\n",
+    "        sel = sid\n",
+    "\n",
+    "    if arr.ndim == 2:           # [T,4] or [T,3]\n",
+    "        deltas = arr\n",
+    "    elif arr.ndim == 3:         # [N,T,4]\n",
+    "        if sel < arr.shape[0]:\n",
+    "            deltas = arr[sel]\n",
+    "        else:\n",
+    "            LBL3D.value = f\"GT行索引超界(想要{sel}, 但N={arr.shape[0]}), 用第0行\"\n",
+    "            deltas = arr[0]\n",
+    "    else:\n",
+    "        LBL3D.value = f\"无法识别GT数组维度: {arr.shape}\"\n",
+    "        return None\n",
+    "\n",
+    "    # 若只有3列，也按 xyz 解释\n",
+    "    if deltas.shape[1] >= 3:\n",
+    "        deltas = deltas[:, :4] if deltas.shape[1] >= 4 else np.hstack([deltas[:, :3], np.zeros((deltas.shape[0],1), dtype=deltas.dtype)])\n",
+    "        return _deltas_to_xyz(deltas.astype(np.float32))\n",
+    "    return None\n",
+    "\n",
+    "def _gather_all_candidates_xyz(run_dir: Path) -> list:\n",
+    "    \"\"\"返回 [(cand_name, xyz), ...]\"\"\"\n",
+    "    ret = []\n",
+    "    cdir = run_dir / 'candidates'\n",
+    "    if not cdir.exists():\n",
+    "        return ret\n",
+    "    # 优先按 summary.json 的 rank 排序\n",
+    "    cands = _list_candidates(run_dir)\n",
+    "    for cid in cands:\n",
+    "        cpath = cdir / cid\n",
+    "        deltas = _deltas_from_candidate(cpath)\n",
+    "        if deltas is None:\n",
+    "            continue\n",
+    "        xyz = _deltas_to_xyz(deltas)\n",
+    "        ret.append((cid, xyz))\n",
+    "    return ret\n",
+    "\n",
+    "def _plot_equal_3d(ax, curves, labels, styles=None):\n",
+    "    \"\"\"curves: list of (N,3); labels对齐。自动设同尺度坐标轴\"\"\"\n",
+    "    mins = np.array([np.inf, np.inf, np.inf], dtype=np.float64)\n",
+    "    maxs = -mins\n",
+    "    for xyz in curves:\n",
+    "        if xyz is None or len(xyz)==0: \n",
+    "            continue\n",
+    "        mins = np.minimum(mins, xyz.min(axis=0))\n",
+    "        maxs = np.maximum(maxs, xyz.max(axis=0))\n",
+    "    # 容错：若全 0\n",
+    "    if not np.all(np.isfinite(mins)) or not np.all(np.isfinite(maxs)):\n",
+    "        mins = np.array([-1,-1,-1], dtype=np.float64)\n",
+    "        maxs = np.array([ 1, 1, 1], dtype=np.float64)\n",
+    "    span = max(1e-6, float(np.max(maxs - mins)))\n",
+    "    center = (mins + maxs) / 2.0\n",
+    "    lo = center - span/2\n",
+    "    hi = center + span/2\n",
+    "\n",
+    "    for i, xyz in enumerate(curves):\n",
+    "        if xyz is None or len(xyz)==0: \n",
+    "            continue\n",
+    "        if styles and i < len(styles):\n",
+    "            ax.plot(xyz[:,0], xyz[:,1], xyz[:,2], styles[i], label=labels[i])\n",
+    "        else:\n",
+    "            ax.plot(xyz[:,0], xyz[:,1], xyz[:,2], label=labels[i])\n",
+    "\n",
+    "    ax.set_xlabel('X'); ax.set_ylabel('Y'); ax.set_zlabel('Z')\n",
+    "    ax.set_xlim(lo[0], hi[0]); ax.set_ylim(lo[1], hi[1]); ax.set_zlim(lo[2], hi[2])\n",
+    "    ax.legend(loc='best'); ax.grid(True)\n",
+    "\n",
+    "def _do_plot3d(_=None):\n",
+    "    base = Path(BASE_DIR.value).expanduser().resolve()\n",
+    "    run_id = RUN.value\n",
+    "    if not run_id:\n",
+    "        LBL3D.value = \"请选择 run_id\"\n",
+    "        return\n",
+    "\n",
+    "    # Final 轨迹（来自 editor/run_xxx/metadata.json 的 delta）\n",
+    "    final_dir = base / 'editor' / run_id\n",
+    "    final_deltas = _deltas_from_metadata(final_dir)\n",
+    "    final_xyz = _deltas_to_xyz(final_deltas) if final_deltas is not None else None\n",
+    "\n",
+    "    # Candidate 轨迹\n",
+    "    cand_xyz_list = []\n",
+    "    if SHOW_CAND.value:\n",
+    "        if ALL_CANDS.value:\n",
+    "            cand_xyz_list = _gather_all_candidates_xyz(final_dir)\n",
+    "        else:\n",
+    "            if CAND.options and CAND.value:\n",
+    "                cdir = final_dir / 'candidates' / CAND.value\n",
+    "                cd = _deltas_from_candidate(cdir)\n",
+    "                if cd is not None:\n",
+    "                    cand_xyz_list = [(CAND.value, _deltas_to_xyz(cd))]\n",
+    "\n",
+    "    # GT 轨迹（可选）\n",
+    "    gt_xyz = _load_gt_xyz(base, run_id, GT_FILE.value, IDX_IN_GT.value) if SHOW_GT.value else None\n",
+    "\n",
+    "    with OUT3D:\n",
+    "        clear_output(wait=True)\n",
+    "        fig = plt.figure(figsize=(7.5, 6))\n",
+    "        ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "        curves, labels, styles = [], [], []\n",
+    "\n",
+    "        if SHOW_GT.value and gt_xyz is not None:\n",
+    "            curves.append(gt_xyz); labels.append('GT'); styles.append('-')     # 线型可按需调整\n",
+    "\n",
+    "        if SHOW_FINAL.value and final_xyz is not None:\n",
+    "            curves.append(final_xyz); labels.append('Final'); styles.append('-')\n",
+    "\n",
+    "        if SHOW_CAND.value and cand_xyz_list:\n",
+    "            for name, xyz in cand_xyz_list:\n",
+    "                curves.append(xyz); labels.append(name); styles.append('--')\n",
+    "\n",
+    "        if not curves:\n",
+    "            LBL3D.value = \"没有可画的轨迹（检查 metadata.json 是否含 delta 或提供 GT 文件）\"\n",
+    "            plt.close(fig)\n",
+    "            return\n",
+    "\n",
+    "        _plot_equal_3d(ax, curves, labels, styles)\n",
+    "        plt.show()\n",
+    "        LBL3D.value = f\"绘制完成：{', '.join(labels)}\"\n",
+    "\n",
+    "BTN_PLOT3D.on_click(_do_plot3d)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm-jupyter",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/trajectory_demo_1.ipynb b/trajectory_demo_1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2af832bff247a080361798c4e0da022c6d16ffaa
--- /dev/null
+++ b/trajectory_demo_1.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "76251597",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ====== Editor 可视化：工具函数 ======\n",
+    "import os, re, math, io\n",
+    "from pathlib import Path\n",
+    "from typing import List, Tuple, Optional, Dict\n",
+    "\n",
+    "import numpy as np\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import animation\n",
+    "from mpl_toolkits.mplot3d import Axes3D  # noqa: F401\n",
+    "\n",
+    "# 与你给的示例一致的命令模板（dx, dy, dz, dθ）\n",
+    "COMMANDS = {\n",
+    "    'Forward':       np.array([ 1.0,  0.0,  0.0,  0.0], dtype=np.float32),\n",
+    "    'Rotate Right':  np.array([ 0.0,  0.0,  0.0,  0.5], dtype=np.float32),\n",
+    "    'Rotate Left':   np.array([ 0.0,  0.0,  0.0, -0.5], dtype=np.float32),\n",
+    "    'Back':          np.array([-1.0,  0.0,  0.0,  0.0], dtype=np.float32),\n",
+    "    'Up':            np.array([ 0.0,  0.0, -1.0,  0.0], dtype=np.float32),\n",
+    "    'Down':          np.array([ 0.0,  0.0,  1.0,  0.0], dtype=np.float32),\n",
+    "}\n",
+    "COMMAND_NAMES = list(COMMANDS.keys())\n",
+    "CMD_MATRIX = np.stack([COMMANDS[k] for k in COMMAND_NAMES], axis=0)  # [K,4]\n",
+    "\n",
+    "def _read_metadata(run_dir: Path) -> dict:\n",
+    "    p = run_dir / \"metadata.json\"\n",
+    "    if not p.exists(): \n",
+    "        return {}\n",
+    "    import json\n",
+    "    try:\n",
+    "        return json.loads(p.read_text(\"utf-8\"))\n",
+    "    except Exception:\n",
+    "        return {}\n",
+    "\n",
+    "def _read_frames_sequence(run_dir: Path) -> List[Path]:\n",
+    "    \"\"\"按顺序返回帧：init.png + step_*.png（不含 goal.png）。\"\"\"\n",
+    "    frames_dir = run_dir / \"frames\"\n",
+    "    if not frames_dir.exists(): \n",
+    "        return []\n",
+    "    seq = []\n",
+    "    init = frames_dir / \"init.png\"\n",
+    "    if init.exists():\n",
+    "        seq.append(init)\n",
+    "    # 收集 step_*.png\n",
+    "    step_paths = []\n",
+    "    for p in frames_dir.iterdir():\n",
+    "        if p.is_file() and re.match(r\"step_\\d{3}\\.png$\", p.name):\n",
+    "            step_paths.append(p)\n",
+    "    step_paths = sorted(step_paths, key=lambda x: x.name)\n",
+    "    seq.extend(step_paths)\n",
+    "    return seq\n",
+    "\n",
+    "def _deltas_from_metadata(run_dir: Path) -> Optional[np.ndarray]:\n",
+    "    \"\"\"从 metadata.json.frames[].delta 读取 [T,4]。\"\"\"\n",
+    "    meta = _read_metadata(run_dir)\n",
+    "    frames = meta.get(\"frames\", [])\n",
+    "    ds = []\n",
+    "    for f in frames:\n",
+    "        # 跳过 INIT\n",
+    "        if f.get(\"action\",\"INIT\") == \"INIT\":\n",
+    "            continue\n",
+    "        d = f.get(\"delta\")\n",
+    "        if not d: \n",
+    "            continue\n",
+    "        ds.append([float(d.get('dx',0)), float(d.get('dy',0)), float(d.get('dz',0)), float(d.get('dtheta',0))])\n",
+    "    if not ds: \n",
+    "        return None\n",
+    "    return np.asarray(ds, dtype=np.float32)\n",
+    "\n",
+    "def _deltas_from_candidate_pref(run_dir: Path) -> Optional[np.ndarray]:\n",
+    "    \"\"\"候选目录优先使用 deltas.npy；否则回落到其 metadata。\"\"\"\n",
+    "    npy = run_dir / \"deltas.npy\"\n",
+    "    if npy.exists():\n",
+    "        arr = np.load(npy)\n",
+    "        if arr.ndim == 3 and arr.shape[0] == 1:  # (1,T,4) → (T,4)\n",
+    "            arr = arr[0]\n",
+    "        return arr.astype(np.float32)\n",
+    "    return _deltas_from_metadata(run_dir)\n",
+    "\n",
+    "def _integrate_local(deltas: np.ndarray, theta0: float=np.pi/2, local_frame: bool=True) -> Tuple[np.ndarray, np.ndarray]:\n",
+    "    \"\"\"\n",
+    "    把 [T,4](dx,dy,dz,dθ) 积分为：\n",
+    "      positions: [T+1, 3]，从(0,0,0)开始\n",
+    "      thetas:    [T+1]，每步先更新θ，再将(dx,dy)绕当前θ旋到世界坐标后累加（与示例一致）\n",
+    "    \"\"\"\n",
+    "    if deltas is None or len(deltas) == 0:\n",
+    "        return np.zeros((1,3), np.float32), np.array([theta0], np.float32)\n",
+    "\n",
+    "    pos = [np.array([0.0,0.0,0.0], dtype=np.float32)]\n",
+    "    ths = [float(theta0)]\n",
+    "    for dx,dy,dz,dth in deltas:\n",
+    "        th = ths[-1] + float(dth)  # 先转向\n",
+    "        if local_frame:\n",
+    "            c, s = math.cos(th), math.sin(th)\n",
+    "            wx = c*dx - s*dy\n",
+    "            wy = s*dx + c*dy\n",
+    "        else:\n",
+    "            wx, wy = dx, dy\n",
+    "        wz = dz\n",
+    "        pos.append(pos[-1] + np.array([wx, wy, wz], np.float32))\n",
+    "        ths.append(th)\n",
+    "    return np.stack(pos,0), np.asarray(ths, np.float32)\n",
+    "\n",
+    "def _decode_commands_l2(deltas: np.ndarray) -> List[str]:\n",
+    "    \"\"\"\n",
+    "    近似把连续 deltas 映射回离散命令（用于标题与帧上文字；没有就尽量贴近）。\n",
+    "    使用 L2 最近原型（COMMANDS），带一点阈值宽容。\n",
+    "    \"\"\"\n",
+    "    if deltas is None or len(deltas) == 0:\n",
+    "        return []\n",
+    "    # 简单尺度归一，防止dz与dx量纲不同带来的偏置\n",
+    "    scale = np.array([1.0, 1.0, 1.0, 1.0], np.float32)\n",
+    "    names = []\n",
+    "    for v in deltas:\n",
+    "        d = (v / scale)[None, :]           # [1,4]\n",
+    "        dist = np.linalg.norm(d - CMD_MATRIX, axis=1)  # [K]\n",
+    "        k = int(np.argmin(dist))\n",
+    "        names.append(COMMAND_NAMES[k])\n",
+    "    return names\n",
+    "\n",
+    "def _load_video_numpy(paths: List[Path]) -> np.ndarray:\n",
+    "    frames = []\n",
+    "    for p in paths:\n",
+    "        im = Image.open(p).convert(\"RGB\")\n",
+    "        frames.append(np.asarray(im))\n",
+    "    return np.stack(frames, 0)  # [F,H,W,3]\n",
+    "\n",
+    "def _draw_step_text(img_np: np.ndarray, text: str, xy=(10,10), color=(255,0,0)) -> np.ndarray:\n",
+    "    \"\"\"在帧上叠字（和你示例一致：红字左上角）。\"\"\"\n",
+    "    im = Image.fromarray(img_np.copy())\n",
+    "    draw = ImageDraw.Draw(im)\n",
+    "    try:\n",
+    "        font = ImageFont.load_default()\n",
+    "    except Exception:\n",
+    "        font = None\n",
+    "    draw.text(xy, text, fill=color, font=font)\n",
+    "    return np.asarray(im)\n",
+    "\n",
+    "def _axis_with_margin(ax, xs, ys, zs, margin=1.0):\n",
+    "    ax.set_xlim(float(np.min(xs))-margin, float(np.max(xs))+margin)\n",
+    "    ax.set_ylim(float(np.min(ys))-margin, float(np.max(ys))+margin)\n",
+    "    ax.set_zlim(float(np.min(zs))-margin, float(np.max(zs))+margin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "814e0812",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ====== 主函数：与示例同款布局/样式的动画 ======\n",
+    "def animate_editor_run(\n",
+    "    base_dir: str,\n",
+    "    run_id: str,\n",
+    "    source: str = \"candidate\",       # \"candidate\" 或 \"final\"\n",
+    "    cand_name: str = \"cand_000\",     # 当 source=\"candidate\" 时使用\n",
+    "    theta0: float = np.pi/2,         # 初始朝向\n",
+    "    local_frame: bool = True,        # 是否在局部坐标中移动（先转向再走）\n",
+    "    fps: int = 2,\n",
+    "    margin: float = 1.0,\n",
+    "    save_mp4: bool = True,\n",
+    "    out_path: str = \"output.mp4\",\n",
+    "    gt_npy: str = \"\",                # 可选：GT 的 .npy（[T,4] 或 [N,T,4]）\n",
+    "    gt_row: int = -1                 # 可选：当GT是[N,T,4]时的行号（默认用 run_id 编号）\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    从 <base_dir>/editor/run_xxx/ 读取帧与deltas，生成和你示例一样的动画：\n",
+    "    - 左：观测/生成帧（叠加 \"Step i: <action>\"）\n",
+    "    - 右：3D 轨迹 (蓝色 bo- 折线 + 红色方向箭头)\n",
+    "    - 顶部：Sequence: 动作名称 | 动作名称 | ...\n",
+    "    \"\"\"\n",
+    "    base = Path(base_dir).expanduser().resolve()\n",
+    "    final_dir = base / \"editor\" / run_id\n",
+    "    if source == \"candidate\":\n",
+    "        run_dir = final_dir / \"candidates\" / cand_name\n",
+    "        if not run_dir.exists():\n",
+    "            print(f\"[WARN] 找不到 {run_dir}，回退到 final\")\n",
+    "            run_dir = final_dir\n",
+    "    else:\n",
+    "        run_dir = final_dir\n",
+    "\n",
+    "    # 1) 帧序列（严格对齐 init+steps）\n",
+    "    seq_paths = _read_frames_sequence(run_dir)\n",
+    "    if not seq_paths:\n",
+    "        raise FileNotFoundError(f\"未找到帧（init/step_*）: {run_dir}/frames/\")\n",
+    "    video = _load_video_numpy(seq_paths)  # [F,H,W,3]\n",
+    "    F = video.shape[0]\n",
+    "\n",
+    "    # 2) deltas：candidate 优先 deltas.npy，否则 metadata；final 则从 metadata\n",
+    "    deltas = _deltas_from_candidate_pref(run_dir) if (run_dir != final_dir) else _deltas_from_metadata(run_dir)\n",
+    "\n",
+    "    # 3) 轨迹积分（与示例一致：先旋转θ，再走 (dx,dy)）\n",
+    "    positions, thetas = _integrate_local(deltas, theta0=theta0, local_frame=local_frame)  # [T+1,3],[T+1]\n",
+    "    # 对齐帧数：positions 应该和 video 一样是 F（init + T）\n",
+    "    if positions.shape[0] >= F:\n",
+    "        positions = positions[:F]\n",
+    "        thetas = thetas[:F]\n",
+    "    else:\n",
+    "        # 若 positions 偏短，重复最后一个点补齐\n",
+    "        pad = F - positions.shape[0]\n",
+    "        positions = np.vstack([positions, np.repeat(positions[-1:,:], pad, axis=0)])\n",
+    "        thetas = np.concatenate([thetas, np.repeat(thetas[-1:], pad, axis=0)])\n",
+    "\n",
+    "    xs, ys, zs = positions[:,0], positions[:,1], positions[:,2]\n",
+    "\n",
+    "    # 4) 命令名（用近邻反解），用于标题与帧角标\n",
+    "    cmd_names = _decode_commands_l2(deltas) if deltas is not None else []\n",
+    "    # 如果长度对不上，就截断（或补 \"Move\"）\n",
+    "    if len(cmd_names) < max(0, F-1):\n",
+    "        cmd_names += [\"Move\"] * (F-1 - len(cmd_names))\n",
+    "    if len(cmd_names) > max(0, F-1):\n",
+    "        cmd_names = cmd_names[:F-1]\n",
+    "\n",
+    "    # 5) 画布布局（与你的示例一致）\n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    # 顶部标题：Sequence: a | b | c\n",
+    "    seq_title = \" | \".join(cmd_names) if cmd_names else \"(no decoded commands)\"\n",
+    "    fig.suptitle(\"Sequence: \" + seq_title, fontsize=12)\n",
+    "    plt.subplots_adjust(top=0.85)\n",
+    "\n",
+    "    from matplotlib import gridspec\n",
+    "    gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1.5])\n",
+    "\n",
+    "    # 左：视频帧\n",
+    "    ax1 = fig.add_subplot(gs[0])\n",
+    "    im1 = ax1.imshow(video[0])\n",
+    "    ax1.axis(\"off\")\n",
+    "    ax1.set_title(\"Generated View\")\n",
+    "\n",
+    "    # 右：3D 轨迹\n",
+    "    ax2 = fig.add_subplot(gs[1], projection='3d')\n",
+    "    ax2.set_title(\"3D Trajectory\")\n",
+    "    _axis_with_margin(ax2, xs, ys, zs, margin=margin)\n",
+    "    traj_line, = ax2.plot([], [], [], 'bo-', lw=2)  # 样式 bo- 与示例一致\n",
+    "    arrow = ax2.quiver(xs[0], ys[0], zs[0], np.cos(thetas[0]), np.sin(thetas[0]), 0,\n",
+    "                       length=1.0, normalize=True, color='red')\n",
+    "\n",
+    "    # 6) init / animate（与示例一致）\n",
+    "    def init():\n",
+    "        # 叠字：Step 0: INIT（可选）\n",
+    "        frame0 = _draw_step_text(video[0], \"Step 0: INIT\", (10,10), (255,0,0))\n",
+    "        im1.set_data(frame0)\n",
+    "        traj_line.set_data([], [])\n",
+    "        traj_line.set_3d_properties([])\n",
+    "        return im1, traj_line, arrow\n",
+    "\n",
+    "    def animate(i):\n",
+    "        nonlocal arrow\n",
+    "        # 帧 i 的文字：i==0→INIT；i>=1→命令名\n",
+    "        if i == 0:\n",
+    "            text = \"Step 0: INIT\"\n",
+    "        else:\n",
+    "            text = f\"Step {i}: {cmd_names[i-1] if i-1 < len(cmd_names) else 'Move'}\"\n",
+    "        frame = _draw_step_text(video[i], text, (10,10), (255,0,0))\n",
+    "        im1.set_data(frame)\n",
+    "\n",
+    "        traj_line.set_data(xs[:i+1], ys[:i+1])\n",
+    "        traj_line.set_3d_properties(zs[:i+1])\n",
+    "\n",
+    "        # 更新朝向箭头（红色）\n",
+    "        try:\n",
+    "            arrow.remove()\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "        dx = float(np.cos(thetas[i])); dy = float(np.sin(thetas[i])); dz = 0.0\n",
+    "        arrow = ax2.quiver(xs[i], ys[i], zs[i], dx, dy, dz, length=1.0, normalize=True, color='red')\n",
+    "        return im1, traj_line, arrow\n",
+    "\n",
+    "    anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
+    "                                   frames=F, interval=int(1000/max(1,fps)), blit=False)\n",
+    "\n",
+    "    if save_mp4:\n",
+    "        # 优先 ffmpeg，否则回退 GIF（Pillow）\n",
+    "        try:\n",
+    "            writer = animation.FFMpegWriter(fps=fps, bitrate=2000)\n",
+    "            anim.save(out_path, writer=writer)\n",
+    "            print(f\"[OK] MP4 saved → {out_path} (ffmpeg)\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"[WARN] ffmpeg 不可用：{e}；改用 GIF（PillowWriter）\")\n",
+    "            gif_out = os.path.splitext(out_path)[0] + \".gif\"\n",
+    "            anim.save(gif_out, writer=animation.PillowWriter(fps=fps))\n",
+    "            print(f\"[OK] GIF saved → {gif_out}\")\n",
+    "    else:\n",
+    "        plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f53643ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MovieWriter stderr:\n",
+      "[out#0/mp4 @ 0x5625b1fa5440] Error opening output output12.mp4: No space left on device\n",
+      "Error opening output file output12.mp4.\n",
+      "Error opening output files: No space left on device\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[WARN] ffmpeg 不可用：Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '1200x600', '-pix_fmt', 'rgba', '-r', '2', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-b', '2000k', '-y', 'output12.mp4']' returned non-zero exit status 228.；改用 GIF（PillowWriter）\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "[Errno 28] No space left on device: 'output12.gif'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mBrokenPipeError\u001b[0m                           Traceback (most recent call last)",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:233\u001b[0m, in \u001b[0;36mAbstractMovieWriter.saving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 233\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:1107\u001b[0m, in \u001b[0;36mAnimation.save\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1106\u001b[0m         frame_number \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m-> 1107\u001b[0m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrab_frame\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msavefig_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:359\u001b[0m, in \u001b[0;36mMovieWriter.grab_frame\u001b[0;34m(self, **savefig_kwargs)\u001b[0m\n\u001b[1;32m    358\u001b[0m \u001b[38;5;66;03m# Save the figure data to the sink, using the frame format and dpi.\u001b[39;00m\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_proc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstdin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mframe_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    360\u001b[0m \u001b[43m                 \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msavefig_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/figure.py:3378\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3375\u001b[0m         stack\u001b[38;5;241m.\u001b[39menter_context(\n\u001b[1;32m   3376\u001b[0m             ax\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39m_cm_set(facecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m-> 3378\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/backend_bases.py:2366\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2365\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2366\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2367\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2368\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2369\u001b[0m \u001b[43m            \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2370\u001b[0m \u001b[43m            \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2371\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2372\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2373\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/backend_bases.py:2232\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2231\u001b[0m     skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2232\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2233\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mskip\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   2234\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:448\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_raw\u001b[0;34m(self, filename_or_obj)\u001b[0m\n\u001b[1;32m    447\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39mopen_file_cm(filename_or_obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m fh:\n\u001b[0;32m--> 448\u001b[0m     \u001b[43mfh\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuffer_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mBrokenPipeError\u001b[0m: [Errno 32] Broken pipe",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 126\u001b[0m, in \u001b[0;36manimate_editor_run\u001b[0;34m(base_dir, run_id, source, cand_name, theta0, local_frame, fps, margin, save_mp4, out_path, gt_npy, gt_row)\u001b[0m\n\u001b[1;32m    125\u001b[0m writer \u001b[38;5;241m=\u001b[39m animation\u001b[38;5;241m.\u001b[39mFFMpegWriter(fps\u001b[38;5;241m=\u001b[39mfps, bitrate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2000\u001b[39m)\n\u001b[0;32m--> 126\u001b[0m \u001b[43manim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwriter\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[OK] MP4 saved → \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mout_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (ffmpeg)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:1085\u001b[0m, in \u001b[0;36mAnimation.save\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1082\u001b[0m \u001b[38;5;66;03m# canvas._is_saving = True makes the draw_event animation-starting\u001b[39;00m\n\u001b[1;32m   1083\u001b[0m \u001b[38;5;66;03m# callback a no-op; canvas.manager = None prevents resizing the GUI\u001b[39;00m\n\u001b[1;32m   1084\u001b[0m \u001b[38;5;66;03m# widget (both are likewise done in savefig()).\u001b[39;00m\n\u001b[0;32m-> 1085\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrc_context({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msavefig.bbox\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m}), \\\n\u001b[1;32m   1086\u001b[0m      writer\u001b[38;5;241m.\u001b[39msaving(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fig, filename, dpi), \\\n\u001b[1;32m   1087\u001b[0m      cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fig\u001b[38;5;241m.\u001b[39mcanvas,\n\u001b[1;32m   1088\u001b[0m                        _is_saving\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, manager\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m   1089\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m anim \u001b[38;5;129;01min\u001b[39;00m all_anim:\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/contextlib.py:153\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m    152\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraceback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    154\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[1;32m    156\u001b[0m     \u001b[38;5;66;03m# was passed to throw().  This prevents a StopIteration\u001b[39;00m\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:235\u001b[0m, in \u001b[0;36mAbstractMovieWriter.saving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m--> 235\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinish\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:349\u001b[0m, in \u001b[0;36mMovieWriter.finish\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_proc\u001b[38;5;241m.\u001b[39mreturncode:\n\u001b[0;32m--> 349\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m subprocess\u001b[38;5;241m.\u001b[39mCalledProcessError(\n\u001b[1;32m    350\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_proc\u001b[38;5;241m.\u001b[39mreturncode, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_proc\u001b[38;5;241m.\u001b[39margs, out, err)\n",
+      "\u001b[0;31mCalledProcessError\u001b[0m: Command '['ffmpeg', '-f', 'rawvideo', '-vcodec', 'rawvideo', '-s', '1200x600', '-pix_fmt', 'rgba', '-r', '2', '-loglevel', 'error', '-i', 'pipe:', '-vcodec', 'h264', '-pix_fmt', 'yuv420p', '-b', '2000k', '-y', 'output12.mp4']' returned non-zero exit status 228.",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43manimate_editor_run\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbase_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrun_012\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcandidate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m           \u001b[49m\u001b[38;5;66;43;03m# 或 \"final\"\u001b[39;49;00m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcand_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcand_000\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m         \u001b[49m\u001b[38;5;66;43;03m# candidate 时有效\u001b[39;49;00m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtheta0\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpi\u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m               \u001b[49m\u001b[38;5;66;43;03m# 初始朝上\u001b[39;49;00m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlocal_frame\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m             \u001b[49m\u001b[38;5;66;43;03m# 与你的 update_position 行为一致\u001b[39;49;00m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmargin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43msave_mp4\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43mout_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43moutput12.mp4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[2], line 131\u001b[0m, in \u001b[0;36manimate_editor_run\u001b[0;34m(base_dir, run_id, source, cand_name, theta0, local_frame, fps, margin, save_mp4, out_path, gt_npy, gt_row)\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[WARN] ffmpeg 不可用：\u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m；改用 GIF（PillowWriter）\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    130\u001b[0m         gif_out \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplitext(out_path)[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.gif\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 131\u001b[0m         \u001b[43manim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgif_out\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manimation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPillowWriter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfps\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[OK] GIF saved → \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mgif_out\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:1085\u001b[0m, in \u001b[0;36mAnimation.save\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1081\u001b[0m savefig_kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransparent\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m   \u001b[38;5;66;03m# just to be safe!\u001b[39;00m\n\u001b[1;32m   1082\u001b[0m \u001b[38;5;66;03m# canvas._is_saving = True makes the draw_event animation-starting\u001b[39;00m\n\u001b[1;32m   1083\u001b[0m \u001b[38;5;66;03m# callback a no-op; canvas.manager = None prevents resizing the GUI\u001b[39;00m\n\u001b[1;32m   1084\u001b[0m \u001b[38;5;66;03m# widget (both are likewise done in savefig()).\u001b[39;00m\n\u001b[0;32m-> 1085\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m mpl\u001b[38;5;241m.\u001b[39mrc_context({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msavefig.bbox\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m}), \\\n\u001b[1;32m   1086\u001b[0m      writer\u001b[38;5;241m.\u001b[39msaving(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fig, filename, dpi), \\\n\u001b[1;32m   1087\u001b[0m      cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fig\u001b[38;5;241m.\u001b[39mcanvas,\n\u001b[1;32m   1088\u001b[0m                        _is_saving\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, manager\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m   1089\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m anim \u001b[38;5;129;01min\u001b[39;00m all_anim:\n\u001b[1;32m   1090\u001b[0m         anim\u001b[38;5;241m.\u001b[39m_init_draw()  \u001b[38;5;66;03m# Clear the initial frame\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/contextlib.py:142\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    141\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 142\u001b[0m         \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    144\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:235\u001b[0m, in \u001b[0;36mAbstractMovieWriter.saving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m--> 235\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinish\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/matplotlib/animation.py:501\u001b[0m, in \u001b[0;36mPillowWriter.finish\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    500\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfinish\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 501\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_frames\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave_all\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mappend_images\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_frames\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfps\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nwm3/lib/python3.10/site-packages/PIL/Image.py:2583\u001b[0m, in \u001b[0;36mImage.save\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2581\u001b[0m         fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2582\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2583\u001b[0m         fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw+b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2584\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2585\u001b[0m     fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n",
+      "\u001b[0;31mOSError\u001b[0m: [Errno 28] No space left on device: 'output12.gif'"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "animate_editor_run(\n",
+    "    base_dir=\"/data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/\",\n",
+    "    run_id=\"run_012\",\n",
+    "    source=\"candidate\",           # 或 \"final\"\n",
+    "    cand_name=\"cand_000\",         # candidate 时有效\n",
+    "    theta0=np.pi/2,               # 初始朝上\n",
+    "    local_frame=True,             # 与你的 update_position 行为一致\n",
+    "    fps=2,\n",
+    "    margin=1.0,\n",
+    "    save_mp4=True,\n",
+    "    out_path=\"output12.mp4\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nwm3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/trajectory_demo_2.ipynb b/trajectory_demo_2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..75e77dd13c1e0df417f19c492404d67256976288
--- /dev/null
+++ b/trajectory_demo_2.ipynb
@@ -0,0 +1,299 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5bf1fd26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 根目录：里面可能有很多 run 子目录；每个子目录都含 frames/ 与 metadata.json\n",
+    "ROOT = \"/data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006\"\n",
+    "OVERWRITE = False        # 已存在是否覆盖\n",
+    "DRY_RUN = False          # 只打印将要做什么，不实际生成"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "51ede2f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, json, traceback\n",
+    "import numpy as np\n",
+    "from PIL import Image, ImageDraw\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import animation\n",
+    "from matplotlib import gridspec\n",
+    "from pathlib import Path\n",
+    "\n",
+    "def _nice_lim(vals, margin=1.0):\n",
+    "    lo, hi = float(np.min(vals)), float(np.max(vals))\n",
+    "    if np.isclose(lo, hi):\n",
+    "        lo, hi = lo - margin, hi + margin\n",
+    "    else:\n",
+    "        lo, hi = lo - margin, hi + margin\n",
+    "    return lo, hi\n",
+    "\n",
+    "def _draw_text(img_arr, text):\n",
+    "    img = Image.fromarray(img_arr)\n",
+    "    draw = ImageDraw.Draw(img)\n",
+    "    draw.text((10, 10), text, fill=(255, 0, 0))\n",
+    "    return np.array(img)\n",
+    "\n",
+    "def _read_metadata(meta_path: str):\n",
+    "    with open(meta_path, \"r\", encoding=\"utf-8\") as f:\n",
+    "        meta = json.load(f)\n",
+    "    return meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d34a44d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_run(run_dir: str, add_text=True):\n",
+    "    \"\"\"\n",
+    "    run_dir: 目录中应包含 metadata.json 和 frames/xxx.png\n",
+    "    返回: video[np.uint8 T,H,W,3], positions[List[(x,y,z,theta)]], meta(dict)\n",
+    "    \"\"\"\n",
+    "    run_dir = Path(run_dir)\n",
+    "    meta_path = run_dir / \"metadata.json\"\n",
+    "    assert meta_path.exists(), f\"找不到 {meta_path}\"\n",
+    "\n",
+    "    meta = _read_metadata(str(meta_path))\n",
+    "    frames_info = meta[\"frames\"]\n",
+    "    fps = int(meta.get(\"fps\", 2))\n",
+    "\n",
+    "    video = []\n",
+    "    positions = []\n",
+    "    for fobj in frames_info:\n",
+    "        png_rel = fobj[\"png\"]  # 可能是 \"frames/xxx.png\" 或仅文件名\n",
+    "        png_path = run_dir / Path(png_rel).name\n",
+    "        if not png_path.exists():\n",
+    "            # 兼容路径含 frames 子目录\n",
+    "            png_path = run_dir / png_rel\n",
+    "        assert png_path.exists(), f\"帧不存在: {png_path}\"\n",
+    "\n",
+    "        img = np.array(Image.open(png_path).convert(\"RGB\"))\n",
+    "\n",
+    "        if add_text:\n",
+    "            step_idx = fobj[\"frame\"]\n",
+    "            action   = fobj.get(\"action\", \"\")\n",
+    "            img = _draw_text(img, f\"Step {step_idx}: {action}\")\n",
+    "\n",
+    "        video.append(img)\n",
+    "\n",
+    "        pose = fobj[\"pose\"]\n",
+    "        positions.append((pose[\"x\"], pose[\"y\"], pose[\"z\"], pose[\"theta_rad\"]))\n",
+    "\n",
+    "    # 统一尺寸\n",
+    "    H0, W0 = video[0].shape[:2]\n",
+    "    for i in range(len(video)):\n",
+    "        if video[i].shape[:2] != (H0, W0):\n",
+    "            video[i] = np.array(Image.fromarray(video[i]).resize((W0, H0)))\n",
+    "\n",
+    "    video = np.stack(video, axis=0)\n",
+    "    meta[\"_fps\"] = fps\n",
+    "    return video, positions, meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "058a5997",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def render_run_to_mp4(run_dir: str, save_name=\"output.mp4\", overwrite=False, dry_run=False):\n",
+    "    \"\"\"\n",
+    "    读取 run_dir 下的数据并保存 mp4 到 run_dir/save_name\n",
+    "    返回输出路径或 None\n",
+    "    \"\"\"\n",
+    "    run_dir = Path(run_dir)\n",
+    "    out_mp4 = run_dir / save_name\n",
+    "    if out_mp4.exists() and not overwrite:\n",
+    "        print(f\"[Skip] {out_mp4} 已存在（设置 OVERWRITE=True 可覆盖）\")\n",
+    "        return str(out_mp4)\n",
+    "\n",
+    "    if dry_run:\n",
+    "        print(f\"[DryRun] 预备生成: {out_mp4}\")\n",
+    "        return None\n",
+    "\n",
+    "    video, positions, meta = load_run(str(run_dir))\n",
+    "    fps = int(meta.get(\"_fps\", meta.get(\"fps\", 2)))\n",
+    "\n",
+    "    xs = [p[0] for p in positions]\n",
+    "    ys = [p[1] for p in positions]\n",
+    "    zs = [p[2] for p in positions]\n",
+    "    thetas = [p[3] for p in positions]\n",
+    "\n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    fig.suptitle(f\"Run {meta.get('run_index', '')} | type={meta.get('type', '')}\", fontsize=12)\n",
+    "    plt.subplots_adjust(top=0.9)\n",
+    "\n",
+    "    gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1.5])\n",
+    "    ax1 = fig.add_subplot(gs[0])\n",
+    "    im1 = ax1.imshow(video[0])\n",
+    "    ax1.axis('off')\n",
+    "    ax1.set_title(\"Generated View\")\n",
+    "\n",
+    "    ax2 = fig.add_subplot(gs[1], projection='3d')\n",
+    "    ax2.set_title(\"3D Trajectory\")\n",
+    "    ax2.set_xlim(*_nice_lim(xs, 1))\n",
+    "    ax2.set_ylim(*_nice_lim(ys, 1))\n",
+    "    ax2.set_zlim(*_nice_lim(zs, 1))\n",
+    "\n",
+    "    traj_line, = ax2.plot([], [], [], 'bo-', lw=2)\n",
+    "    arrow = None\n",
+    "\n",
+    "    def init():\n",
+    "        nonlocal arrow\n",
+    "        im1.set_data(video[0])\n",
+    "        traj_line.set_data([], [])\n",
+    "        traj_line.set_3d_properties([])\n",
+    "        if arrow is not None:\n",
+    "            try: arrow.remove()\n",
+    "            except Exception: pass\n",
+    "        dx, dy, dz = np.cos(thetas[0]), np.sin(thetas[0]), 0\n",
+    "        arrow = ax2.quiver(xs[0], ys[0], zs[0], dx, dy, dz, length=1.0, normalize=True, color='red')\n",
+    "        return im1, traj_line, arrow\n",
+    "\n",
+    "    def animate(i):\n",
+    "        nonlocal arrow\n",
+    "        im1.set_data(video[i])\n",
+    "        traj_line.set_data(xs[:i+1], ys[:i+1])\n",
+    "        traj_line.set_3d_properties(zs[:i+1])\n",
+    "        if arrow is not None:\n",
+    "            try: arrow.remove()\n",
+    "            except Exception: pass\n",
+    "        dx, dy, dz = np.cos(thetas[i]), np.sin(thetas[i]), 0\n",
+    "        arrow = ax2.quiver(xs[i], ys[i], zs[i], dx, dy, dz, length=1.0, normalize=True, color='red')\n",
+    "        return im1, traj_line, arrow\n",
+    "\n",
+    "    anim = animation.FuncAnimation(\n",
+    "        fig, animate, init_func=init,\n",
+    "        frames=min(len(video), len(positions)),\n",
+    "        interval=max(1, int(1000 // max(1, fps))),\n",
+    "        blit=False\n",
+    "    )\n",
+    "\n",
+    "    anim.save(str(out_mp4), writer='ffmpeg', fps=fps)\n",
+    "    plt.close(fig)\n",
+    "    print(f\"[OK] Saved: {out_mp4}\")\n",
+    "    return str(out_mp4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c2fae8f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def iter_run_dirs(root: str):\n",
+    "    \"\"\"\n",
+    "    递归找到所有包含 metadata.json 的目录（允许该目录名是 frames/ 或上层 run 目录）\n",
+    "    返回去重后的目录列表（即 metadata.json 所在目录）\n",
+    "    \"\"\"\n",
+    "    root = Path(root)\n",
+    "    found = set()\n",
+    "    for p in root.rglob(\"metadata.json\"):\n",
+    "        found.add(str(p.parent))\n",
+    "    return sorted(found)\n",
+    "\n",
+    "def batch_render(root: str, save_name=\"output.mp4\", overwrite=False, dry_run=False):\n",
+    "    run_dirs = iter_run_dirs(root)\n",
+    "    print(f\"发现 {len(run_dirs)} 个 run 目录\")\n",
+    "    outputs = []\n",
+    "    for rd in run_dirs:\n",
+    "        try:\n",
+    "            out = render_run_to_mp4(rd, save_name=save_name, overwrite=overwrite, dry_run=dry_run)\n",
+    "            outputs.append((rd, out, \"ok\"))\n",
+    "        except Exception as e:\n",
+    "            print(f\"[ERR] {rd}\\n{e}\")\n",
+    "            traceback.print_exc()\n",
+    "            outputs.append((rd, None, \"error\"))\n",
+    "    return outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e496a1c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "发现 11 个 run 目录\n",
+      "[Skip] /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/output.mp4 已存在（设置 OVERWRITE=True 可覆盖）\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_000/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_001/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_002/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_003/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_004/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_005/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_006/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_007/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_008/output.mp4\n",
+      "[OK] Saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_009/output.mp4\n",
+      "完成，统计：\n",
+      "OK: 11 | ERR: 0\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_000 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_000/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_001 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_001/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_002 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_002/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_003 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_003/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_004 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_004/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_005 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_005/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_006 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_006/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_007 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_007/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_008 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_008/output.mp4\n",
+      "ok -> /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_009 => /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CEM_N10_K10_RS1_rep3_OPT1/editor/run_006/candidates/cand_009/output.mp4\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = batch_render(ROOT)\n",
+    "print(\"完成，统计：\")\n",
+    "ok = sum(1 for _,_,s in results if s==\"ok\")\n",
+    "err = sum(1 for _,_,s in results if s==\"error\")\n",
+    "print(f\"OK: {ok} | ERR: {err}\")\n",
+    "for rd, out, st in results:\n",
+    "    print(st, \"->\", rd, \"=>\", out)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nwm2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/trajectory_generation.py b/trajectory_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0698c50f0b990be75890032dc2c46547ad622e5
--- /dev/null
+++ b/trajectory_generation.py
@@ -0,0 +1,185 @@
+# A 3D trajectory genration module for a drone. Generates several trajectories by sampling from random positions near certain waypoints.
+# Trajectory is a sequence of 3D points of (x,y,z,yaw).
+
+
+
+
+import torch
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+def sample_trajectory_from_distribution(start, mu, sigma):
+    """
+    从给定分布中采样轨迹。
+    每步采样 [distance, delta_yaw, vertical_flag] 来控制移动。
+    
+    参数:
+    - start: 起始点 (x, y, z, yaw)
+    - mu: Tensor[steps, 3]，每步的动作均值（distance, delta_yaw, is_vertical）
+    - sigma: Tensor[steps, 3]，每步的动作方差
+
+    返回:
+    - trajectory: List of (x, y, z, yaw)
+    """
+    assert mu.shape == sigma.shape, "mu and sigma shape mismatch"
+    steps = mu.shape[0]
+    trajectory = [start]
+    current_pos = np.array(start[:3], dtype=float)
+    current_yaw = start[3]
+
+    for step in range(steps):
+        # 从高斯分布采样动作参数
+        action = torch.randn(3) * sigma[step] + mu[step]
+        distance = action[0].item()
+        delta_yaw = np.clip(action[1].item(), -np.pi/2, np.pi/2)
+        is_vertical = torch.sigmoid(action[2]).item() > 0.5
+
+        current_yaw = (current_yaw + delta_yaw) % (2 * np.pi)
+        dx = dy = dz = 0
+
+        if is_vertical:
+            dz = distance * np.random.choice([-1, 1])
+        else:
+            dx = distance * np.cos(current_yaw)
+            dy = distance * np.sin(current_yaw)
+
+        current_pos += np.array([dx, dy, dz])
+        trajectory.append((current_pos[0], current_pos[1], current_pos[2], current_yaw))
+
+    return trajectory
+
+def random_trajectory(start, steps=6):
+    trajectory = [start]
+    current_position = np.array(start[:3], dtype=float)  # (x, y, z) - 确保为浮点类型
+    
+    for _ in range(steps):
+        dx = dy = dz = 0  # Initialize movement components
+
+        is_vertical = np.random.choice([True, False], p=[0.5, 0.5])  # 30% vertical movement
+        delta_yaw = np.random.normal(0, np.pi/4)  # Normal distribution for yaw change (mean=0, std=π/4)
+        delta_yaw = np.clip(delta_yaw, -np.pi/2, np.pi/2) # restrict yaw to [-π/2, π/2]
+        current_yaw = (start[3] + delta_yaw) % (2 * np.pi)  # Update yaw
+        distance = np.random.uniform(2, 4)  # Random distance
+        if is_vertical:
+            # Vertical movement
+            dz = distance * np.random.choice([-1, 1])
+        else:
+            # Horizontal movement
+            dx = distance * np.cos(current_yaw)
+            dy = distance * np.sin(current_yaw)
+        
+        current_position += np.array([dx, dy, dz])
+        trajectory.append((current_position[0], current_position[1], current_position[2], current_yaw))
+    
+    return trajectory
+
+def trajectory_generation(start_coord, steps=6, candidate_number=20):
+    """
+    生成多个轨迹的函数
+    
+    参数:
+    start_coord: tuple - 起始坐标 (x, y, z, yaw)
+    steps: int - 每条轨迹的步数，默认6步
+    candidate_number: int - 候选轨迹数量，默认20条
+    
+    返回:
+    list - 包含多个轨迹的列表，每个轨迹是一个包含坐标点的列表
+    """
+    candidate_trajectoires = []
+    
+    while len(candidate_trajectoires) < candidate_number:
+        # Generate a random starting direction
+        starting_yaw = np.random.uniform(-np.pi, np.pi)
+        trajectory = random_trajectory(
+            (start_coord[0], start_coord[1], start_coord[2], starting_yaw),
+            steps
+        )
+        candidate_trajectoires.append(trajectory)
+    
+    return candidate_trajectoires
+
+def plot_3d_trajectories(candidate_trajectoires, save_path="./plot", filename="trajectories_3d.png", show_plot=True):
+    """
+    绘制3D轨迹图的函数
+    
+    参数:
+    candidate_trajectoires: list - 包含多个轨迹的列表
+    save_path: str - 图片保存路径，默认为 "./plot"
+    filename: str - 图片文件名，默认为 "trajectories_3d.png"
+    show_plot: bool - 是否显示图片，默认为 True
+    """
+    # 创建保存目录（如果不存在）
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+    
+    fig = plt.figure(figsize=(12, 9))
+    ax = fig.add_subplot(111, projection='3d')
+    
+    colors = ['blue', 'green', 'orange', 'purple', 'brown', 'red', 'cyan', 'magenta', 'yellow', 'black']  # 扩展颜色列表
+    
+    for i, traj in enumerate(candidate_trajectoires):
+        xs, ys, zs = zip(*[(point[0], point[1], point[2]) for point in traj])
+        color = colors[i % len(colors)]
+        
+        # 绘制轨迹线
+        ax.plot(xs, ys, zs, color=color, alpha=0.7, linewidth=2, label=f'Trajectory {i+1}')
+        
+        # 标记起点
+        ax.scatter(xs[0], ys[0], zs[0], color=color, s=100, marker='o', edgecolors='black', linewidth=1)
+        
+        # 标记中间节点（除了起点和终点）
+        if len(xs) > 2:
+            ax.scatter(xs[1:-1], ys[1:-1], zs[1:-1], color=color, s=30, marker='o', alpha=0.8)
+        
+        # 标记终点
+        ax.scatter(xs[-1], ys[-1], zs[-1], color=color, s=80, marker='s', edgecolors='black', linewidth=1)
+    
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_zlabel('Z')
+    ax.set_title('3D Trajectories Visualization')
+    
+    # 只在轨迹数量较少时显示图例
+    if len(candidate_trajectoires) <= 10:
+        ax.legend()
+    
+    # 保存图片
+    full_path = os.path.join(save_path, filename)
+    plt.savefig(full_path, dpi=300, bbox_inches='tight')
+    print(f"图片已保存到: {full_path}")
+    
+    # 显示图片
+    if show_plot:
+        plt.show()
+    else:
+        plt.close()
+
+
+if __name__ == "__main__":
+    # Initiate
+    origin_crd = (0.0, 0.0, 0.0, 0.0)  # (x, y, z, yaw)
+    candidate_number = 20
+    step_number = 6
+
+    candidate_trajectoires = []
+
+    # Generate candidate trajectories
+    candidate_trajectoires = trajectory_generation(
+        origin_crd, 
+        steps=step_number, 
+        candidate_number=candidate_number
+    )
+    
+    # Plot the trajectories
+    plot_3d_trajectories(
+        candidate_trajectoires, 
+        save_path="./plot", 
+        filename="drone_trajectories.png"
+    )
+
+
+    
+
+
+
diff --git a/trajectory_navigation_demo.ipynb b/trajectory_navigation_demo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..433274c4810a5fbe78bbfcee505da4648a865449
--- /dev/null
+++ b/trajectory_navigation_demo.ipynb
@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5bf1fd26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# === Config (edit here) ===\n",
+    "ROOT = \"/data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CAND_N8_K3_RS1_rep3/editor/run_006\"            # 根目录：包含多个子目录，每个子目录有 metadata.json 与 frames/*.png\n",
+    "MASTER_OUT = ROOT + \"/master.mp4\"  # 输出视频文件名\n",
+    "FPS = 2                    # 合成视频帧率\n",
+    "PAUSE_SEC = 2.0   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "51ede2f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# === Imports ===\n",
+    "import os, json\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "from PIL import Image, ImageDraw\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import gridspec, animation\n",
+    "from IPython.display import HTML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0d34a44d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_master_animation(root: str, fps: int = 2, pause_sec: float = 0.0, overlay_text: bool = True):\n",
+    "    \"\"\"\n",
+    "    Build a master animation from runs under `root`.\n",
+    "\n",
+    "    Layout:\n",
+    "      - Left  : Current View (frame image, optional step/action overlay)\n",
+    "      - Middle: 3D trajectories (black = finished runs, red = current run) + heading arrow\n",
+    "      - Right : Fixed GT image (ROOT/frames/goal.png), resized to match frames\n",
+    "\n",
+    "    Rules:\n",
+    "      - During a run, the red polyline grows with frames.\n",
+    "      - ONLY on that run's *last frame* do we finalize it into a black line.\n",
+    "      - Pause between runs is implemented by repeating the last frame (no on-screen text).\n",
+    "\n",
+    "    Returns:\n",
+    "      (fig, anim)  # Matplotlib Figure and FuncAnimation\n",
+    "    \"\"\"\n",
+    "    root = Path(root)\n",
+    "    run_dirs = sorted({p.parent for p in root.rglob(\"metadata.json\")})\n",
+    "    assert run_dirs, f\"No metadata.json found under {root}\"\n",
+    "\n",
+    "    # --- Fixed global GT at ROOT/frames/goal.png ---\n",
+    "    global_gt_path = root / \"frames\" / \"goal.png\"\n",
+    "    assert global_gt_path.exists(), f\"Global GT not found: {global_gt_path}\"\n",
+    "    global_gt_img = Image.open(global_gt_path).convert(\"RGB\")\n",
+    "\n",
+    "    # --- Load all runs: [(frames_rgb, poses, targets_rgb)] ---\n",
+    "    runs = []\n",
+    "    for rd in run_dirs:\n",
+    "        meta = json.load(open(rd / \"metadata.json\", \"r\", encoding=\"utf-8\"))\n",
+    "        frames_rgb, poses, targets_rgb = [], [], []\n",
+    "\n",
+    "        for f in meta[\"frames\"]:\n",
+    "            # Frame path: allow \"frames/xxx.png\" or just \"xxx.png\"\n",
+    "            rel = f[\"png\"]\n",
+    "            p1 = rd / rel\n",
+    "            p2 = rd / Path(rel).name\n",
+    "            png = p1 if p1.exists() else p2\n",
+    "            assert png.exists(), f\"Frame not found: {png}\"\n",
+    "\n",
+    "            img = Image.open(png).convert(\"RGB\")\n",
+    "            if overlay_text:\n",
+    "                step = f.get(\"frame\", 0); act = f.get(\"action\", \"\")\n",
+    "                draw = ImageDraw.Draw(img)\n",
+    "                draw.text((10, 10), f\"Step {step}: {act}\", fill=(255, 0, 0))\n",
+    "            frames_rgb.append(np.array(img))\n",
+    "\n",
+    "            p = f[\"pose\"]\n",
+    "            poses.append((p[\"x\"], p[\"y\"], p[\"z\"], p[\"theta_rad\"]))\n",
+    "            targets_rgb.append(None)  # placeholder (size set below)\n",
+    "\n",
+    "        # Unify size within this run (use first frame as reference)\n",
+    "        h, w = frames_rgb[0].shape[:2]\n",
+    "        for i in range(len(frames_rgb)):\n",
+    "            if frames_rgb[i].shape[:2] != (h, w):\n",
+    "                frames_rgb[i] = np.array(Image.fromarray(frames_rgb[i]).resize((w, h)))\n",
+    "\n",
+    "        # Fixed GT for all frames in this run\n",
+    "        gt_resized = np.array(global_gt_img.resize((w, h)))\n",
+    "        targets_rgb = [gt_resized for _ in targets_rgb]\n",
+    "\n",
+    "        runs.append((frames_rgb, poses, targets_rgb))\n",
+    "\n",
+    "    # --- Global 3D axis limits ---\n",
+    "    all_xyz = np.array([p[:3] for _, poses, _ in runs for p in poses], dtype=np.float32)\n",
+    "    minv = all_xyz.min(axis=0); maxv = all_xyz.max(axis=0)\n",
+    "    pad = 0.05 * max(maxv - minv) or 1.0\n",
+    "\n",
+    "    # --- Build schedule with optional silent pauses ---\n",
+    "    # Each item: (run_idx, frame_idx, is_pause, pause_tick)\n",
+    "    pause_frames = int(round(max(0.0, pause_sec) * max(1, fps)))\n",
+    "    schedule = []\n",
+    "    for i, (frames_rgb, _, _) in enumerate(runs):\n",
+    "        T = len(frames_rgb)\n",
+    "        for fidx in range(T):\n",
+    "            schedule.append((i, fidx, False, 0))          # normal frames\n",
+    "        schedule.append((i, T - 1, False, 0))             # one freeze frame\n",
+    "        for k in range(1, pause_frames + 1):\n",
+    "            schedule.append((i, T - 1, True, k))          # extra pauses\n",
+    "\n",
+    "    # --- Figure: [Left | Middle(3D) | Right(GT)] ---\n",
+    "    fig = plt.figure(figsize=(15, 6))\n",
+    "    gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1.5, 1])\n",
+    "\n",
+    "    ax_img = fig.add_subplot(gs[0]); ax_img.axis('off'); ax_img.set_title(\"Current View\")\n",
+    "    im = ax_img.imshow(runs[0][0][0])\n",
+    "\n",
+    "    ax3d = fig.add_subplot(gs[1], projection='3d')\n",
+    "    ax3d.set_title(\"Overlaid Trajectories (black=done, red=current)\")\n",
+    "    ax3d.set_xlim(minv[0] - pad, maxv[0] + pad)\n",
+    "    ax3d.set_ylim(minv[1] - pad, maxv[1] + pad)\n",
+    "    ax3d.set_zlim(minv[2] - pad, maxv[2] + pad)\n",
+    "\n",
+    "    ax_gt = fig.add_subplot(gs[2]); ax_gt.axis('off'); ax_gt.set_title(\"Target (GT for loss)\")\n",
+    "    im_gt = ax_gt.imshow(runs[0][2][0])\n",
+    "\n",
+    "    # --- Artists state ---\n",
+    "    done_lines = []         # finished runs (black)\n",
+    "    current_line = None     # growing red line\n",
+    "    current_arrow = None    # heading arrow (quiver)\n",
+    "    prev_run = None\n",
+    "\n",
+    "    # Arrow length relative to scene size\n",
+    "    arrow_len = 0.07 * max(maxv - minv)\n",
+    "\n",
+    "    def init():\n",
+    "        nonlocal current_line, current_arrow, prev_run\n",
+    "        im.set_data(runs[0][0][0])\n",
+    "        im_gt.set_data(runs[0][2][0])\n",
+    "        current_line, = ax3d.plot([], [], [], '-', lw=3, color='red')\n",
+    "        current_arrow = ax3d.quiver(0, 0, 0, 0, 0, 0, length=arrow_len, normalize=False, color='red')\n",
+    "        prev_run = 0\n",
+    "        return [im, im_gt, current_line, current_arrow]\n",
+    "\n",
+    "    def animate(k):\n",
+    "        nonlocal current_line, current_arrow, prev_run\n",
+    "        r, f, is_pause, _ = schedule[k]\n",
+    "        frames_rgb, poses, targets_rgb = runs[r]\n",
+    "\n",
+    "        # If we switched to a new run: reset red line & arrow (do NOT finalize here)\n",
+    "        if prev_run is not None and prev_run != r:\n",
+    "            if current_line is not None:\n",
+    "                current_line.remove()\n",
+    "            current_line, = ax3d.plot([], [], [], '-', lw=3, color='red')\n",
+    "            if current_arrow is not None:\n",
+    "                try: current_arrow.remove()\n",
+    "                except Exception: pass\n",
+    "            current_arrow = ax3d.quiver(0, 0, 0, 0, 0, 0, length=arrow_len, normalize=False, color='red')\n",
+    "            prev_run = r\n",
+    "\n",
+    "        # Update left/right images\n",
+    "        im.set_data(frames_rgb[f])\n",
+    "        im_gt.set_data(targets_rgb[f])\n",
+    "\n",
+    "        # Update the red partial path up to current frame\n",
+    "        xs, ys, zs = zip(*[p[:3] for p in poses[:f+1]])\n",
+    "        current_line.set_data(xs, ys)\n",
+    "        current_line.set_3d_properties(zs)\n",
+    "\n",
+    "        # Update heading arrow at current pose using theta\n",
+    "        x, y, z, theta = poses[f]\n",
+    "        dx, dy, dz = np.cos(theta) * arrow_len, np.sin(theta) * arrow_len, 0.0\n",
+    "        if current_arrow is not None:\n",
+    "            try: current_arrow.remove()\n",
+    "            except Exception: pass\n",
+    "        current_arrow = ax3d.quiver(x, y, z, dx, dy, dz, length=1.0, normalize=False, color='red')\n",
+    "\n",
+    "        # Finalize this run ONLY at its true last frame (not during pauses)\n",
+    "        if not is_pause and f == len(poses) - 1:\n",
+    "            xs_all, ys_all, zs_all = zip(*[p[:3] for p in poses])\n",
+    "            done_lines.append(ax3d.plot(xs_all, ys_all, zs_all, '-', lw=2, color='black', alpha=0.85)[0])\n",
+    "\n",
+    "        return [im, im_gt, current_line, current_arrow] + done_lines\n",
+    "\n",
+    "    anim = animation.FuncAnimation(\n",
+    "        fig, animate, init_func=init,\n",
+    "        frames=len(schedule),\n",
+    "        interval=max(1, 1000 // max(1, fps)),\n",
+    "        blit=False\n",
+    "    )\n",
+    "    return fig, anim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3c24d7d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# === Preview in notebook (interactive) ===\n",
+    "fig, anim = build_master_animation(ROOT, fps=FPS, pause_sec=PAUSE_SEC, overlay_text=True)\n",
+    "# HTML(anim.to_jshtml())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bf17875d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[OK] Master saved: /data1/tpz/nwm-main/results/nwm_cdit_airvln_16/airvln_16/CAND_N8_K3_RS1_rep3/editor/run_006/master.mp4\n"
+     ]
+    }
+   ],
+   "source": [
+    "anim.save(MASTER_OUT, writer='ffmpeg', fps=FPS)\n",
+    "plt.close(fig)\n",
+    "print(f\"[OK] Master saved: {MASTER_OUT}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nwm2",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/wuhan-union.log b/wuhan-union.log
new file mode 100644
index 0000000000000000000000000000000000000000..8d000eaf25861a095ac4ae92ddd001476a073397
--- /dev/null
+++ b/wuhan-union.log
@@ -0,0 +1,13652 @@
+[2025-05-14 12:34:04,952] torch.distributed.run: [WARNING] 
+[2025-05-14 12:34:04,952] torch.distributed.run: [WARNING] *****************************************
+[2025-05-14 12:34:04,952] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+[2025-05-14 12:34:04,952] torch.distributed.run: [WARNING] *****************************************
+[2025-05-14 12:34:07] Experiment directory created at logs/nwm_cdit_wuhan_union
+[2025-05-14 12:34:22] CDiT Parameters: 1,011,959,456
+[2025-05-14 12:34:22] Dataset contains 36,358 images
+[2025-05-14 12:34:22] Training for 300 epochs...
+[2025-05-14 12:34:22] Beginning epoch 0...
+[2025-05-14 12:35:10] (step=0000100) Train Loss: 0.3427, Train Steps/Sec: 2.10, Samples/Sec: 75.53
+[2025-05-14 12:35:57] (step=0000200) Train Loss: 0.2072, Train Steps/Sec: 2.14, Samples/Sec: 77.12
+[2025-05-14 12:36:43] (step=0000300) Train Loss: 0.2045, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 12:37:29] (step=0000400) Train Loss: 0.2018, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 12:38:16] (step=0000500) Train Loss: 0.1846, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 12:39:02] (step=0000600) Train Loss: 0.1824, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 12:39:48] (step=0000700) Train Loss: 0.1763, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 12:40:34] (step=0000800) Train Loss: 0.1673, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 12:41:21] (step=0000900) Train Loss: 0.1784, Train Steps/Sec: 2.15, Samples/Sec: 77.56
+[2025-05-14 12:42:07] (step=0001000) Train Loss: 0.1750, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 12:42:53] (step=0001100) Train Loss: 0.1650, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 12:43:39] (step=0001200) Train Loss: 0.1718, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 12:44:26] (step=0001300) Train Loss: 0.1730, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 12:45:12] (step=0001400) Train Loss: 0.1626, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 12:45:58] (step=0001500) Train Loss: 0.1620, Train Steps/Sec: 2.16, Samples/Sec: 77.60
+[2025-05-14 12:46:45] (step=0001600) Train Loss: 0.1671, Train Steps/Sec: 2.16, Samples/Sec: 77.74
+[2025-05-14 12:47:31] (step=0001700) Train Loss: 0.1643, Train Steps/Sec: 2.16, Samples/Sec: 77.71
+[2025-05-14 12:48:17] (step=0001800) Train Loss: 0.1509, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 12:49:03] (step=0001900) Train Loss: 0.1603, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 12:49:50] (step=0002000) Train Loss: 0.1626, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 12:50:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 12:50:51] (step=0002100) Train Loss: 0.1520, Train Steps/Sec: 1.63, Samples/Sec: 58.65
+[2025-05-14 12:51:37] (step=0002200) Train Loss: 0.1581, Train Steps/Sec: 2.16, Samples/Sec: 77.61
+[2025-05-14 12:52:24] (step=0002300) Train Loss: 0.1534, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 12:53:10] (step=0002400) Train Loss: 0.1581, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 12:53:56] (step=0002500) Train Loss: 0.1424, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 12:54:42] (step=0002600) Train Loss: 0.1608, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 12:55:29] (step=0002700) Train Loss: 0.1565, Train Steps/Sec: 2.16, Samples/Sec: 77.75
+[2025-05-14 12:56:15] (step=0002800) Train Loss: 0.1467, Train Steps/Sec: 2.16, Samples/Sec: 77.61
+[2025-05-14 12:57:01] (step=0002900) Train Loss: 0.1579, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 12:57:47] (step=0003000) Train Loss: 0.1436, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 12:58:34] (step=0003100) Train Loss: 0.1489, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 12:59:20] (step=0003200) Train Loss: 0.1441, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 13:00:06] (step=0003300) Train Loss: 0.1432, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 13:00:52] (step=0003400) Train Loss: 0.1384, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-14 13:01:39] (step=0003500) Train Loss: 0.1496, Train Steps/Sec: 2.16, Samples/Sec: 77.64
+[2025-05-14 13:02:25] (step=0003600) Train Loss: 0.1418, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 13:03:11] (step=0003700) Train Loss: 0.1472, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 13:03:58] (step=0003800) Train Loss: 0.1484, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 13:04:44] (step=0003900) Train Loss: 0.1498, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 13:05:30] (step=0004000) Train Loss: 0.1479, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-14 13:05:46] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 13:06:05] Beginning epoch 1...
+[2025-05-14 13:06:33] (step=0004100) Train Loss: 0.1400, Train Steps/Sec: 1.60, Samples/Sec: 57.52
+[2025-05-14 13:07:19] (step=0004200) Train Loss: 0.1442, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:08:05] (step=0004300) Train Loss: 0.1437, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 13:08:51] (step=0004400) Train Loss: 0.1468, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 13:09:38] (step=0004500) Train Loss: 0.1428, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 13:10:24] (step=0004600) Train Loss: 0.1442, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-14 13:11:10] (step=0004700) Train Loss: 0.1411, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:11:57] (step=0004800) Train Loss: 0.1492, Train Steps/Sec: 2.16, Samples/Sec: 77.60
+[2025-05-14 13:12:43] (step=0004900) Train Loss: 0.1428, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 13:13:29] (step=0005000) Train Loss: 0.1413, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 13:14:15] (step=0005100) Train Loss: 0.1436, Train Steps/Sec: 2.16, Samples/Sec: 77.63
+[2025-05-14 13:15:02] (step=0005200) Train Loss: 0.1338, Train Steps/Sec: 2.16, Samples/Sec: 77.64
+[2025-05-14 13:15:48] (step=0005300) Train Loss: 0.1350, Train Steps/Sec: 2.15, Samples/Sec: 77.55
+[2025-05-14 13:16:34] (step=0005400) Train Loss: 0.1411, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 13:17:21] (step=0005500) Train Loss: 0.1361, Train Steps/Sec: 2.16, Samples/Sec: 77.63
+[2025-05-14 13:18:07] (step=0005600) Train Loss: 0.1406, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 13:18:53] (step=0005700) Train Loss: 0.1425, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 13:19:40] (step=0005800) Train Loss: 0.1404, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:20:26] (step=0005900) Train Loss: 0.1337, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-14 13:21:12] (step=0006000) Train Loss: 0.1340, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 13:21:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 13:22:15] (step=0006100) Train Loss: 0.1385, Train Steps/Sec: 1.59, Samples/Sec: 57.24
+[2025-05-14 13:23:01] (step=0006200) Train Loss: 0.1401, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 13:23:47] (step=0006300) Train Loss: 0.1396, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 13:24:34] (step=0006400) Train Loss: 0.1286, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 13:25:20] (step=0006500) Train Loss: 0.1372, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-14 13:26:06] (step=0006600) Train Loss: 0.1385, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 13:26:52] (step=0006700) Train Loss: 0.1340, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 13:27:39] (step=0006800) Train Loss: 0.1383, Train Steps/Sec: 2.16, Samples/Sec: 77.62
+[2025-05-14 13:28:25] (step=0006900) Train Loss: 0.1339, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:29:11] (step=0007000) Train Loss: 0.1354, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 13:29:58] (step=0007100) Train Loss: 0.1383, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:30:44] (step=0007200) Train Loss: 0.1350, Train Steps/Sec: 2.16, Samples/Sec: 77.74
+[2025-05-14 13:31:30] (step=0007300) Train Loss: 0.1340, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 13:32:16] (step=0007400) Train Loss: 0.1353, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:33:03] (step=0007500) Train Loss: 0.1353, Train Steps/Sec: 2.16, Samples/Sec: 77.60
+[2025-05-14 13:33:49] (step=0007600) Train Loss: 0.1342, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 13:34:35] (step=0007700) Train Loss: 0.1370, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 13:35:22] (step=0007800) Train Loss: 0.1368, Train Steps/Sec: 2.16, Samples/Sec: 77.71
+[2025-05-14 13:36:08] (step=0007900) Train Loss: 0.1292, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 13:36:54] (step=0008000) Train Loss: 0.1328, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:37:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 13:37:47] Beginning epoch 2...
+[2025-05-14 13:37:56] (step=0008100) Train Loss: 0.1339, Train Steps/Sec: 1.61, Samples/Sec: 57.86
+[2025-05-14 13:38:43] (step=0008200) Train Loss: 0.1223, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:39:29] (step=0008300) Train Loss: 0.1353, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 13:40:15] (step=0008400) Train Loss: 0.1340, Train Steps/Sec: 2.16, Samples/Sec: 77.69
+[2025-05-14 13:41:01] (step=0008500) Train Loss: 0.1358, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 13:41:48] (step=0008600) Train Loss: 0.1331, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 13:42:34] (step=0008700) Train Loss: 0.1299, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 13:43:20] (step=0008800) Train Loss: 0.1281, Train Steps/Sec: 2.15, Samples/Sec: 77.57
+[2025-05-14 13:44:07] (step=0008900) Train Loss: 0.1356, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 13:44:53] (step=0009000) Train Loss: 0.1244, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 13:45:39] (step=0009100) Train Loss: 0.1252, Train Steps/Sec: 2.16, Samples/Sec: 77.74
+[2025-05-14 13:46:25] (step=0009200) Train Loss: 0.1301, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 13:47:12] (step=0009300) Train Loss: 0.1285, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 13:47:58] (step=0009400) Train Loss: 0.1309, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-05-14 13:48:44] (step=0009500) Train Loss: 0.1347, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 13:49:30] (step=0009600) Train Loss: 0.1330, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 13:50:17] (step=0009700) Train Loss: 0.1273, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-05-14 13:51:03] (step=0009800) Train Loss: 0.1284, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 13:51:49] (step=0009900) Train Loss: 0.1326, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 13:52:35] (step=0010000) Train Loss: 0.1233, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+[2025-05-14 13:52:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+[2025-05-14 13:52:55] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 13:54:54] (step=0010000) Perceptual Loss: 0.8820, Eval Time: 122.84
+[2025-05-14 13:55:41] (step=0010100) Train Loss: 0.1248, Train Steps/Sec: 0.54, Samples/Sec: 19.44
+[2025-05-14 13:56:27] (step=0010200) Train Loss: 0.1309, Train Steps/Sec: 2.16, Samples/Sec: 77.59
+[2025-05-14 13:57:13] (step=0010300) Train Loss: 0.1205, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 13:58:00] (step=0010400) Train Loss: 0.1292, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 13:58:46] (step=0010500) Train Loss: 0.1299, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 13:59:32] (step=0010600) Train Loss: 0.1302, Train Steps/Sec: 2.15, Samples/Sec: 77.48
+[2025-05-14 14:00:19] (step=0010700) Train Loss: 0.1327, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 14:01:05] (step=0010800) Train Loss: 0.1289, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 14:01:51] (step=0010900) Train Loss: 0.1165, Train Steps/Sec: 2.16, Samples/Sec: 77.69
+[2025-05-14 14:02:37] (step=0011000) Train Loss: 0.1256, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 14:03:24] (step=0011100) Train Loss: 0.1239, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 14:04:10] (step=0011200) Train Loss: 0.1208, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 14:04:56] (step=0011300) Train Loss: 0.1254, Train Steps/Sec: 2.15, Samples/Sec: 77.57
+[2025-05-14 14:05:43] (step=0011400) Train Loss: 0.1317, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 14:06:29] (step=0011500) Train Loss: 0.1287, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 14:07:15] (step=0011600) Train Loss: 0.1253, Train Steps/Sec: 2.16, Samples/Sec: 77.67
+[2025-05-14 14:08:02] (step=0011700) Train Loss: 0.1287, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 14:08:48] (step=0011800) Train Loss: 0.1245, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 14:09:34] (step=0011900) Train Loss: 0.1267, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 14:10:20] (step=0012000) Train Loss: 0.1243, Train Steps/Sec: 2.15, Samples/Sec: 77.53
+[2025-05-14 14:10:33] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 14:11:19] (step=0012100) Train Loss: 0.1251, Train Steps/Sec: 1.70, Samples/Sec: 61.28
+[2025-05-14 14:11:28] Beginning epoch 3...
+[2025-05-14 14:12:06] (step=0012200) Train Loss: 0.1240, Train Steps/Sec: 2.15, Samples/Sec: 77.32
+[2025-05-14 14:12:52] (step=0012300) Train Loss: 0.1238, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 14:13:38] (step=0012400) Train Loss: 0.1208, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 14:14:25] (step=0012500) Train Loss: 0.1275, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 14:15:11] (step=0012600) Train Loss: 0.1257, Train Steps/Sec: 2.16, Samples/Sec: 77.60
+[2025-05-14 14:15:57] (step=0012700) Train Loss: 0.1251, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 14:16:43] (step=0012800) Train Loss: 0.1255, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 14:17:30] (step=0012900) Train Loss: 0.1218, Train Steps/Sec: 2.16, Samples/Sec: 77.70
+[2025-05-14 14:18:16] (step=0013000) Train Loss: 0.1264, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 14:19:02] (step=0013100) Train Loss: 0.1276, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 14:19:49] (step=0013200) Train Loss: 0.1215, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 14:20:35] (step=0013300) Train Loss: 0.1324, Train Steps/Sec: 2.15, Samples/Sec: 77.54
+[2025-05-14 14:21:21] (step=0013400) Train Loss: 0.1228, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-05-14 14:22:08] (step=0013500) Train Loss: 0.1249, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-05-14 14:22:54] (step=0013600) Train Loss: 0.1204, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 14:23:40] (step=0013700) Train Loss: 0.1261, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 14:24:27] (step=0013800) Train Loss: 0.1170, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-14 14:25:13] (step=0013900) Train Loss: 0.1167, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 14:25:59] (step=0014000) Train Loss: 0.1284, Train Steps/Sec: 2.15, Samples/Sec: 77.48
+[2025-05-14 14:26:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 14:26:58] (step=0014100) Train Loss: 0.1274, Train Steps/Sec: 1.70, Samples/Sec: 61.10
+[2025-05-14 14:27:44] (step=0014200) Train Loss: 0.1178, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 14:28:31] (step=0014300) Train Loss: 0.1140, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-14 14:29:17] (step=0014400) Train Loss: 0.1182, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 14:30:03] (step=0014500) Train Loss: 0.1230, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-05-14 14:30:50] (step=0014600) Train Loss: 0.1259, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 14:31:36] (step=0014700) Train Loss: 0.1209, Train Steps/Sec: 2.15, Samples/Sec: 77.55
+[2025-05-14 14:32:22] (step=0014800) Train Loss: 0.1170, Train Steps/Sec: 2.16, Samples/Sec: 77.68
+[2025-05-14 14:33:09] (step=0014900) Train Loss: 0.1158, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 14:33:55] (step=0015000) Train Loss: 0.1231, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 14:34:41] (step=0015100) Train Loss: 0.1185, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 14:35:27] (step=0015200) Train Loss: 0.1242, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 14:36:14] (step=0015300) Train Loss: 0.1181, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 14:37:00] (step=0015400) Train Loss: 0.1218, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 14:37:46] (step=0015500) Train Loss: 0.1208, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 14:38:32] (step=0015600) Train Loss: 0.1147, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 14:39:19] (step=0015700) Train Loss: 0.1203, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 14:40:05] (step=0015800) Train Loss: 0.1229, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 14:40:51] (step=0015900) Train Loss: 0.1143, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 14:41:37] (step=0016000) Train Loss: 0.1186, Train Steps/Sec: 2.16, Samples/Sec: 77.65
+[2025-05-14 14:41:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 14:42:36] (step=0016100) Train Loss: 0.1253, Train Steps/Sec: 1.70, Samples/Sec: 61.07
+[2025-05-14 14:43:04] Beginning epoch 4...
+[2025-05-14 14:43:23] (step=0016200) Train Loss: 0.1169, Train Steps/Sec: 2.15, Samples/Sec: 77.42
+[2025-05-14 14:44:09] (step=0016300) Train Loss: 0.1176, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 14:44:55] (step=0016400) Train Loss: 0.1179, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-14 14:45:41] (step=0016500) Train Loss: 0.1244, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 14:46:28] (step=0016600) Train Loss: 0.1213, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 14:47:14] (step=0016700) Train Loss: 0.1149, Train Steps/Sec: 2.16, Samples/Sec: 77.62
+[2025-05-14 14:48:00] (step=0016800) Train Loss: 0.1120, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 14:48:46] (step=0016900) Train Loss: 0.1167, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-14 14:49:33] (step=0017000) Train Loss: 0.1186, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 14:50:19] (step=0017100) Train Loss: 0.1191, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-14 14:51:05] (step=0017200) Train Loss: 0.1150, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 14:51:51] (step=0017300) Train Loss: 0.1184, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 14:52:38] (step=0017400) Train Loss: 0.1157, Train Steps/Sec: 2.15, Samples/Sec: 77.50
+[2025-05-14 14:53:24] (step=0017500) Train Loss: 0.1133, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 14:54:10] (step=0017600) Train Loss: 0.1277, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 14:54:56] (step=0017700) Train Loss: 0.1219, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 14:55:43] (step=0017800) Train Loss: 0.1189, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 14:56:29] (step=0017900) Train Loss: 0.1207, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 14:57:15] (step=0018000) Train Loss: 0.1207, Train Steps/Sec: 2.16, Samples/Sec: 77.74
+[2025-05-14 14:57:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 14:58:14] (step=0018100) Train Loss: 0.1172, Train Steps/Sec: 1.69, Samples/Sec: 60.84
+[2025-05-14 14:59:01] (step=0018200) Train Loss: 0.1125, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 14:59:47] (step=0018300) Train Loss: 0.1139, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 15:00:33] (step=0018400) Train Loss: 0.1216, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 15:01:19] (step=0018500) Train Loss: 0.1193, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 15:02:05] (step=0018600) Train Loss: 0.1108, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 15:02:52] (step=0018700) Train Loss: 0.1092, Train Steps/Sec: 2.15, Samples/Sec: 77.53
+[2025-05-14 15:03:38] (step=0018800) Train Loss: 0.1130, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 15:04:24] (step=0018900) Train Loss: 0.1126, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 15:05:10] (step=0019000) Train Loss: 0.1126, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 15:05:57] (step=0019100) Train Loss: 0.1139, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 15:06:43] (step=0019200) Train Loss: 0.1170, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 15:07:29] (step=0019300) Train Loss: 0.1161, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 15:08:15] (step=0019400) Train Loss: 0.1170, Train Steps/Sec: 2.16, Samples/Sec: 77.64
+[2025-05-14 15:09:02] (step=0019500) Train Loss: 0.1185, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 15:09:48] (step=0019600) Train Loss: 0.1154, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 15:10:34] (step=0019700) Train Loss: 0.1196, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 15:11:20] (step=0019800) Train Loss: 0.1157, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 15:12:07] (step=0019900) Train Loss: 0.1087, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 15:12:53] (step=0020000) Train Loss: 0.1143, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 15:13:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0020000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 15:13:20] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 15:15:20] (step=0020000) Perceptual Loss: 0.6671, Eval Time: 122.69
+[2025-05-14 15:16:06] (step=0020100) Train Loss: 0.1163, Train Steps/Sec: 0.52, Samples/Sec: 18.60
+[2025-05-14 15:16:53] (step=0020200) Train Loss: 0.1144, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 15:16:53] Beginning epoch 5...
+[2025-05-14 15:17:39] (step=0020300) Train Loss: 0.1161, Train Steps/Sec: 2.15, Samples/Sec: 77.38
+[2025-05-14 15:18:25] (step=0020400) Train Loss: 0.1098, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 15:19:11] (step=0020500) Train Loss: 0.1142, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 15:19:58] (step=0020600) Train Loss: 0.1172, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 15:20:44] (step=0020700) Train Loss: 0.1095, Train Steps/Sec: 2.15, Samples/Sec: 77.57
+[2025-05-14 15:21:30] (step=0020800) Train Loss: 0.1124, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 15:22:17] (step=0020900) Train Loss: 0.1179, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 15:23:03] (step=0021000) Train Loss: 0.1106, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 15:23:49] (step=0021100) Train Loss: 0.1218, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 15:24:35] (step=0021200) Train Loss: 0.1178, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 15:25:21] (step=0021300) Train Loss: 0.1185, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 15:26:08] (step=0021400) Train Loss: 0.1159, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 15:26:54] (step=0021500) Train Loss: 0.1106, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 15:27:40] (step=0021600) Train Loss: 0.1171, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 15:28:26] (step=0021700) Train Loss: 0.1092, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 15:29:12] (step=0021800) Train Loss: 0.1152, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 15:29:58] (step=0021900) Train Loss: 0.1201, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 15:30:44] (step=0022000) Train Loss: 0.1135, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 15:30:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 15:31:44] (step=0022100) Train Loss: 0.1120, Train Steps/Sec: 1.69, Samples/Sec: 60.79
+[2025-05-14 15:32:30] (step=0022200) Train Loss: 0.1095, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 15:33:16] (step=0022300) Train Loss: 0.1092, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 15:34:02] (step=0022400) Train Loss: 0.1039, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 15:34:48] (step=0022500) Train Loss: 0.1141, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-14 15:35:34] (step=0022600) Train Loss: 0.1131, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 15:36:21] (step=0022700) Train Loss: 0.1181, Train Steps/Sec: 2.15, Samples/Sec: 77.36
+[2025-05-14 15:37:07] (step=0022800) Train Loss: 0.1105, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 15:37:53] (step=0022900) Train Loss: 0.1134, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 15:38:40] (step=0023000) Train Loss: 0.1068, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 15:39:26] (step=0023100) Train Loss: 0.1163, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 15:40:12] (step=0023200) Train Loss: 0.1096, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 15:40:58] (step=0023300) Train Loss: 0.1089, Train Steps/Sec: 2.16, Samples/Sec: 77.69
+[2025-05-14 15:41:45] (step=0023400) Train Loss: 0.1099, Train Steps/Sec: 2.15, Samples/Sec: 77.51
+[2025-05-14 15:42:31] (step=0023500) Train Loss: 0.1050, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 15:43:17] (step=0023600) Train Loss: 0.1026, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 15:44:04] (step=0023700) Train Loss: 0.1031, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 15:44:50] (step=0023800) Train Loss: 0.1063, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 15:45:36] (step=0023900) Train Loss: 0.1143, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 15:46:23] (step=0024000) Train Loss: 0.1091, Train Steps/Sec: 2.15, Samples/Sec: 77.55
+[2025-05-14 15:46:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 15:47:22] (step=0024100) Train Loss: 0.1090, Train Steps/Sec: 1.68, Samples/Sec: 60.60
+[2025-05-14 15:48:08] (step=0024200) Train Loss: 0.1081, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 15:48:27] Beginning epoch 6...
+[2025-05-14 15:48:55] (step=0024300) Train Loss: 0.1073, Train Steps/Sec: 2.15, Samples/Sec: 77.33
+[2025-05-14 15:49:41] (step=0024400) Train Loss: 0.1133, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 15:50:27] (step=0024500) Train Loss: 0.1161, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 15:51:14] (step=0024600) Train Loss: 0.1108, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 15:52:00] (step=0024700) Train Loss: 0.1096, Train Steps/Sec: 2.15, Samples/Sec: 77.44
+[2025-05-14 15:52:46] (step=0024800) Train Loss: 0.1126, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 15:53:33] (step=0024900) Train Loss: 0.1171, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 15:54:19] (step=0025000) Train Loss: 0.1127, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 15:55:05] (step=0025100) Train Loss: 0.1121, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 15:55:51] (step=0025200) Train Loss: 0.1085, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 15:56:38] (step=0025300) Train Loss: 0.1095, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 15:57:24] (step=0025400) Train Loss: 0.1072, Train Steps/Sec: 2.15, Samples/Sec: 77.40
+[2025-05-14 15:58:10] (step=0025500) Train Loss: 0.1114, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 15:58:57] (step=0025600) Train Loss: 0.1087, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 15:59:43] (step=0025700) Train Loss: 0.1099, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 16:00:29] (step=0025800) Train Loss: 0.1160, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 16:01:15] (step=0025900) Train Loss: 0.1129, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 16:02:02] (step=0026000) Train Loss: 0.1158, Train Steps/Sec: 2.15, Samples/Sec: 77.58
+[2025-05-14 16:02:15] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 16:03:01] (step=0026100) Train Loss: 0.1103, Train Steps/Sec: 1.69, Samples/Sec: 60.90
+[2025-05-14 16:03:47] (step=0026200) Train Loss: 0.1064, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 16:04:34] (step=0026300) Train Loss: 0.1089, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 16:05:20] (step=0026400) Train Loss: 0.1098, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 16:06:06] (step=0026500) Train Loss: 0.1094, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 16:06:52] (step=0026600) Train Loss: 0.1083, Train Steps/Sec: 2.16, Samples/Sec: 77.65
+[2025-05-14 16:07:39] (step=0026700) Train Loss: 0.1059, Train Steps/Sec: 2.15, Samples/Sec: 77.54
+[2025-05-14 16:08:25] (step=0026800) Train Loss: 0.1081, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 16:09:11] (step=0026900) Train Loss: 0.1051, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 16:09:57] (step=0027000) Train Loss: 0.1033, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 16:10:43] (step=0027100) Train Loss: 0.1076, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 16:11:29] (step=0027200) Train Loss: 0.1063, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 16:12:16] (step=0027300) Train Loss: 0.1174, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-14 16:13:02] (step=0027400) Train Loss: 0.0960, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 16:13:48] (step=0027500) Train Loss: 0.1100, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 16:14:34] (step=0027600) Train Loss: 0.1071, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 16:15:20] (step=0027700) Train Loss: 0.1081, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 16:16:06] (step=0027800) Train Loss: 0.1060, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 16:16:52] (step=0027900) Train Loss: 0.1169, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 16:17:39] (step=0028000) Train Loss: 0.1146, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 16:17:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 16:18:38] (step=0028100) Train Loss: 0.1106, Train Steps/Sec: 1.69, Samples/Sec: 60.83
+[2025-05-14 16:19:24] (step=0028200) Train Loss: 0.1074, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 16:20:01] Beginning epoch 7...
+[2025-05-14 16:20:10] (step=0028300) Train Loss: 0.1084, Train Steps/Sec: 2.16, Samples/Sec: 77.67
+[2025-05-14 16:20:56] (step=0028400) Train Loss: 0.1039, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 16:21:42] (step=0028500) Train Loss: 0.1086, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 16:22:29] (step=0028600) Train Loss: 0.1090, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 16:23:15] (step=0028700) Train Loss: 0.1080, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 16:24:01] (step=0028800) Train Loss: 0.1017, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-14 16:24:47] (step=0028900) Train Loss: 0.1077, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 16:25:33] (step=0029000) Train Loss: 0.1078, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 16:26:19] (step=0029100) Train Loss: 0.0998, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 16:27:05] (step=0029200) Train Loss: 0.1063, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-14 16:27:51] (step=0029300) Train Loss: 0.1014, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 16:28:38] (step=0029400) Train Loss: 0.1072, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 16:29:24] (step=0029500) Train Loss: 0.1046, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 16:30:10] (step=0029600) Train Loss: 0.1106, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 16:30:56] (step=0029700) Train Loss: 0.1072, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-14 16:31:42] (step=0029800) Train Loss: 0.1064, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-14 16:32:29] (step=0029900) Train Loss: 0.1019, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 16:33:15] (step=0030000) Train Loss: 0.1104, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 16:33:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 16:33:30] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 16:35:29] (step=0030000) Perceptual Loss: 0.4259, Eval Time: 122.55
+[2025-05-14 16:36:16] (step=0030100) Train Loss: 0.1071, Train Steps/Sec: 0.55, Samples/Sec: 19.91
+[2025-05-14 16:37:02] (step=0030200) Train Loss: 0.1029, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 16:37:48] (step=0030300) Train Loss: 0.1020, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 16:38:34] (step=0030400) Train Loss: 0.1077, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 16:39:20] (step=0030500) Train Loss: 0.1094, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 16:40:06] (step=0030600) Train Loss: 0.1120, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 16:40:53] (step=0030700) Train Loss: 0.1096, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-14 16:41:39] (step=0030800) Train Loss: 0.1080, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 16:42:25] (step=0030900) Train Loss: 0.1015, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 16:43:11] (step=0031000) Train Loss: 0.0996, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 16:43:57] (step=0031100) Train Loss: 0.1103, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 16:44:43] (step=0031200) Train Loss: 0.1055, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 16:45:29] (step=0031300) Train Loss: 0.1104, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 16:46:16] (step=0031400) Train Loss: 0.1021, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 16:47:02] (step=0031500) Train Loss: 0.0980, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 16:47:48] (step=0031600) Train Loss: 0.1080, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 16:48:34] (step=0031700) Train Loss: 0.1083, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 16:49:20] (step=0031800) Train Loss: 0.1063, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 16:50:06] (step=0031900) Train Loss: 0.1056, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 16:50:52] (step=0032000) Train Loss: 0.1022, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 16:51:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 16:51:51] (step=0032100) Train Loss: 0.1018, Train Steps/Sec: 1.71, Samples/Sec: 61.52
+[2025-05-14 16:52:37] (step=0032200) Train Loss: 0.1043, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-14 16:53:23] (step=0032300) Train Loss: 0.1041, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 16:53:32] Beginning epoch 8...
+[2025-05-14 16:54:10] (step=0032400) Train Loss: 0.1108, Train Steps/Sec: 2.15, Samples/Sec: 77.57
+[2025-05-14 16:54:56] (step=0032500) Train Loss: 0.1098, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 16:55:42] (step=0032600) Train Loss: 0.1014, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 16:56:28] (step=0032700) Train Loss: 0.1051, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 16:57:15] (step=0032800) Train Loss: 0.1055, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 16:58:01] (step=0032900) Train Loss: 0.1079, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 16:58:47] (step=0033000) Train Loss: 0.1029, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 16:59:33] (step=0033100) Train Loss: 0.1135, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:00:19] (step=0033200) Train Loss: 0.1125, Train Steps/Sec: 2.17, Samples/Sec: 77.94
+[2025-05-14 17:01:05] (step=0033300) Train Loss: 0.1124, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:01:51] (step=0033400) Train Loss: 0.1017, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 17:02:38] (step=0033500) Train Loss: 0.0986, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 17:03:24] (step=0033600) Train Loss: 0.1123, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 17:04:10] (step=0033700) Train Loss: 0.1069, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:04:56] (step=0033800) Train Loss: 0.0963, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 17:05:42] (step=0033900) Train Loss: 0.1070, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 17:06:28] (step=0034000) Train Loss: 0.1080, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:06:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 17:07:26] (step=0034100) Train Loss: 0.1054, Train Steps/Sec: 1.72, Samples/Sec: 61.87
+[2025-05-14 17:08:12] (step=0034200) Train Loss: 0.0999, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 17:08:58] (step=0034300) Train Loss: 0.1027, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 17:09:45] (step=0034400) Train Loss: 0.1036, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 17:10:31] (step=0034500) Train Loss: 0.1041, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 17:11:17] (step=0034600) Train Loss: 0.1030, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 17:12:03] (step=0034700) Train Loss: 0.1030, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:12:49] (step=0034800) Train Loss: 0.1057, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 17:13:35] (step=0034900) Train Loss: 0.1083, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 17:14:21] (step=0035000) Train Loss: 0.1056, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 17:15:08] (step=0035100) Train Loss: 0.1077, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-14 17:15:54] (step=0035200) Train Loss: 0.1054, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 17:16:40] (step=0035300) Train Loss: 0.1058, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 17:17:26] (step=0035400) Train Loss: 0.1011, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 17:18:12] (step=0035500) Train Loss: 0.1094, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 17:18:58] (step=0035600) Train Loss: 0.1042, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 17:19:44] (step=0035700) Train Loss: 0.0960, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 17:20:31] (step=0035800) Train Loss: 0.1061, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 17:21:17] (step=0035900) Train Loss: 0.1082, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 17:22:03] (step=0036000) Train Loss: 0.1053, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 17:22:15] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 17:23:02] (step=0036100) Train Loss: 0.1104, Train Steps/Sec: 1.70, Samples/Sec: 61.20
+[2025-05-14 17:23:48] (step=0036200) Train Loss: 0.1061, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 17:24:34] (step=0036300) Train Loss: 0.1095, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 17:25:01] Beginning epoch 9...
+[2025-05-14 17:25:20] (step=0036400) Train Loss: 0.1038, Train Steps/Sec: 2.15, Samples/Sec: 77.47
+[2025-05-14 17:26:06] (step=0036500) Train Loss: 0.0995, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 17:26:52] (step=0036600) Train Loss: 0.1071, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 17:27:39] (step=0036700) Train Loss: 0.1049, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:28:25] (step=0036800) Train Loss: 0.1014, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 17:29:11] (step=0036900) Train Loss: 0.1085, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 17:29:57] (step=0037000) Train Loss: 0.0986, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-14 17:30:43] (step=0037100) Train Loss: 0.0993, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:31:29] (step=0037200) Train Loss: 0.1045, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 17:32:15] (step=0037300) Train Loss: 0.1035, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:33:02] (step=0037400) Train Loss: 0.1069, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 17:33:48] (step=0037500) Train Loss: 0.0948, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 17:34:34] (step=0037600) Train Loss: 0.1068, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 17:35:20] (step=0037700) Train Loss: 0.1035, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 17:36:06] (step=0037800) Train Loss: 0.1050, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 17:36:52] (step=0037900) Train Loss: 0.1071, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 17:37:38] (step=0038000) Train Loss: 0.0975, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:37:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 17:38:37] (step=0038100) Train Loss: 0.1064, Train Steps/Sec: 1.70, Samples/Sec: 61.27
+[2025-05-14 17:39:23] (step=0038200) Train Loss: 0.0987, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-14 17:40:10] (step=0038300) Train Loss: 0.1078, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 17:40:56] (step=0038400) Train Loss: 0.1031, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 17:41:42] (step=0038500) Train Loss: 0.1045, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 17:42:28] (step=0038600) Train Loss: 0.1056, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-14 17:43:14] (step=0038700) Train Loss: 0.1035, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 17:44:01] (step=0038800) Train Loss: 0.1051, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-05-14 17:44:47] (step=0038900) Train Loss: 0.0994, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-14 17:45:33] (step=0039000) Train Loss: 0.1085, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 17:46:19] (step=0039100) Train Loss: 0.0987, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 17:47:05] (step=0039200) Train Loss: 0.0954, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-14 17:47:51] (step=0039300) Train Loss: 0.1013, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 17:48:37] (step=0039400) Train Loss: 0.0987, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 17:49:24] (step=0039500) Train Loss: 0.1005, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 17:50:10] (step=0039600) Train Loss: 0.0986, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 17:50:56] (step=0039700) Train Loss: 0.1009, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 17:51:42] (step=0039800) Train Loss: 0.1069, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 17:52:28] (step=0039900) Train Loss: 0.1039, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:53:14] (step=0040000) Train Loss: 0.1047, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 17:53:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0040000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 17:53:41] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 17:55:40] (step=0040000) Perceptual Loss: 0.3221, Eval Time: 122.39
+[2025-05-14 17:56:27] (step=0040100) Train Loss: 0.1042, Train Steps/Sec: 0.52, Samples/Sec: 18.74
+[2025-05-14 17:57:13] (step=0040200) Train Loss: 0.0988, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 17:57:59] (step=0040300) Train Loss: 0.1007, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 17:58:45] (step=0040400) Train Loss: 0.0979, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 17:58:45] Beginning epoch 10...
+[2025-05-14 17:59:32] (step=0040500) Train Loss: 0.1011, Train Steps/Sec: 2.15, Samples/Sec: 77.27
+[2025-05-14 18:00:18] (step=0040600) Train Loss: 0.1027, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 18:01:04] (step=0040700) Train Loss: 0.0977, Train Steps/Sec: 2.16, Samples/Sec: 77.67
+[2025-05-14 18:01:51] (step=0040800) Train Loss: 0.0964, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 18:02:37] (step=0040900) Train Loss: 0.0997, Train Steps/Sec: 2.16, Samples/Sec: 77.65
+[2025-05-14 18:03:23] (step=0041000) Train Loss: 0.1065, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 18:04:09] (step=0041100) Train Loss: 0.1001, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-14 18:04:56] (step=0041200) Train Loss: 0.1072, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 18:05:42] (step=0041300) Train Loss: 0.1026, Train Steps/Sec: 2.16, Samples/Sec: 77.75
+[2025-05-14 18:06:28] (step=0041400) Train Loss: 0.0972, Train Steps/Sec: 2.15, Samples/Sec: 77.54
+[2025-05-14 18:07:15] (step=0041500) Train Loss: 0.1024, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 18:08:01] (step=0041600) Train Loss: 0.1016, Train Steps/Sec: 2.16, Samples/Sec: 77.65
+[2025-05-14 18:08:47] (step=0041700) Train Loss: 0.1058, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 18:09:34] (step=0041800) Train Loss: 0.0977, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 18:10:20] (step=0041900) Train Loss: 0.0980, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 18:11:06] (step=0042000) Train Loss: 0.0975, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-14 18:11:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 18:12:05] (step=0042100) Train Loss: 0.1007, Train Steps/Sec: 1.70, Samples/Sec: 61.11
+[2025-05-14 18:12:51] (step=0042200) Train Loss: 0.1020, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 18:13:38] (step=0042300) Train Loss: 0.1034, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 18:14:24] (step=0042400) Train Loss: 0.0971, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 18:15:10] (step=0042500) Train Loss: 0.1044, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 18:15:56] (step=0042600) Train Loss: 0.1005, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 18:16:43] (step=0042700) Train Loss: 0.0996, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 18:17:29] (step=0042800) Train Loss: 0.0958, Train Steps/Sec: 2.15, Samples/Sec: 77.42
+[2025-05-14 18:18:15] (step=0042900) Train Loss: 0.0964, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 18:19:02] (step=0043000) Train Loss: 0.0944, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 18:19:48] (step=0043100) Train Loss: 0.1032, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 18:20:34] (step=0043200) Train Loss: 0.0999, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-14 18:21:21] (step=0043300) Train Loss: 0.0947, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-05-14 18:22:07] (step=0043400) Train Loss: 0.0969, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-05-14 18:22:53] (step=0043500) Train Loss: 0.1005, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-05-14 18:23:40] (step=0043600) Train Loss: 0.0980, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 18:24:26] (step=0043700) Train Loss: 0.0984, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-14 18:25:12] (step=0043800) Train Loss: 0.0974, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 18:25:58] (step=0043900) Train Loss: 0.0940, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 18:26:45] (step=0044000) Train Loss: 0.1057, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 18:26:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 18:27:44] (step=0044100) Train Loss: 0.1052, Train Steps/Sec: 1.67, Samples/Sec: 60.25
+[2025-05-14 18:28:31] (step=0044200) Train Loss: 0.0930, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 18:29:17] (step=0044300) Train Loss: 0.0988, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 18:30:03] (step=0044400) Train Loss: 0.1033, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 18:30:22] Beginning epoch 11...
+[2025-05-14 18:30:50] (step=0044500) Train Loss: 0.1032, Train Steps/Sec: 2.15, Samples/Sec: 77.24
+[2025-05-14 18:31:36] (step=0044600) Train Loss: 0.0907, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-14 18:32:23] (step=0044700) Train Loss: 0.1002, Train Steps/Sec: 2.16, Samples/Sec: 77.71
+[2025-05-14 18:33:09] (step=0044800) Train Loss: 0.1058, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 18:33:55] (step=0044900) Train Loss: 0.1017, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 18:34:41] (step=0045000) Train Loss: 0.0986, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 18:35:27] (step=0045100) Train Loss: 0.1023, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 18:36:13] (step=0045200) Train Loss: 0.1012, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 18:36:59] (step=0045300) Train Loss: 0.1005, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 18:37:46] (step=0045400) Train Loss: 0.1000, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 18:38:32] (step=0045500) Train Loss: 0.1024, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 18:39:18] (step=0045600) Train Loss: 0.0960, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 18:40:04] (step=0045700) Train Loss: 0.0889, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 18:40:50] (step=0045800) Train Loss: 0.0967, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 18:41:36] (step=0045900) Train Loss: 0.0970, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 18:42:22] (step=0046000) Train Loss: 0.1047, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 18:42:35] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 18:43:21] (step=0046100) Train Loss: 0.1010, Train Steps/Sec: 1.71, Samples/Sec: 61.70
+[2025-05-14 18:44:07] (step=0046200) Train Loss: 0.0978, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:44:53] (step=0046300) Train Loss: 0.0967, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 18:45:39] (step=0046400) Train Loss: 0.1006, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:46:25] (step=0046500) Train Loss: 0.1024, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:47:11] (step=0046600) Train Loss: 0.0972, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:47:57] (step=0046700) Train Loss: 0.0981, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-14 18:48:44] (step=0046800) Train Loss: 0.0964, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-14 18:49:30] (step=0046900) Train Loss: 0.1016, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 18:50:16] (step=0047000) Train Loss: 0.0967, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 18:51:02] (step=0047100) Train Loss: 0.0971, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:51:48] (step=0047200) Train Loss: 0.1006, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:52:34] (step=0047300) Train Loss: 0.1004, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 18:53:20] (step=0047400) Train Loss: 0.0986, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 18:54:06] (step=0047500) Train Loss: 0.0909, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 18:54:53] (step=0047600) Train Loss: 0.0985, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:55:39] (step=0047700) Train Loss: 0.0985, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:56:25] (step=0047800) Train Loss: 0.1031, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 18:57:11] (step=0047900) Train Loss: 0.0993, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 18:57:57] (step=0048000) Train Loss: 0.0928, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 18:58:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 18:58:55] (step=0048100) Train Loss: 0.0968, Train Steps/Sec: 1.72, Samples/Sec: 62.04
+[2025-05-14 18:59:41] (step=0048200) Train Loss: 0.0957, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 19:00:27] (step=0048300) Train Loss: 0.0995, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 19:01:13] (step=0048400) Train Loss: 0.0992, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 19:01:50] Beginning epoch 12...
+[2025-05-14 19:02:00] (step=0048500) Train Loss: 0.0986, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-05-14 19:02:46] (step=0048600) Train Loss: 0.1011, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 19:03:32] (step=0048700) Train Loss: 0.0993, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 19:04:18] (step=0048800) Train Loss: 0.0986, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 19:05:04] (step=0048900) Train Loss: 0.1002, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 19:05:50] (step=0049000) Train Loss: 0.0968, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 19:06:36] (step=0049100) Train Loss: 0.1007, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 19:07:22] (step=0049200) Train Loss: 0.0955, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 19:08:09] (step=0049300) Train Loss: 0.1003, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:08:55] (step=0049400) Train Loss: 0.0960, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 19:09:41] (step=0049500) Train Loss: 0.1017, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 19:10:27] (step=0049600) Train Loss: 0.0954, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 19:11:13] (step=0049700) Train Loss: 0.0899, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 19:11:59] (step=0049800) Train Loss: 0.0921, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 19:12:45] (step=0049900) Train Loss: 0.1008, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 19:13:31] (step=0050000) Train Loss: 0.0986, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 19:13:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 19:13:46] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 19:15:46] (step=0050000) Perceptual Loss: 0.2950, Eval Time: 122.30
+[2025-05-14 19:16:32] (step=0050100) Train Loss: 0.0956, Train Steps/Sec: 0.55, Samples/Sec: 19.88
+[2025-05-14 19:17:19] (step=0050200) Train Loss: 0.0972, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 19:18:05] (step=0050300) Train Loss: 0.0952, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 19:18:51] (step=0050400) Train Loss: 0.0955, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 19:19:37] (step=0050500) Train Loss: 0.0997, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-14 19:20:23] (step=0050600) Train Loss: 0.0963, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 19:21:09] (step=0050700) Train Loss: 0.0953, Train Steps/Sec: 2.16, Samples/Sec: 77.59
+[2025-05-14 19:21:56] (step=0050800) Train Loss: 0.1005, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 19:22:42] (step=0050900) Train Loss: 0.0985, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-14 19:23:28] (step=0051000) Train Loss: 0.0970, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 19:24:14] (step=0051100) Train Loss: 0.0972, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-14 19:25:00] (step=0051200) Train Loss: 0.1009, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 19:25:46] (step=0051300) Train Loss: 0.0942, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 19:26:33] (step=0051400) Train Loss: 0.0956, Train Steps/Sec: 2.16, Samples/Sec: 77.69
+[2025-05-14 19:27:19] (step=0051500) Train Loss: 0.0995, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 19:28:05] (step=0051600) Train Loss: 0.0978, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 19:28:51] (step=0051700) Train Loss: 0.0954, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 19:29:37] (step=0051800) Train Loss: 0.0983, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 19:30:23] (step=0051900) Train Loss: 0.0968, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:31:09] (step=0052000) Train Loss: 0.1009, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 19:31:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 19:32:09] (step=0052100) Train Loss: 0.0989, Train Steps/Sec: 1.68, Samples/Sec: 60.51
+[2025-05-14 19:32:55] (step=0052200) Train Loss: 0.0977, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:33:41] (step=0052300) Train Loss: 0.0910, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:34:27] (step=0052400) Train Loss: 0.0981, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:35:13] (step=0052500) Train Loss: 0.0956, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:35:22] Beginning epoch 13...
+[2025-05-14 19:35:59] (step=0052600) Train Loss: 0.0988, Train Steps/Sec: 2.16, Samples/Sec: 77.68
+[2025-05-14 19:36:46] (step=0052700) Train Loss: 0.1002, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 19:37:32] (step=0052800) Train Loss: 0.1006, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 19:38:18] (step=0052900) Train Loss: 0.0967, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:39:04] (step=0053000) Train Loss: 0.1015, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:39:50] (step=0053100) Train Loss: 0.0947, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:40:36] (step=0053200) Train Loss: 0.0997, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 19:41:22] (step=0053300) Train Loss: 0.0950, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 19:42:09] (step=0053400) Train Loss: 0.0976, Train Steps/Sec: 2.16, Samples/Sec: 77.74
+[2025-05-14 19:42:55] (step=0053500) Train Loss: 0.1003, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 19:43:41] (step=0053600) Train Loss: 0.1002, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 19:44:27] (step=0053700) Train Loss: 0.0942, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 19:45:13] (step=0053800) Train Loss: 0.0997, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 19:45:59] (step=0053900) Train Loss: 0.0898, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-14 19:46:45] (step=0054000) Train Loss: 0.0921, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-14 19:46:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 19:47:43] (step=0054100) Train Loss: 0.0926, Train Steps/Sec: 1.71, Samples/Sec: 61.70
+[2025-05-14 19:48:30] (step=0054200) Train Loss: 0.0923, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 19:49:16] (step=0054300) Train Loss: 0.0981, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 19:50:02] (step=0054400) Train Loss: 0.0948, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 19:50:48] (step=0054500) Train Loss: 0.0866, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 19:51:34] (step=0054600) Train Loss: 0.0972, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 19:52:20] (step=0054700) Train Loss: 0.0985, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:53:06] (step=0054800) Train Loss: 0.0936, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 19:53:53] (step=0054900) Train Loss: 0.0949, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 19:54:39] (step=0055000) Train Loss: 0.0961, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 19:55:25] (step=0055100) Train Loss: 0.0938, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 19:56:11] (step=0055200) Train Loss: 0.0939, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 19:56:57] (step=0055300) Train Loss: 0.0954, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-14 19:57:43] (step=0055400) Train Loss: 0.0935, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 19:58:29] (step=0055500) Train Loss: 0.0950, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 19:59:15] (step=0055600) Train Loss: 0.0952, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 20:00:02] (step=0055700) Train Loss: 0.0979, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 20:00:48] (step=0055800) Train Loss: 0.0961, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:01:34] (step=0055900) Train Loss: 0.0915, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 20:02:20] (step=0056000) Train Loss: 0.0924, Train Steps/Sec: 2.17, Samples/Sec: 77.94
+[2025-05-14 20:02:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 20:03:18] (step=0056100) Train Loss: 0.0984, Train Steps/Sec: 1.71, Samples/Sec: 61.66
+[2025-05-14 20:04:04] (step=0056200) Train Loss: 0.0923, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 20:04:51] (step=0056300) Train Loss: 0.0935, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 20:05:37] (step=0056400) Train Loss: 0.0960, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 20:06:23] (step=0056500) Train Loss: 0.0962, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:06:50] Beginning epoch 14...
+[2025-05-14 20:07:09] (step=0056600) Train Loss: 0.0938, Train Steps/Sec: 2.15, Samples/Sec: 77.55
+[2025-05-14 20:07:55] (step=0056700) Train Loss: 0.0945, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 20:08:42] (step=0056800) Train Loss: 0.0954, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 20:09:28] (step=0056900) Train Loss: 0.0961, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 20:10:14] (step=0057000) Train Loss: 0.0941, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 20:11:00] (step=0057100) Train Loss: 0.0912, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 20:11:46] (step=0057200) Train Loss: 0.0883, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:12:32] (step=0057300) Train Loss: 0.0969, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 20:13:18] (step=0057400) Train Loss: 0.0952, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 20:14:05] (step=0057500) Train Loss: 0.0916, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 20:14:51] (step=0057600) Train Loss: 0.0956, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 20:15:37] (step=0057700) Train Loss: 0.0967, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:16:23] (step=0057800) Train Loss: 0.0970, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:17:09] (step=0057900) Train Loss: 0.0958, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 20:17:55] (step=0058000) Train Loss: 0.0927, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 20:18:07] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 20:18:53] (step=0058100) Train Loss: 0.0958, Train Steps/Sec: 1.72, Samples/Sec: 61.86
+[2025-05-14 20:19:40] (step=0058200) Train Loss: 0.0984, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 20:20:26] (step=0058300) Train Loss: 0.0919, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 20:21:12] (step=0058400) Train Loss: 0.0902, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:21:58] (step=0058500) Train Loss: 0.0966, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 20:22:44] (step=0058600) Train Loss: 0.0936, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 20:23:30] (step=0058700) Train Loss: 0.0952, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:24:16] (step=0058800) Train Loss: 0.0932, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 20:25:02] (step=0058900) Train Loss: 0.0909, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:25:49] (step=0059000) Train Loss: 0.0896, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 20:26:35] (step=0059100) Train Loss: 0.0941, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 20:27:21] (step=0059200) Train Loss: 0.0945, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 20:28:07] (step=0059300) Train Loss: 0.0921, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 20:28:53] (step=0059400) Train Loss: 0.0932, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 20:29:39] (step=0059500) Train Loss: 0.0973, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 20:30:25] (step=0059600) Train Loss: 0.0949, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 20:31:11] (step=0059700) Train Loss: 0.0961, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 20:31:57] (step=0059800) Train Loss: 0.0933, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 20:32:44] (step=0059900) Train Loss: 0.0912, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 20:33:30] (step=0060000) Train Loss: 0.0925, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 20:33:53] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0060000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 20:33:56] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 20:35:56] (step=0060000) Perceptual Loss: 0.2907, Eval Time: 122.51
+[2025-05-14 20:36:42] (step=0060100) Train Loss: 0.0910, Train Steps/Sec: 0.52, Samples/Sec: 18.69
+[2025-05-14 20:37:28] (step=0060200) Train Loss: 0.0912, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 20:38:15] (step=0060300) Train Loss: 0.0950, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 20:39:01] (step=0060400) Train Loss: 0.0908, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 20:39:47] (step=0060500) Train Loss: 0.0992, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 20:40:33] (step=0060600) Train Loss: 0.0883, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 20:40:33] Beginning epoch 15...
+[2025-05-14 20:41:19] (step=0060700) Train Loss: 0.0943, Train Steps/Sec: 2.15, Samples/Sec: 77.30
+[2025-05-14 20:42:06] (step=0060800) Train Loss: 0.0968, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 20:42:52] (step=0060900) Train Loss: 0.0898, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 20:43:38] (step=0061000) Train Loss: 0.0941, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 20:44:24] (step=0061100) Train Loss: 0.0902, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-14 20:45:10] (step=0061200) Train Loss: 0.0949, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 20:45:56] (step=0061300) Train Loss: 0.0942, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 20:46:43] (step=0061400) Train Loss: 0.0919, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 20:47:29] (step=0061500) Train Loss: 0.0942, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 20:48:15] (step=0061600) Train Loss: 0.0932, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 20:49:01] (step=0061700) Train Loss: 0.0919, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 20:49:48] (step=0061800) Train Loss: 0.0887, Train Steps/Sec: 2.16, Samples/Sec: 77.70
+[2025-05-14 20:50:34] (step=0061900) Train Loss: 0.0909, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 20:51:20] (step=0062000) Train Loss: 0.0929, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 20:51:33] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 20:52:20] (step=0062100) Train Loss: 0.0982, Train Steps/Sec: 1.69, Samples/Sec: 60.67
+[2025-05-14 20:53:06] (step=0062200) Train Loss: 0.0919, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 20:53:52] (step=0062300) Train Loss: 0.0885, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 20:54:38] (step=0062400) Train Loss: 0.0916, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-05-14 20:55:25] (step=0062500) Train Loss: 0.0907, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 20:56:11] (step=0062600) Train Loss: 0.0884, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 20:56:57] (step=0062700) Train Loss: 0.0878, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 20:57:44] (step=0062800) Train Loss: 0.0942, Train Steps/Sec: 2.15, Samples/Sec: 77.50
+[2025-05-14 20:58:30] (step=0062900) Train Loss: 0.0903, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 20:59:16] (step=0063000) Train Loss: 0.0875, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 21:00:03] (step=0063100) Train Loss: 0.0922, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-05-14 21:00:49] (step=0063200) Train Loss: 0.0973, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 21:01:35] (step=0063300) Train Loss: 0.0917, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 21:02:22] (step=0063400) Train Loss: 0.0938, Train Steps/Sec: 2.15, Samples/Sec: 77.51
+[2025-05-14 21:03:08] (step=0063500) Train Loss: 0.0925, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 21:03:54] (step=0063600) Train Loss: 0.0937, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 21:04:40] (step=0063700) Train Loss: 0.0889, Train Steps/Sec: 2.16, Samples/Sec: 77.75
+[2025-05-14 21:05:27] (step=0063800) Train Loss: 0.0982, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-05-14 21:06:13] (step=0063900) Train Loss: 0.0974, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 21:06:59] (step=0064000) Train Loss: 0.0942, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-14 21:07:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 21:07:58] (step=0064100) Train Loss: 0.0925, Train Steps/Sec: 1.69, Samples/Sec: 60.84
+[2025-05-14 21:08:45] (step=0064200) Train Loss: 0.0911, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 21:09:31] (step=0064300) Train Loss: 0.0859, Train Steps/Sec: 2.16, Samples/Sec: 77.67
+[2025-05-14 21:10:17] (step=0064400) Train Loss: 0.0939, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 21:11:04] (step=0064500) Train Loss: 0.0961, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 21:11:50] (step=0064600) Train Loss: 0.0912, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 21:12:08] Beginning epoch 16...
+[2025-05-14 21:12:36] (step=0064700) Train Loss: 0.0917, Train Steps/Sec: 2.15, Samples/Sec: 77.34
+[2025-05-14 21:13:23] (step=0064800) Train Loss: 0.0864, Train Steps/Sec: 2.15, Samples/Sec: 77.51
+[2025-05-14 21:14:09] (step=0064900) Train Loss: 0.1003, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 21:14:56] (step=0065000) Train Loss: 0.0872, Train Steps/Sec: 2.16, Samples/Sec: 77.62
+[2025-05-14 21:15:42] (step=0065100) Train Loss: 0.0905, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-14 21:16:28] (step=0065200) Train Loss: 0.0908, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 21:17:14] (step=0065300) Train Loss: 0.0887, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-14 21:18:01] (step=0065400) Train Loss: 0.0949, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-14 21:18:47] (step=0065500) Train Loss: 0.0919, Train Steps/Sec: 2.16, Samples/Sec: 77.63
+[2025-05-14 21:19:33] (step=0065600) Train Loss: 0.0875, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 21:20:20] (step=0065700) Train Loss: 0.0954, Train Steps/Sec: 2.16, Samples/Sec: 77.63
+[2025-05-14 21:21:06] (step=0065800) Train Loss: 0.0887, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 21:21:52] (step=0065900) Train Loss: 0.0879, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 21:22:39] (step=0066000) Train Loss: 0.0952, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-05-14 21:22:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 21:23:38] (step=0066100) Train Loss: 0.0836, Train Steps/Sec: 1.69, Samples/Sec: 60.89
+[2025-05-14 21:24:24] (step=0066200) Train Loss: 0.0940, Train Steps/Sec: 2.16, Samples/Sec: 77.74
+[2025-05-14 21:25:10] (step=0066300) Train Loss: 0.0916, Train Steps/Sec: 2.16, Samples/Sec: 77.65
+[2025-05-14 21:25:57] (step=0066400) Train Loss: 0.0834, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-14 21:26:43] (step=0066500) Train Loss: 0.0920, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-14 21:27:29] (step=0066600) Train Loss: 0.0914, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-14 21:28:15] (step=0066700) Train Loss: 0.0915, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 21:29:01] (step=0066800) Train Loss: 0.0963, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-14 21:29:48] (step=0066900) Train Loss: 0.0897, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 21:30:34] (step=0067000) Train Loss: 0.0967, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 21:31:20] (step=0067100) Train Loss: 0.0932, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-14 21:32:06] (step=0067200) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 21:32:52] (step=0067300) Train Loss: 0.0910, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 21:33:38] (step=0067400) Train Loss: 0.0895, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 21:34:25] (step=0067500) Train Loss: 0.0926, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-14 21:35:11] (step=0067600) Train Loss: 0.0878, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 21:35:57] (step=0067700) Train Loss: 0.0925, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 21:36:43] (step=0067800) Train Loss: 0.0868, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 21:37:29] (step=0067900) Train Loss: 0.0904, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 21:38:15] (step=0068000) Train Loss: 0.0851, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 21:38:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 21:39:14] (step=0068100) Train Loss: 0.0870, Train Steps/Sec: 1.72, Samples/Sec: 61.74
+[2025-05-14 21:40:00] (step=0068200) Train Loss: 0.0869, Train Steps/Sec: 2.16, Samples/Sec: 77.71
+[2025-05-14 21:40:46] (step=0068300) Train Loss: 0.0880, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 21:41:32] (step=0068400) Train Loss: 0.0921, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 21:42:18] (step=0068500) Train Loss: 0.0881, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-14 21:43:04] (step=0068600) Train Loss: 0.0882, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 21:43:41] Beginning epoch 17...
+[2025-05-14 21:43:51] (step=0068700) Train Loss: 0.0855, Train Steps/Sec: 2.16, Samples/Sec: 77.59
+[2025-05-14 21:44:37] (step=0068800) Train Loss: 0.0919, Train Steps/Sec: 2.16, Samples/Sec: 77.70
+[2025-05-14 21:45:23] (step=0068900) Train Loss: 0.0857, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 21:46:10] (step=0069000) Train Loss: 0.0946, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 21:46:56] (step=0069100) Train Loss: 0.0912, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-14 21:47:42] (step=0069200) Train Loss: 0.0941, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-14 21:48:28] (step=0069300) Train Loss: 0.0890, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 21:49:14] (step=0069400) Train Loss: 0.0892, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-14 21:50:00] (step=0069500) Train Loss: 0.0896, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-14 21:50:47] (step=0069600) Train Loss: 0.0918, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 21:51:33] (step=0069700) Train Loss: 0.0883, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 21:52:19] (step=0069800) Train Loss: 0.0904, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 21:53:05] (step=0069900) Train Loss: 0.0893, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 21:53:51] (step=0070000) Train Loss: 0.0901, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 21:54:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 21:54:06] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 21:56:06] (step=0070000) Perceptual Loss: 0.2475, Eval Time: 122.45
+[2025-05-14 21:56:52] (step=0070100) Train Loss: 0.0903, Train Steps/Sec: 0.55, Samples/Sec: 19.87
+[2025-05-14 21:57:38] (step=0070200) Train Loss: 0.0932, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-14 21:58:24] (step=0070300) Train Loss: 0.0853, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 21:59:11] (step=0070400) Train Loss: 0.0997, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 21:59:57] (step=0070500) Train Loss: 0.0914, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 22:00:43] (step=0070600) Train Loss: 0.0869, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 22:01:29] (step=0070700) Train Loss: 0.0897, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 22:02:15] (step=0070800) Train Loss: 0.0911, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 22:03:01] (step=0070900) Train Loss: 0.0888, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 22:03:48] (step=0071000) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:04:34] (step=0071100) Train Loss: 0.0886, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:05:20] (step=0071200) Train Loss: 0.0897, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:06:06] (step=0071300) Train Loss: 0.0906, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 22:06:52] (step=0071400) Train Loss: 0.0824, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-14 22:07:38] (step=0071500) Train Loss: 0.0872, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 22:08:24] (step=0071600) Train Loss: 0.0886, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:09:11] (step=0071700) Train Loss: 0.0861, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 22:09:57] (step=0071800) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:10:43] (step=0071900) Train Loss: 0.0926, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:11:29] (step=0072000) Train Loss: 0.0901, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 22:11:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 22:12:28] (step=0072100) Train Loss: 0.0868, Train Steps/Sec: 1.69, Samples/Sec: 60.82
+[2025-05-14 22:13:14] (step=0072200) Train Loss: 0.0896, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:14:00] (step=0072300) Train Loss: 0.0860, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:14:46] (step=0072400) Train Loss: 0.0855, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 22:15:32] (step=0072500) Train Loss: 0.0861, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 22:16:19] (step=0072600) Train Loss: 0.0927, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 22:17:05] (step=0072700) Train Loss: 0.0898, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 22:17:14] Beginning epoch 18...
+[2025-05-14 22:17:51] (step=0072800) Train Loss: 0.0910, Train Steps/Sec: 2.15, Samples/Sec: 77.31
+[2025-05-14 22:18:37] (step=0072900) Train Loss: 0.0822, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:19:24] (step=0073000) Train Loss: 0.0945, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:20:10] (step=0073100) Train Loss: 0.0879, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:20:56] (step=0073200) Train Loss: 0.0914, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 22:21:42] (step=0073300) Train Loss: 0.0895, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:22:28] (step=0073400) Train Loss: 0.0819, Train Steps/Sec: 2.16, Samples/Sec: 77.67
+[2025-05-14 22:23:14] (step=0073500) Train Loss: 0.0922, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 22:24:00] (step=0073600) Train Loss: 0.0846, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:24:47] (step=0073700) Train Loss: 0.0863, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:25:33] (step=0073800) Train Loss: 0.0920, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 22:26:19] (step=0073900) Train Loss: 0.0920, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 22:27:05] (step=0074000) Train Loss: 0.0838, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 22:27:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 22:28:03] (step=0074100) Train Loss: 0.0869, Train Steps/Sec: 1.71, Samples/Sec: 61.59
+[2025-05-14 22:28:50] (step=0074200) Train Loss: 0.0838, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:29:36] (step=0074300) Train Loss: 0.0870, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:30:22] (step=0074400) Train Loss: 0.0908, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 22:31:08] (step=0074500) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:31:54] (step=0074600) Train Loss: 0.0927, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:32:40] (step=0074700) Train Loss: 0.0876, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-14 22:33:27] (step=0074800) Train Loss: 0.0912, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-14 22:34:13] (step=0074900) Train Loss: 0.0914, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-14 22:34:59] (step=0075000) Train Loss: 0.0880, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-14 22:35:45] (step=0075100) Train Loss: 0.0908, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:36:31] (step=0075200) Train Loss: 0.0814, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:37:17] (step=0075300) Train Loss: 0.0861, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-14 22:38:03] (step=0075400) Train Loss: 0.0920, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 22:38:49] (step=0075500) Train Loss: 0.0907, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-14 22:39:36] (step=0075600) Train Loss: 0.0895, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:40:22] (step=0075700) Train Loss: 0.0867, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:41:08] (step=0075800) Train Loss: 0.0936, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:41:54] (step=0075900) Train Loss: 0.0875, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 22:42:40] (step=0076000) Train Loss: 0.0903, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-14 22:42:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 22:43:38] (step=0076100) Train Loss: 0.0864, Train Steps/Sec: 1.72, Samples/Sec: 61.82
+[2025-05-14 22:44:24] (step=0076200) Train Loss: 0.0847, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 22:45:10] (step=0076300) Train Loss: 0.0875, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 22:45:57] (step=0076400) Train Loss: 0.0871, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:46:43] (step=0076500) Train Loss: 0.0893, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 22:47:29] (step=0076600) Train Loss: 0.0916, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 22:48:15] (step=0076700) Train Loss: 0.0859, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-14 22:48:43] Beginning epoch 19...
+[2025-05-14 22:49:01] (step=0076800) Train Loss: 0.0860, Train Steps/Sec: 2.15, Samples/Sec: 77.53
+[2025-05-14 22:49:48] (step=0076900) Train Loss: 0.0882, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:50:34] (step=0077000) Train Loss: 0.0919, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:51:20] (step=0077100) Train Loss: 0.0890, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 22:52:06] (step=0077200) Train Loss: 0.0873, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:52:52] (step=0077300) Train Loss: 0.0860, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 22:53:38] (step=0077400) Train Loss: 0.0853, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-14 22:54:24] (step=0077500) Train Loss: 0.0871, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 22:55:10] (step=0077600) Train Loss: 0.0913, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 22:55:57] (step=0077700) Train Loss: 0.0859, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:56:43] (step=0077800) Train Loss: 0.0858, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 22:57:29] (step=0077900) Train Loss: 0.0882, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 22:58:15] (step=0078000) Train Loss: 0.0823, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 22:58:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 22:59:13] (step=0078100) Train Loss: 0.0862, Train Steps/Sec: 1.72, Samples/Sec: 61.80
+[2025-05-14 22:59:59] (step=0078200) Train Loss: 0.0834, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-14 23:00:45] (step=0078300) Train Loss: 0.0891, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 23:01:32] (step=0078400) Train Loss: 0.0878, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 23:02:18] (step=0078500) Train Loss: 0.0833, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 23:03:04] (step=0078600) Train Loss: 0.0914, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 23:03:50] (step=0078700) Train Loss: 0.0907, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 23:04:36] (step=0078800) Train Loss: 0.0855, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-14 23:05:22] (step=0078900) Train Loss: 0.0901, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 23:06:08] (step=0079000) Train Loss: 0.0861, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 23:06:55] (step=0079100) Train Loss: 0.0897, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 23:07:41] (step=0079200) Train Loss: 0.0884, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 23:08:27] (step=0079300) Train Loss: 0.0851, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 23:09:13] (step=0079400) Train Loss: 0.0871, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 23:09:59] (step=0079500) Train Loss: 0.0890, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 23:10:45] (step=0079600) Train Loss: 0.0838, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 23:11:31] (step=0079700) Train Loss: 0.0828, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 23:12:17] (step=0079800) Train Loss: 0.0911, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 23:13:03] (step=0079900) Train Loss: 0.0847, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 23:13:50] (step=0080000) Train Loss: 0.0858, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 23:14:14] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0080000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 23:14:17] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 23:16:16] (step=0080000) Perceptual Loss: 0.2622, Eval Time: 122.54
+[2025-05-14 23:17:02] (step=0080100) Train Loss: 0.0874, Train Steps/Sec: 0.52, Samples/Sec: 18.67
+[2025-05-14 23:17:49] (step=0080200) Train Loss: 0.0859, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 23:18:35] (step=0080300) Train Loss: 0.0845, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-14 23:19:21] (step=0080400) Train Loss: 0.0858, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 23:20:07] (step=0080500) Train Loss: 0.0879, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 23:20:53] (step=0080600) Train Loss: 0.0864, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 23:21:39] (step=0080700) Train Loss: 0.0829, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 23:22:25] (step=0080800) Train Loss: 0.0876, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-14 23:22:25] Beginning epoch 20...
+[2025-05-14 23:23:12] (step=0080900) Train Loss: 0.0882, Train Steps/Sec: 2.15, Samples/Sec: 77.52
+[2025-05-14 23:23:58] (step=0081000) Train Loss: 0.0823, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 23:24:44] (step=0081100) Train Loss: 0.0849, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 23:25:30] (step=0081200) Train Loss: 0.0828, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 23:26:16] (step=0081300) Train Loss: 0.0871, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:27:03] (step=0081400) Train Loss: 0.0882, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 23:27:49] (step=0081500) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-14 23:28:35] (step=0081600) Train Loss: 0.0886, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-14 23:29:21] (step=0081700) Train Loss: 0.0863, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-14 23:30:07] (step=0081800) Train Loss: 0.0868, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:30:53] (step=0081900) Train Loss: 0.0901, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 23:31:39] (step=0082000) Train Loss: 0.0853, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 23:31:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 23:32:37] (step=0082100) Train Loss: 0.0818, Train Steps/Sec: 1.72, Samples/Sec: 61.81
+[2025-05-14 23:33:23] (step=0082200) Train Loss: 0.0864, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-14 23:34:10] (step=0082300) Train Loss: 0.0872, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-14 23:34:56] (step=0082400) Train Loss: 0.0874, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 23:35:42] (step=0082500) Train Loss: 0.0853, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 23:36:28] (step=0082600) Train Loss: 0.0866, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:37:14] (step=0082700) Train Loss: 0.0824, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 23:38:00] (step=0082800) Train Loss: 0.0871, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-14 23:38:46] (step=0082900) Train Loss: 0.0829, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:39:32] (step=0083000) Train Loss: 0.0904, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:40:18] (step=0083100) Train Loss: 0.0841, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-14 23:41:05] (step=0083200) Train Loss: 0.0870, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:41:51] (step=0083300) Train Loss: 0.0842, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:42:37] (step=0083400) Train Loss: 0.0886, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-14 23:43:23] (step=0083500) Train Loss: 0.0861, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 23:44:09] (step=0083600) Train Loss: 0.0860, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:44:55] (step=0083700) Train Loss: 0.0822, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-14 23:45:41] (step=0083800) Train Loss: 0.0915, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-14 23:46:27] (step=0083900) Train Loss: 0.0866, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:47:13] (step=0084000) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:47:26] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-14 23:48:12] (step=0084100) Train Loss: 0.0890, Train Steps/Sec: 1.71, Samples/Sec: 61.72
+[2025-05-14 23:48:58] (step=0084200) Train Loss: 0.0878, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 23:49:44] (step=0084300) Train Loss: 0.0860, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 23:50:30] (step=0084400) Train Loss: 0.0908, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-14 23:51:16] (step=0084500) Train Loss: 0.0839, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-14 23:52:02] (step=0084600) Train Loss: 0.0880, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:52:48] (step=0084700) Train Loss: 0.0827, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-14 23:53:35] (step=0084800) Train Loss: 0.0875, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-14 23:53:53] Beginning epoch 21...
+[2025-05-14 23:54:21] (step=0084900) Train Loss: 0.0883, Train Steps/Sec: 2.16, Samples/Sec: 77.68
+[2025-05-14 23:55:07] (step=0085000) Train Loss: 0.0939, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 23:55:53] (step=0085100) Train Loss: 0.0881, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-14 23:56:39] (step=0085200) Train Loss: 0.0873, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 23:57:25] (step=0085300) Train Loss: 0.0860, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-14 23:58:11] (step=0085400) Train Loss: 0.0868, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-14 23:58:58] (step=0085500) Train Loss: 0.0872, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-14 23:59:44] (step=0085600) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 00:00:30] (step=0085700) Train Loss: 0.0872, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 00:01:16] (step=0085800) Train Loss: 0.0881, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 00:02:02] (step=0085900) Train Loss: 0.0872, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 00:02:48] (step=0086000) Train Loss: 0.0915, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 00:03:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 00:03:47] (step=0086100) Train Loss: 0.0889, Train Steps/Sec: 1.69, Samples/Sec: 60.88
+[2025-05-15 00:04:34] (step=0086200) Train Loss: 0.0871, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 00:05:20] (step=0086300) Train Loss: 0.0870, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 00:06:06] (step=0086400) Train Loss: 0.0863, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 00:06:52] (step=0086500) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 00:07:38] (step=0086600) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 00:08:24] (step=0086700) Train Loss: 0.0849, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 00:09:10] (step=0086800) Train Loss: 0.0822, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-15 00:09:57] (step=0086900) Train Loss: 0.0843, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-15 00:10:43] (step=0087000) Train Loss: 0.0806, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-15 00:11:29] (step=0087100) Train Loss: 0.0900, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 00:12:15] (step=0087200) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 00:13:01] (step=0087300) Train Loss: 0.0828, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 00:13:47] (step=0087400) Train Loss: 0.0884, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 00:14:34] (step=0087500) Train Loss: 0.0830, Train Steps/Sec: 2.16, Samples/Sec: 77.71
+[2025-05-15 00:15:20] (step=0087600) Train Loss: 0.0843, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 00:16:06] (step=0087700) Train Loss: 0.0864, Train Steps/Sec: 2.16, Samples/Sec: 77.69
+[2025-05-15 00:16:52] (step=0087800) Train Loss: 0.0856, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 00:17:38] (step=0087900) Train Loss: 0.0864, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 00:18:25] (step=0088000) Train Loss: 0.0886, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 00:18:37] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 00:19:23] (step=0088100) Train Loss: 0.0873, Train Steps/Sec: 1.72, Samples/Sec: 61.77
+[2025-05-15 00:20:09] (step=0088200) Train Loss: 0.0848, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-15 00:20:55] (step=0088300) Train Loss: 0.0898, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-15 00:21:42] (step=0088400) Train Loss: 0.0859, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 00:22:28] (step=0088500) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 00:23:14] (step=0088600) Train Loss: 0.0835, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 00:24:00] (step=0088700) Train Loss: 0.0843, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-15 00:24:46] (step=0088800) Train Loss: 0.0850, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 00:25:23] Beginning epoch 22...
+[2025-05-15 00:25:33] (step=0088900) Train Loss: 0.0874, Train Steps/Sec: 2.15, Samples/Sec: 77.25
+[2025-05-15 00:26:19] (step=0089000) Train Loss: 0.0835, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-15 00:27:05] (step=0089100) Train Loss: 0.0820, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 00:27:52] (step=0089200) Train Loss: 0.0851, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 00:28:38] (step=0089300) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 00:29:24] (step=0089400) Train Loss: 0.0829, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 00:30:10] (step=0089500) Train Loss: 0.0847, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 00:30:56] (step=0089600) Train Loss: 0.0887, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 00:31:42] (step=0089700) Train Loss: 0.0836, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 00:32:28] (step=0089800) Train Loss: 0.0855, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 00:33:14] (step=0089900) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 00:34:01] (step=0090000) Train Loss: 0.0875, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 00:34:13] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 00:34:16] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 00:36:15] (step=0090000) Perceptual Loss: 0.2633, Eval Time: 122.48
+[2025-05-15 00:37:02] (step=0090100) Train Loss: 0.0848, Train Steps/Sec: 0.55, Samples/Sec: 19.89
+[2025-05-15 00:37:48] (step=0090200) Train Loss: 0.0871, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 00:38:34] (step=0090300) Train Loss: 0.0888, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 00:39:20] (step=0090400) Train Loss: 0.0826, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 00:40:06] (step=0090500) Train Loss: 0.0849, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 00:40:52] (step=0090600) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 00:41:38] (step=0090700) Train Loss: 0.0866, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 00:42:24] (step=0090800) Train Loss: 0.0856, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 00:43:11] (step=0090900) Train Loss: 0.0896, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 00:43:57] (step=0091000) Train Loss: 0.0823, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 00:44:43] (step=0091100) Train Loss: 0.0851, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 00:45:29] (step=0091200) Train Loss: 0.0907, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 00:46:15] (step=0091300) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 00:47:01] (step=0091400) Train Loss: 0.0829, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 00:47:47] (step=0091500) Train Loss: 0.0843, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 00:48:33] (step=0091600) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 00:49:19] (step=0091700) Train Loss: 0.0839, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 00:50:06] (step=0091800) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 00:50:52] (step=0091900) Train Loss: 0.0896, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 00:51:38] (step=0092000) Train Loss: 0.0879, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 00:51:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 00:52:37] (step=0092100) Train Loss: 0.0829, Train Steps/Sec: 1.69, Samples/Sec: 60.90
+[2025-05-15 00:53:23] (step=0092200) Train Loss: 0.0780, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 00:54:09] (step=0092300) Train Loss: 0.0800, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 00:54:55] (step=0092400) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 00:55:41] (step=0092500) Train Loss: 0.0775, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 00:56:27] (step=0092600) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 00:57:13] (step=0092700) Train Loss: 0.0794, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 00:58:00] (step=0092800) Train Loss: 0.0834, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 00:58:46] (step=0092900) Train Loss: 0.0805, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 00:58:55] Beginning epoch 23...
+[2025-05-15 00:59:32] (step=0093000) Train Loss: 0.0808, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-15 01:00:18] (step=0093100) Train Loss: 0.0895, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 01:01:04] (step=0093200) Train Loss: 0.0879, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 01:01:50] (step=0093300) Train Loss: 0.0777, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:02:36] (step=0093400) Train Loss: 0.0835, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 01:03:23] (step=0093500) Train Loss: 0.0908, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 01:04:09] (step=0093600) Train Loss: 0.0776, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 01:04:55] (step=0093700) Train Loss: 0.0839, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 01:05:41] (step=0093800) Train Loss: 0.0840, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:06:27] (step=0093900) Train Loss: 0.0908, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:07:13] (step=0094000) Train Loss: 0.0853, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:07:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 01:08:11] (step=0094100) Train Loss: 0.0842, Train Steps/Sec: 1.71, Samples/Sec: 61.62
+[2025-05-15 01:08:57] (step=0094200) Train Loss: 0.0834, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 01:09:44] (step=0094300) Train Loss: 0.0884, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:10:30] (step=0094400) Train Loss: 0.0819, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:11:16] (step=0094500) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 01:12:02] (step=0094600) Train Loss: 0.0823, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:12:48] (step=0094700) Train Loss: 0.0844, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:13:34] (step=0094800) Train Loss: 0.0799, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 01:14:20] (step=0094900) Train Loss: 0.0880, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 01:15:06] (step=0095000) Train Loss: 0.0816, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:15:52] (step=0095100) Train Loss: 0.0860, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:16:38] (step=0095200) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:17:24] (step=0095300) Train Loss: 0.0806, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:18:10] (step=0095400) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:18:57] (step=0095500) Train Loss: 0.0775, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-15 01:19:43] (step=0095600) Train Loss: 0.0820, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:20:29] (step=0095700) Train Loss: 0.0852, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:21:15] (step=0095800) Train Loss: 0.0749, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:22:01] (step=0095900) Train Loss: 0.0860, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:22:47] (step=0096000) Train Loss: 0.0872, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:22:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 01:23:45] (step=0096100) Train Loss: 0.0788, Train Steps/Sec: 1.71, Samples/Sec: 61.60
+[2025-05-15 01:24:32] (step=0096200) Train Loss: 0.0859, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 01:25:18] (step=0096300) Train Loss: 0.0807, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:26:04] (step=0096400) Train Loss: 0.0795, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:26:50] (step=0096500) Train Loss: 0.0839, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:27:36] (step=0096600) Train Loss: 0.0855, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 01:28:22] (step=0096700) Train Loss: 0.0784, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:29:08] (step=0096800) Train Loss: 0.0818, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 01:29:54] (step=0096900) Train Loss: 0.0823, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:30:22] Beginning epoch 24...
+[2025-05-15 01:30:40] (step=0097000) Train Loss: 0.0821, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-15 01:31:27] (step=0097100) Train Loss: 0.0834, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:32:13] (step=0097200) Train Loss: 0.0847, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:32:59] (step=0097300) Train Loss: 0.0801, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:33:45] (step=0097400) Train Loss: 0.0819, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:34:31] (step=0097500) Train Loss: 0.0808, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 01:35:17] (step=0097600) Train Loss: 0.0812, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 01:36:03] (step=0097700) Train Loss: 0.0834, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 01:36:49] (step=0097800) Train Loss: 0.0811, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:37:35] (step=0097900) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:38:21] (step=0098000) Train Loss: 0.0874, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:38:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 01:39:20] (step=0098100) Train Loss: 0.0851, Train Steps/Sec: 1.70, Samples/Sec: 61.18
+[2025-05-15 01:40:06] (step=0098200) Train Loss: 0.0833, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 01:40:52] (step=0098300) Train Loss: 0.0883, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:41:38] (step=0098400) Train Loss: 0.0836, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:42:25] (step=0098500) Train Loss: 0.0830, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:43:11] (step=0098600) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:43:57] (step=0098700) Train Loss: 0.0780, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:44:43] (step=0098800) Train Loss: 0.0740, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 01:45:29] (step=0098900) Train Loss: 0.0892, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 01:46:15] (step=0099000) Train Loss: 0.0826, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:47:01] (step=0099100) Train Loss: 0.0798, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:47:47] (step=0099200) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:48:33] (step=0099300) Train Loss: 0.0850, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:49:19] (step=0099400) Train Loss: 0.0801, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:50:06] (step=0099500) Train Loss: 0.0842, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-15 01:50:52] (step=0099600) Train Loss: 0.0818, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:51:38] (step=0099700) Train Loss: 0.0806, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 01:52:24] (step=0099800) Train Loss: 0.0866, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 01:53:10] (step=0099900) Train Loss: 0.0805, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 01:53:56] (step=0100000) Train Loss: 0.0866, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 01:54:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0100000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 01:54:23] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 01:56:23] (step=0100000) Perceptual Loss: 0.2715, Eval Time: 122.40
+[2025-05-15 01:57:09] (step=0100100) Train Loss: 0.0825, Train Steps/Sec: 0.52, Samples/Sec: 18.64
+[2025-05-15 01:57:55] (step=0100200) Train Loss: 0.0846, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 01:58:41] (step=0100300) Train Loss: 0.0804, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 01:59:27] (step=0100400) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 02:00:13] (step=0100500) Train Loss: 0.0854, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 02:00:59] (step=0100600) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 02:01:46] (step=0100700) Train Loss: 0.0793, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 02:02:32] (step=0100800) Train Loss: 0.0780, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 02:03:18] (step=0100900) Train Loss: 0.0818, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 02:04:04] (step=0101000) Train Loss: 0.0885, Train Steps/Sec: 2.17, Samples/Sec: 78.22
+[2025-05-15 02:04:04] Beginning epoch 25...
+[2025-05-15 02:04:50] (step=0101100) Train Loss: 0.0819, Train Steps/Sec: 2.15, Samples/Sec: 77.54
+[2025-05-15 02:05:37] (step=0101200) Train Loss: 0.0841, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 02:06:23] (step=0101300) Train Loss: 0.0840, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 02:07:09] (step=0101400) Train Loss: 0.0746, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-05-15 02:07:55] (step=0101500) Train Loss: 0.0850, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 02:08:41] (step=0101600) Train Loss: 0.0864, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 02:09:27] (step=0101700) Train Loss: 0.0836, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 02:10:13] (step=0101800) Train Loss: 0.0854, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 02:10:59] (step=0101900) Train Loss: 0.0797, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 02:11:45] (step=0102000) Train Loss: 0.0861, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:11:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 02:12:44] (step=0102100) Train Loss: 0.0782, Train Steps/Sec: 1.70, Samples/Sec: 61.24
+[2025-05-15 02:13:30] (step=0102200) Train Loss: 0.0829, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:14:16] (step=0102300) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 02:15:02] (step=0102400) Train Loss: 0.0825, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:15:48] (step=0102500) Train Loss: 0.0792, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:16:34] (step=0102600) Train Loss: 0.0822, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:17:21] (step=0102700) Train Loss: 0.0861, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 02:18:07] (step=0102800) Train Loss: 0.0873, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 02:18:53] (step=0102900) Train Loss: 0.0846, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 02:19:39] (step=0103000) Train Loss: 0.0936, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 02:20:25] (step=0103100) Train Loss: 0.0837, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 02:21:11] (step=0103200) Train Loss: 0.0807, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 02:21:57] (step=0103300) Train Loss: 0.0851, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:22:43] (step=0103400) Train Loss: 0.0826, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 02:23:29] (step=0103500) Train Loss: 0.0803, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 02:24:15] (step=0103600) Train Loss: 0.0844, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 02:25:01] (step=0103700) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:25:47] (step=0103800) Train Loss: 0.0850, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:26:34] (step=0103900) Train Loss: 0.0771, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 02:27:20] (step=0104000) Train Loss: 0.0916, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:27:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 02:28:18] (step=0104100) Train Loss: 0.0795, Train Steps/Sec: 1.72, Samples/Sec: 61.92
+[2025-05-15 02:29:04] (step=0104200) Train Loss: 0.0852, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 02:29:50] (step=0104300) Train Loss: 0.0870, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-05-15 02:30:36] (step=0104400) Train Loss: 0.0822, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:31:22] (step=0104500) Train Loss: 0.0862, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 02:32:08] (step=0104600) Train Loss: 0.0765, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:32:54] (step=0104700) Train Loss: 0.0849, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 02:33:40] (step=0104800) Train Loss: 0.0818, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 02:34:26] (step=0104900) Train Loss: 0.0805, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 02:35:12] (step=0105000) Train Loss: 0.0806, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:35:31] Beginning epoch 26...
+[2025-05-15 02:35:59] (step=0105100) Train Loss: 0.0862, Train Steps/Sec: 2.16, Samples/Sec: 77.67
+[2025-05-15 02:36:45] (step=0105200) Train Loss: 0.0816, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 02:37:31] (step=0105300) Train Loss: 0.0844, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 02:38:17] (step=0105400) Train Loss: 0.0804, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-15 02:39:03] (step=0105500) Train Loss: 0.0839, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 02:39:50] (step=0105600) Train Loss: 0.0863, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-15 02:40:36] (step=0105700) Train Loss: 0.0813, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 02:41:22] (step=0105800) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 02:42:08] (step=0105900) Train Loss: 0.0770, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 02:42:54] (step=0106000) Train Loss: 0.0833, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 02:43:06] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 02:43:52] (step=0106100) Train Loss: 0.0810, Train Steps/Sec: 1.71, Samples/Sec: 61.66
+[2025-05-15 02:44:38] (step=0106200) Train Loss: 0.0810, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 02:45:25] (step=0106300) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 02:46:11] (step=0106400) Train Loss: 0.0758, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 02:46:57] (step=0106500) Train Loss: 0.0791, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:47:43] (step=0106600) Train Loss: 0.0830, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:48:29] (step=0106700) Train Loss: 0.0803, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 02:49:15] (step=0106800) Train Loss: 0.0824, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 02:50:01] (step=0106900) Train Loss: 0.0894, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:50:47] (step=0107000) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:51:33] (step=0107100) Train Loss: 0.0761, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:52:20] (step=0107200) Train Loss: 0.0828, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 02:53:06] (step=0107300) Train Loss: 0.0740, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 02:53:52] (step=0107400) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 02:54:38] (step=0107500) Train Loss: 0.0894, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 02:55:24] (step=0107600) Train Loss: 0.0831, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:56:10] (step=0107700) Train Loss: 0.0844, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 02:56:56] (step=0107800) Train Loss: 0.0833, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 02:57:42] (step=0107900) Train Loss: 0.0791, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 02:58:29] (step=0108000) Train Loss: 0.0869, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 02:58:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 02:59:27] (step=0108100) Train Loss: 0.0798, Train Steps/Sec: 1.72, Samples/Sec: 61.81
+[2025-05-15 03:00:13] (step=0108200) Train Loss: 0.0844, Train Steps/Sec: 2.16, Samples/Sec: 77.68
+[2025-05-15 03:00:59] (step=0108300) Train Loss: 0.0837, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 03:01:45] (step=0108400) Train Loss: 0.0912, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 03:02:31] (step=0108500) Train Loss: 0.0816, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 03:03:18] (step=0108600) Train Loss: 0.0740, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 03:04:04] (step=0108700) Train Loss: 0.0807, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 03:04:50] (step=0108800) Train Loss: 0.0825, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-15 03:05:36] (step=0108900) Train Loss: 0.0847, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 03:06:22] (step=0109000) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 03:06:59] Beginning epoch 27...
+[2025-05-15 03:07:09] (step=0109100) Train Loss: 0.0766, Train Steps/Sec: 2.16, Samples/Sec: 77.75
+[2025-05-15 03:07:55] (step=0109200) Train Loss: 0.0830, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 03:08:41] (step=0109300) Train Loss: 0.0818, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 03:09:27] (step=0109400) Train Loss: 0.0798, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 03:10:13] (step=0109500) Train Loss: 0.0754, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 03:10:59] (step=0109600) Train Loss: 0.0781, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:11:45] (step=0109700) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:12:31] (step=0109800) Train Loss: 0.0804, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:13:17] (step=0109900) Train Loss: 0.0833, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:14:03] (step=0110000) Train Loss: 0.0806, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 03:14:16] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 03:14:19] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 03:16:18] (step=0110000) Perceptual Loss: 0.2638, Eval Time: 122.15
+[2025-05-15 03:17:05] (step=0110100) Train Loss: 0.0861, Train Steps/Sec: 0.55, Samples/Sec: 19.87
+[2025-05-15 03:17:51] (step=0110200) Train Loss: 0.0769, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 03:18:37] (step=0110300) Train Loss: 0.0798, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:19:23] (step=0110400) Train Loss: 0.0799, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:20:09] (step=0110500) Train Loss: 0.0819, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:20:55] (step=0110600) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:21:41] (step=0110700) Train Loss: 0.0769, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 03:22:27] (step=0110800) Train Loss: 0.0882, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 03:23:13] (step=0110900) Train Loss: 0.0780, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 03:23:59] (step=0111000) Train Loss: 0.0799, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 03:24:46] (step=0111100) Train Loss: 0.0773, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 03:25:32] (step=0111200) Train Loss: 0.0811, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 03:26:18] (step=0111300) Train Loss: 0.0808, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 03:27:04] (step=0111400) Train Loss: 0.0856, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-15 03:27:50] (step=0111500) Train Loss: 0.0763, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 03:28:36] (step=0111600) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:29:22] (step=0111700) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 03:30:08] (step=0111800) Train Loss: 0.0813, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 03:30:54] (step=0111900) Train Loss: 0.0796, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 03:31:40] (step=0112000) Train Loss: 0.0835, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 03:31:53] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 03:32:39] (step=0112100) Train Loss: 0.0795, Train Steps/Sec: 1.70, Samples/Sec: 61.08
+[2025-05-15 03:33:25] (step=0112200) Train Loss: 0.0889, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:34:12] (step=0112300) Train Loss: 0.0805, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:34:58] (step=0112400) Train Loss: 0.0796, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:35:44] (step=0112500) Train Loss: 0.0787, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 03:36:30] (step=0112600) Train Loss: 0.0791, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 03:37:16] (step=0112700) Train Loss: 0.0834, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 03:38:02] (step=0112800) Train Loss: 0.0826, Train Steps/Sec: 2.16, Samples/Sec: 77.75
+[2025-05-15 03:38:48] (step=0112900) Train Loss: 0.0800, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 03:39:34] (step=0113000) Train Loss: 0.0852, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 03:40:20] (step=0113100) Train Loss: 0.0759, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:40:30] Beginning epoch 28...
+[2025-05-15 03:41:07] (step=0113200) Train Loss: 0.0765, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-05-15 03:41:53] (step=0113300) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:42:39] (step=0113400) Train Loss: 0.0831, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 03:43:25] (step=0113500) Train Loss: 0.0843, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 03:44:11] (step=0113600) Train Loss: 0.0795, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:44:57] (step=0113700) Train Loss: 0.0735, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:45:43] (step=0113800) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:46:29] (step=0113900) Train Loss: 0.0795, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:47:15] (step=0114000) Train Loss: 0.0776, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 03:47:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 03:48:14] (step=0114100) Train Loss: 0.0759, Train Steps/Sec: 1.71, Samples/Sec: 61.39
+[2025-05-15 03:49:00] (step=0114200) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:49:46] (step=0114300) Train Loss: 0.0830, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:50:32] (step=0114400) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:51:18] (step=0114500) Train Loss: 0.0772, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 03:52:04] (step=0114600) Train Loss: 0.0726, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 03:52:51] (step=0114700) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 03:53:37] (step=0114800) Train Loss: 0.0853, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 03:54:23] (step=0114900) Train Loss: 0.0799, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 03:55:09] (step=0115000) Train Loss: 0.0783, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:55:55] (step=0115100) Train Loss: 0.0825, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 03:56:41] (step=0115200) Train Loss: 0.0777, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 03:57:27] (step=0115300) Train Loss: 0.0796, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 03:58:13] (step=0115400) Train Loss: 0.0862, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 03:58:59] (step=0115500) Train Loss: 0.0817, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 03:59:45] (step=0115600) Train Loss: 0.0846, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 04:00:31] (step=0115700) Train Loss: 0.0752, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 04:01:18] (step=0115800) Train Loss: 0.0795, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 04:02:04] (step=0115900) Train Loss: 0.0919, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:02:50] (step=0116000) Train Loss: 0.0750, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:03:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 04:03:48] (step=0116100) Train Loss: 0.0794, Train Steps/Sec: 1.71, Samples/Sec: 61.59
+[2025-05-15 04:04:34] (step=0116200) Train Loss: 0.0803, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 04:05:20] (step=0116300) Train Loss: 0.0783, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:06:06] (step=0116400) Train Loss: 0.0804, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:06:52] (step=0116500) Train Loss: 0.0838, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 04:07:39] (step=0116600) Train Loss: 0.0791, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 04:08:25] (step=0116700) Train Loss: 0.0836, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 04:09:11] (step=0116800) Train Loss: 0.0814, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-15 04:09:57] (step=0116900) Train Loss: 0.0766, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:10:43] (step=0117000) Train Loss: 0.0831, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:11:29] (step=0117100) Train Loss: 0.0737, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:11:57] Beginning epoch 29...
+[2025-05-15 04:12:15] (step=0117200) Train Loss: 0.0776, Train Steps/Sec: 2.16, Samples/Sec: 77.70
+[2025-05-15 04:13:01] (step=0117300) Train Loss: 0.0742, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 04:13:48] (step=0117400) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 04:14:34] (step=0117500) Train Loss: 0.0786, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 04:15:20] (step=0117600) Train Loss: 0.0780, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:16:06] (step=0117700) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:16:52] (step=0117800) Train Loss: 0.0800, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:17:38] (step=0117900) Train Loss: 0.0795, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 04:18:24] (step=0118000) Train Loss: 0.0778, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 04:18:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 04:19:23] (step=0118100) Train Loss: 0.0751, Train Steps/Sec: 1.71, Samples/Sec: 61.58
+[2025-05-15 04:20:09] (step=0118200) Train Loss: 0.0821, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 04:20:55] (step=0118300) Train Loss: 0.0819, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 04:21:41] (step=0118400) Train Loss: 0.0834, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:22:27] (step=0118500) Train Loss: 0.0801, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:23:13] (step=0118600) Train Loss: 0.0836, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:23:59] (step=0118700) Train Loss: 0.0812, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 04:24:45] (step=0118800) Train Loss: 0.0839, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 04:25:32] (step=0118900) Train Loss: 0.0826, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 04:26:18] (step=0119000) Train Loss: 0.0773, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 04:27:04] (step=0119100) Train Loss: 0.0835, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:27:50] (step=0119200) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:28:36] (step=0119300) Train Loss: 0.0822, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:29:22] (step=0119400) Train Loss: 0.0763, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 04:30:08] (step=0119500) Train Loss: 0.0831, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 04:30:54] (step=0119600) Train Loss: 0.0878, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:31:40] (step=0119700) Train Loss: 0.0799, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:32:26] (step=0119800) Train Loss: 0.0811, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:33:12] (step=0119900) Train Loss: 0.0797, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 04:33:58] (step=0120000) Train Loss: 0.0770, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 04:34:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0120000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 04:34:24] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 04:36:24] (step=0120000) Perceptual Loss: 0.2680, Eval Time: 122.19
+[2025-05-15 04:37:10] (step=0120100) Train Loss: 0.0781, Train Steps/Sec: 0.52, Samples/Sec: 18.77
+[2025-05-15 04:37:56] (step=0120200) Train Loss: 0.0786, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 04:38:42] (step=0120300) Train Loss: 0.0811, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:39:28] (step=0120400) Train Loss: 0.0736, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:40:14] (step=0120500) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:41:01] (step=0120600) Train Loss: 0.0773, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:41:47] (step=0120700) Train Loss: 0.0801, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 04:42:33] (step=0120800) Train Loss: 0.0803, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 04:43:19] (step=0120900) Train Loss: 0.0776, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 04:44:05] (step=0121000) Train Loss: 0.0874, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 04:44:51] (step=0121100) Train Loss: 0.0900, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:45:37] (step=0121200) Train Loss: 0.0785, Train Steps/Sec: 2.17, Samples/Sec: 78.23
+[2025-05-15 04:45:37] Beginning epoch 30...
+[2025-05-15 04:46:23] (step=0121300) Train Loss: 0.0808, Train Steps/Sec: 2.16, Samples/Sec: 77.68
+[2025-05-15 04:47:10] (step=0121400) Train Loss: 0.0760, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 04:47:56] (step=0121500) Train Loss: 0.0852, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 04:48:42] (step=0121600) Train Loss: 0.0754, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 04:49:28] (step=0121700) Train Loss: 0.0832, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 04:50:14] (step=0121800) Train Loss: 0.0787, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:51:00] (step=0121900) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:51:46] (step=0122000) Train Loss: 0.0813, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:51:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 04:52:45] (step=0122100) Train Loss: 0.0809, Train Steps/Sec: 1.70, Samples/Sec: 61.37
+[2025-05-15 04:53:31] (step=0122200) Train Loss: 0.0770, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 04:54:17] (step=0122300) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:55:03] (step=0122400) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:55:49] (step=0122500) Train Loss: 0.0843, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:56:35] (step=0122600) Train Loss: 0.0863, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 04:57:21] (step=0122700) Train Loss: 0.0888, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 04:58:08] (step=0122800) Train Loss: 0.0804, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 04:58:54] (step=0122900) Train Loss: 0.0850, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 04:59:40] (step=0123000) Train Loss: 0.0792, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:00:26] (step=0123100) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 05:01:12] (step=0123200) Train Loss: 0.0825, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 05:01:58] (step=0123300) Train Loss: 0.0829, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 05:02:44] (step=0123400) Train Loss: 0.0809, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 05:03:30] (step=0123500) Train Loss: 0.0841, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 05:04:16] (step=0123600) Train Loss: 0.0861, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 05:05:02] (step=0123700) Train Loss: 0.0853, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 05:05:48] (step=0123800) Train Loss: 0.0800, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 05:06:35] (step=0123900) Train Loss: 0.0802, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 05:07:21] (step=0124000) Train Loss: 0.0799, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 05:07:33] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 05:08:19] (step=0124100) Train Loss: 0.0764, Train Steps/Sec: 1.71, Samples/Sec: 61.63
+[2025-05-15 05:09:05] (step=0124200) Train Loss: 0.0774, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 05:09:51] (step=0124300) Train Loss: 0.0795, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 05:10:37] (step=0124400) Train Loss: 0.0780, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:11:23] (step=0124500) Train Loss: 0.0815, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 05:12:10] (step=0124600) Train Loss: 0.0837, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 05:12:56] (step=0124700) Train Loss: 0.0822, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 05:13:42] (step=0124800) Train Loss: 0.0972, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 05:14:28] (step=0124900) Train Loss: 0.0795, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:15:14] (step=0125000) Train Loss: 0.0814, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:16:00] (step=0125100) Train Loss: 0.0859, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:16:46] (step=0125200) Train Loss: 0.0796, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:17:04] Beginning epoch 31...
+[2025-05-15 05:17:32] (step=0125300) Train Loss: 0.0841, Train Steps/Sec: 2.16, Samples/Sec: 77.61
+[2025-05-15 05:18:19] (step=0125400) Train Loss: 0.0840, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:19:05] (step=0125500) Train Loss: 0.0826, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 05:19:51] (step=0125600) Train Loss: 0.0866, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 05:20:37] (step=0125700) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:21:23] (step=0125800) Train Loss: 0.0808, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:22:09] (step=0125900) Train Loss: 0.0843, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:22:55] (step=0126000) Train Loss: 0.0817, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 05:23:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 05:23:54] (step=0126100) Train Loss: 0.0843, Train Steps/Sec: 1.69, Samples/Sec: 61.00
+[2025-05-15 05:24:40] (step=0126200) Train Loss: 0.0781, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 05:25:26] (step=0126300) Train Loss: 0.0791, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 05:26:12] (step=0126400) Train Loss: 0.0805, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 05:26:58] (step=0126500) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:27:45] (step=0126600) Train Loss: 0.0821, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 05:28:31] (step=0126700) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 05:29:17] (step=0126800) Train Loss: 0.0881, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 05:30:03] (step=0126900) Train Loss: 0.0826, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 05:30:49] (step=0127000) Train Loss: 0.0841, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 05:31:35] (step=0127100) Train Loss: 0.0808, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 05:32:21] (step=0127200) Train Loss: 0.0831, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:33:07] (step=0127300) Train Loss: 0.0864, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 05:33:53] (step=0127400) Train Loss: 0.0858, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 05:34:40] (step=0127500) Train Loss: 0.0862, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 05:35:26] (step=0127600) Train Loss: 0.0776, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 05:36:12] (step=0127700) Train Loss: 0.0786, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 05:36:58] (step=0127800) Train Loss: 0.0802, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:37:44] (step=0127900) Train Loss: 0.0797, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:38:30] (step=0128000) Train Loss: 0.0818, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 05:38:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 05:39:28] (step=0128100) Train Loss: 0.0851, Train Steps/Sec: 1.72, Samples/Sec: 62.06
+[2025-05-15 05:40:14] (step=0128200) Train Loss: 0.0832, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 05:41:00] (step=0128300) Train Loss: 0.0814, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:41:46] (step=0128400) Train Loss: 0.0821, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:42:33] (step=0128500) Train Loss: 0.0856, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 05:43:19] (step=0128600) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:44:05] (step=0128700) Train Loss: 0.0808, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:44:51] (step=0128800) Train Loss: 0.0936, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 05:45:37] (step=0128900) Train Loss: 0.0824, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 05:46:23] (step=0129000) Train Loss: 0.0866, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 05:47:09] (step=0129100) Train Loss: 0.0801, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 05:47:55] (step=0129200) Train Loss: 0.0770, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 05:48:32] Beginning epoch 32...
+[2025-05-15 05:48:42] (step=0129300) Train Loss: 0.0775, Train Steps/Sec: 2.16, Samples/Sec: 77.70
+[2025-05-15 05:49:28] (step=0129400) Train Loss: 0.2217, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:50:14] (step=0129500) Train Loss: 0.1990, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 05:51:00] (step=0129600) Train Loss: 0.1085, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 05:51:46] (step=0129700) Train Loss: 0.0967, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 05:52:32] (step=0129800) Train Loss: 0.0845, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 05:53:18] (step=0129900) Train Loss: 0.0837, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 05:54:04] (step=0130000) Train Loss: 0.0822, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 05:54:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 05:54:20] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 05:56:19] (step=0130000) Perceptual Loss: 0.3007, Eval Time: 122.49
+[2025-05-15 05:57:06] (step=0130100) Train Loss: 0.0822, Train Steps/Sec: 0.55, Samples/Sec: 19.84
+[2025-05-15 05:57:52] (step=0130200) Train Loss: 0.0831, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 05:58:38] (step=0130300) Train Loss: 0.0826, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 05:59:24] (step=0130400) Train Loss: 0.0894, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 06:00:10] (step=0130500) Train Loss: 0.0877, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 06:00:56] (step=0130600) Train Loss: 0.0863, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 06:01:42] (step=0130700) Train Loss: 0.0890, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 06:02:28] (step=0130800) Train Loss: 0.0856, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 06:03:15] (step=0130900) Train Loss: 0.0877, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 06:04:01] (step=0131000) Train Loss: 0.1073, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 06:04:47] (step=0131100) Train Loss: 0.0905, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 06:05:33] (step=0131200) Train Loss: 0.0808, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 06:06:19] (step=0131300) Train Loss: 0.0909, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 06:07:05] (step=0131400) Train Loss: 0.0880, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 06:07:51] (step=0131500) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 06:08:37] (step=0131600) Train Loss: 0.0824, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 06:09:23] (step=0131700) Train Loss: 0.0813, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 06:10:10] (step=0131800) Train Loss: 0.1052, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 06:10:56] (step=0131900) Train Loss: 0.0990, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 06:11:42] (step=0132000) Train Loss: 0.0931, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 06:11:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 06:12:40] (step=0132100) Train Loss: 0.0871, Train Steps/Sec: 1.71, Samples/Sec: 61.70
+[2025-05-15 06:13:26] (step=0132200) Train Loss: 0.0912, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 06:14:12] (step=0132300) Train Loss: 0.1222, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:14:58] (step=0132400) Train Loss: 0.1000, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 06:15:44] (step=0132500) Train Loss: 0.0905, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 06:16:30] (step=0132600) Train Loss: 0.0918, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 06:17:17] (step=0132700) Train Loss: 0.0848, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:18:03] (step=0132800) Train Loss: 0.0836, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 06:18:49] (step=0132900) Train Loss: 0.0824, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:19:35] (step=0133000) Train Loss: 0.0852, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:20:21] (step=0133100) Train Loss: 0.0890, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 06:21:07] (step=0133200) Train Loss: 0.0904, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 06:21:53] (step=0133300) Train Loss: 0.0831, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:22:02] Beginning epoch 33...
+[2025-05-15 06:22:40] (step=0133400) Train Loss: 0.0825, Train Steps/Sec: 2.16, Samples/Sec: 77.61
+[2025-05-15 06:23:26] (step=0133500) Train Loss: 0.0825, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 06:24:12] (step=0133600) Train Loss: 0.0824, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 06:24:58] (step=0133700) Train Loss: 0.0808, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 06:25:44] (step=0133800) Train Loss: 0.0791, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 06:26:30] (step=0133900) Train Loss: 0.0857, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 06:27:16] (step=0134000) Train Loss: 0.0838, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:27:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 06:28:14] (step=0134100) Train Loss: 0.0927, Train Steps/Sec: 1.72, Samples/Sec: 62.04
+[2025-05-15 06:29:00] (step=0134200) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 06:29:46] (step=0134300) Train Loss: 0.0800, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 06:30:33] (step=0134400) Train Loss: 0.0796, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 06:31:19] (step=0134500) Train Loss: 0.0784, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:32:05] (step=0134600) Train Loss: 0.0781, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:32:51] (step=0134700) Train Loss: 0.0812, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:33:37] (step=0134800) Train Loss: 0.0786, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 06:34:23] (step=0134900) Train Loss: 0.0776, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:35:09] (step=0135000) Train Loss: 0.0802, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:35:55] (step=0135100) Train Loss: 0.0805, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 06:36:41] (step=0135200) Train Loss: 0.0805, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:37:27] (step=0135300) Train Loss: 0.0834, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:38:13] (step=0135400) Train Loss: 0.0858, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:39:00] (step=0135500) Train Loss: 0.0791, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 06:39:46] (step=0135600) Train Loss: 0.0807, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:40:32] (step=0135700) Train Loss: 0.0849, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:41:18] (step=0135800) Train Loss: 0.0795, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 06:42:04] (step=0135900) Train Loss: 0.0826, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:42:50] (step=0136000) Train Loss: 0.0750, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 06:43:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 06:43:48] (step=0136100) Train Loss: 0.0806, Train Steps/Sec: 1.72, Samples/Sec: 62.04
+[2025-05-15 06:44:34] (step=0136200) Train Loss: 0.0751, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 06:45:20] (step=0136300) Train Loss: 0.0796, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 06:46:06] (step=0136400) Train Loss: 0.0856, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 06:46:53] (step=0136500) Train Loss: 0.0803, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 06:47:39] (step=0136600) Train Loss: 0.0801, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:48:25] (step=0136700) Train Loss: 0.0783, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 06:49:11] (step=0136800) Train Loss: 0.0804, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 06:49:57] (step=0136900) Train Loss: 0.0846, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 06:50:43] (step=0137000) Train Loss: 0.0749, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:51:29] (step=0137100) Train Loss: 0.0816, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 06:52:15] (step=0137200) Train Loss: 0.0799, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:53:01] (step=0137300) Train Loss: 0.0761, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 06:53:29] Beginning epoch 34...
+[2025-05-15 06:53:48] (step=0137400) Train Loss: 0.0773, Train Steps/Sec: 2.16, Samples/Sec: 77.75
+[2025-05-15 06:54:34] (step=0137500) Train Loss: 0.0801, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 06:55:20] (step=0137600) Train Loss: 0.0832, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 06:56:06] (step=0137700) Train Loss: 0.0797, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 06:56:52] (step=0137800) Train Loss: 0.0806, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 06:57:38] (step=0137900) Train Loss: 0.0785, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 06:58:24] (step=0138000) Train Loss: 0.0829, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 06:58:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 06:59:22] (step=0138100) Train Loss: 0.0803, Train Steps/Sec: 1.73, Samples/Sec: 62.24
+[2025-05-15 07:00:08] (step=0138200) Train Loss: 0.0843, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 07:00:55] (step=0138300) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 07:01:41] (step=0138400) Train Loss: 0.0763, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 07:02:27] (step=0138500) Train Loss: 0.0763, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 07:03:13] (step=0138600) Train Loss: 0.0797, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 07:03:59] (step=0138700) Train Loss: 0.0765, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:04:45] (step=0138800) Train Loss: 0.0765, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 07:05:31] (step=0138900) Train Loss: 0.0766, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 07:06:17] (step=0139000) Train Loss: 0.0789, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 07:07:03] (step=0139100) Train Loss: 0.0791, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:07:49] (step=0139200) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:08:36] (step=0139300) Train Loss: 0.0773, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 07:09:22] (step=0139400) Train Loss: 0.0799, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:10:08] (step=0139500) Train Loss: 0.0792, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 07:10:54] (step=0139600) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 07:11:40] (step=0139700) Train Loss: 0.0809, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 07:12:26] (step=0139800) Train Loss: 0.0798, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 07:13:12] (step=0139900) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 07:13:58] (step=0140000) Train Loss: 0.0798, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 07:14:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0140000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 07:14:25] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 07:16:24] (step=0140000) Perceptual Loss: 0.3118, Eval Time: 122.36
+[2025-05-15 07:17:11] (step=0140100) Train Loss: 0.0784, Train Steps/Sec: 0.52, Samples/Sec: 18.71
+[2025-05-15 07:17:57] (step=0140200) Train Loss: 0.0775, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:18:43] (step=0140300) Train Loss: 0.0765, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 07:19:29] (step=0140400) Train Loss: 0.0808, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 07:20:15] (step=0140500) Train Loss: 0.0775, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 07:21:01] (step=0140600) Train Loss: 0.0762, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 07:21:47] (step=0140700) Train Loss: 0.0777, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 07:22:34] (step=0140800) Train Loss: 0.0785, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 07:23:20] (step=0140900) Train Loss: 0.0818, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 07:24:06] (step=0141000) Train Loss: 0.0775, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 07:24:52] (step=0141100) Train Loss: 0.0854, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:25:38] (step=0141200) Train Loss: 0.0768, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 07:26:24] (step=0141300) Train Loss: 0.0833, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 07:27:10] (step=0141400) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 07:27:10] Beginning epoch 35...
+[2025-05-15 07:27:57] (step=0141500) Train Loss: 0.0776, Train Steps/Sec: 2.15, Samples/Sec: 77.51
+[2025-05-15 07:28:43] (step=0141600) Train Loss: 0.0789, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 07:29:29] (step=0141700) Train Loss: 0.0783, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 07:30:15] (step=0141800) Train Loss: 0.0769, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 07:31:01] (step=0141900) Train Loss: 0.0796, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 07:31:47] (step=0142000) Train Loss: 0.0815, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 07:31:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 07:32:45] (step=0142100) Train Loss: 0.0818, Train Steps/Sec: 1.72, Samples/Sec: 61.88
+[2025-05-15 07:33:32] (step=0142200) Train Loss: 0.0728, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-15 07:34:18] (step=0142300) Train Loss: 0.0816, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 07:35:04] (step=0142400) Train Loss: 0.0766, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-15 07:35:50] (step=0142500) Train Loss: 0.0776, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 07:36:36] (step=0142600) Train Loss: 0.0774, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-15 07:37:22] (step=0142700) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:38:08] (step=0142800) Train Loss: 0.0738, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 07:38:55] (step=0142900) Train Loss: 0.0783, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 07:39:41] (step=0143000) Train Loss: 0.0774, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 07:40:27] (step=0143100) Train Loss: 0.0763, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:41:13] (step=0143200) Train Loss: 0.0689, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:41:59] (step=0143300) Train Loss: 0.0761, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 07:42:45] (step=0143400) Train Loss: 0.0797, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 07:43:31] (step=0143500) Train Loss: 0.0773, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 07:44:17] (step=0143600) Train Loss: 0.0770, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 07:45:03] (step=0143700) Train Loss: 0.0840, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 07:45:49] (step=0143800) Train Loss: 0.0805, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:46:36] (step=0143900) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:47:22] (step=0144000) Train Loss: 0.0747, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:47:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 07:48:20] (step=0144100) Train Loss: 0.0777, Train Steps/Sec: 1.71, Samples/Sec: 61.38
+[2025-05-15 07:49:06] (step=0144200) Train Loss: 0.0780, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 07:49:53] (step=0144300) Train Loss: 0.0727, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 07:50:39] (step=0144400) Train Loss: 0.0696, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 07:51:25] (step=0144500) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:52:11] (step=0144600) Train Loss: 0.0781, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:52:57] (step=0144700) Train Loss: 0.0881, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:53:43] (step=0144800) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 07:54:29] (step=0144900) Train Loss: 0.0811, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-15 07:55:15] (step=0145000) Train Loss: 0.0776, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:56:01] (step=0145100) Train Loss: 0.0829, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 07:56:48] (step=0145200) Train Loss: 0.0718, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:57:34] (step=0145300) Train Loss: 0.0808, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:58:20] (step=0145400) Train Loss: 0.0798, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 07:58:38] Beginning epoch 36...
+[2025-05-15 07:59:06] (step=0145500) Train Loss: 0.0791, Train Steps/Sec: 2.15, Samples/Sec: 77.27
+[2025-05-15 07:59:52] (step=0145600) Train Loss: 0.0743, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:00:38] (step=0145700) Train Loss: 0.0806, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 08:01:24] (step=0145800) Train Loss: 0.0749, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 08:02:11] (step=0145900) Train Loss: 0.0765, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 08:02:57] (step=0146000) Train Loss: 0.0734, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:03:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 08:03:55] (step=0146100) Train Loss: 0.0793, Train Steps/Sec: 1.72, Samples/Sec: 62.02
+[2025-05-15 08:04:41] (step=0146200) Train Loss: 0.0769, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 08:05:27] (step=0146300) Train Loss: 0.0774, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 08:06:13] (step=0146400) Train Loss: 0.0728, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 08:06:59] (step=0146500) Train Loss: 0.0755, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 08:07:45] (step=0146600) Train Loss: 0.0750, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 08:08:31] (step=0146700) Train Loss: 0.0824, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 08:09:17] (step=0146800) Train Loss: 0.0764, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 08:10:04] (step=0146900) Train Loss: 0.0777, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 08:10:50] (step=0147000) Train Loss: 0.0794, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 08:11:36] (step=0147100) Train Loss: 0.0781, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 08:12:22] (step=0147200) Train Loss: 0.0762, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 08:13:08] (step=0147300) Train Loss: 0.0787, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 08:13:54] (step=0147400) Train Loss: 0.0796, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 08:14:40] (step=0147500) Train Loss: 0.0779, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 08:15:26] (step=0147600) Train Loss: 0.0803, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 08:16:12] (step=0147700) Train Loss: 0.0791, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 08:16:58] (step=0147800) Train Loss: 0.0782, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 08:17:44] (step=0147900) Train Loss: 0.0741, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:18:31] (step=0148000) Train Loss: 0.0824, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:18:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 08:19:29] (step=0148100) Train Loss: 0.0731, Train Steps/Sec: 1.72, Samples/Sec: 62.01
+[2025-05-15 08:20:15] (step=0148200) Train Loss: 0.0762, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 08:21:01] (step=0148300) Train Loss: 0.0767, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 08:21:47] (step=0148400) Train Loss: 0.0780, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:22:33] (step=0148500) Train Loss: 0.0788, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:23:19] (step=0148600) Train Loss: 0.0835, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:24:05] (step=0148700) Train Loss: 0.0790, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:24:51] (step=0148800) Train Loss: 0.0761, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 08:25:38] (step=0148900) Train Loss: 0.0770, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 08:26:24] (step=0149000) Train Loss: 0.0801, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:27:10] (step=0149100) Train Loss: 0.0797, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:27:56] (step=0149200) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 08:28:42] (step=0149300) Train Loss: 0.0766, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:29:28] (step=0149400) Train Loss: 0.0826, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:30:05] Beginning epoch 37...
+[2025-05-15 08:30:15] (step=0149500) Train Loss: 0.0726, Train Steps/Sec: 2.15, Samples/Sec: 77.46
+[2025-05-15 08:31:01] (step=0149600) Train Loss: 0.0742, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 08:31:47] (step=0149700) Train Loss: 0.0697, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:32:33] (step=0149800) Train Loss: 0.0760, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:33:19] (step=0149900) Train Loss: 0.0778, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 08:34:05] (step=0150000) Train Loss: 0.0771, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 08:34:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 08:34:20] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 08:36:19] (step=0150000) Perceptual Loss: 0.2873, Eval Time: 122.27
+[2025-05-15 08:37:06] (step=0150100) Train Loss: 0.0733, Train Steps/Sec: 0.55, Samples/Sec: 19.92
+[2025-05-15 08:37:52] (step=0150200) Train Loss: 0.0743, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 08:38:38] (step=0150300) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 08:39:24] (step=0150400) Train Loss: 0.0806, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 08:40:10] (step=0150500) Train Loss: 0.0721, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:40:56] (step=0150600) Train Loss: 0.0794, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:41:42] (step=0150700) Train Loss: 0.0736, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 08:42:29] (step=0150800) Train Loss: 0.0761, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 08:43:15] (step=0150900) Train Loss: 0.0732, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 08:44:01] (step=0151000) Train Loss: 0.0772, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:44:47] (step=0151100) Train Loss: 0.0784, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 08:45:33] (step=0151200) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:46:19] (step=0151300) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:47:05] (step=0151400) Train Loss: 0.0806, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 08:47:51] (step=0151500) Train Loss: 0.0754, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 08:48:37] (step=0151600) Train Loss: 0.0705, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 08:49:23] (step=0151700) Train Loss: 0.0830, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 08:50:10] (step=0151800) Train Loss: 0.0742, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 08:50:56] (step=0151900) Train Loss: 0.0780, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 08:51:42] (step=0152000) Train Loss: 0.0699, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 08:51:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 08:52:40] (step=0152100) Train Loss: 0.0780, Train Steps/Sec: 1.72, Samples/Sec: 61.88
+[2025-05-15 08:53:26] (step=0152200) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 08:54:12] (step=0152300) Train Loss: 0.0734, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 08:54:58] (step=0152400) Train Loss: 0.1960, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 08:55:44] (step=0152500) Train Loss: 0.2017, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 08:56:30] (step=0152600) Train Loss: 0.1637, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 08:57:16] (step=0152700) Train Loss: 0.0912, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 08:58:03] (step=0152800) Train Loss: 0.0865, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-15 08:58:49] (step=0152900) Train Loss: 0.0829, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 08:59:35] (step=0153000) Train Loss: 0.0767, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 09:00:21] (step=0153100) Train Loss: 0.0750, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 09:01:07] (step=0153200) Train Loss: 0.0713, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:01:53] (step=0153300) Train Loss: 0.0705, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 09:02:39] (step=0153400) Train Loss: 0.0716, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 09:03:25] (step=0153500) Train Loss: 0.0801, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 09:03:34] Beginning epoch 38...
+[2025-05-15 09:04:12] (step=0153600) Train Loss: 0.0763, Train Steps/Sec: 2.16, Samples/Sec: 77.65
+[2025-05-15 09:04:58] (step=0153700) Train Loss: 0.0707, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 09:05:44] (step=0153800) Train Loss: 0.0735, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 09:06:30] (step=0153900) Train Loss: 0.0723, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:07:16] (step=0154000) Train Loss: 0.0761, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 09:07:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 09:08:14] (step=0154100) Train Loss: 0.0740, Train Steps/Sec: 1.72, Samples/Sec: 61.89
+[2025-05-15 09:09:00] (step=0154200) Train Loss: 0.0686, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 09:09:46] (step=0154300) Train Loss: 0.0746, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:10:32] (step=0154400) Train Loss: 0.0740, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 09:11:18] (step=0154500) Train Loss: 0.0746, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 09:12:04] (step=0154600) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:12:51] (step=0154700) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 09:13:37] (step=0154800) Train Loss: 0.0705, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 09:14:23] (step=0154900) Train Loss: 0.0801, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 09:15:09] (step=0155000) Train Loss: 0.0767, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 09:15:55] (step=0155100) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 09:16:41] (step=0155200) Train Loss: 0.0775, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:17:27] (step=0155300) Train Loss: 0.0740, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:18:13] (step=0155400) Train Loss: 0.0819, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 09:19:00] (step=0155500) Train Loss: 0.0814, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 09:19:46] (step=0155600) Train Loss: 0.0684, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 09:20:32] (step=0155700) Train Loss: 0.0734, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 09:21:18] (step=0155800) Train Loss: 0.0794, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:22:04] (step=0155900) Train Loss: 0.0774, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 09:22:50] (step=0156000) Train Loss: 0.0768, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 09:23:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 09:23:48] (step=0156100) Train Loss: 0.0759, Train Steps/Sec: 1.71, Samples/Sec: 61.56
+[2025-05-15 09:24:34] (step=0156200) Train Loss: 0.0779, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 09:25:21] (step=0156300) Train Loss: 0.0792, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 09:26:07] (step=0156400) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 09:26:53] (step=0156500) Train Loss: 0.0761, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 09:27:39] (step=0156600) Train Loss: 0.0773, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 09:28:25] (step=0156700) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 09:29:11] (step=0156800) Train Loss: 0.0754, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-15 09:29:57] (step=0156900) Train Loss: 0.0779, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 09:30:43] (step=0157000) Train Loss: 0.0811, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 09:31:29] (step=0157100) Train Loss: 0.0756, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 09:32:16] (step=0157200) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 09:33:02] (step=0157300) Train Loss: 0.0738, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 09:33:48] (step=0157400) Train Loss: 0.0746, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 09:34:34] (step=0157500) Train Loss: 0.0810, Train Steps/Sec: 2.16, Samples/Sec: 77.76
+[2025-05-15 09:35:02] Beginning epoch 39...
+[2025-05-15 09:35:21] (step=0157600) Train Loss: 0.0758, Train Steps/Sec: 2.14, Samples/Sec: 77.19
+[2025-05-15 09:36:07] (step=0157700) Train Loss: 0.0757, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-15 09:36:53] (step=0157800) Train Loss: 0.0728, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-15 09:37:39] (step=0157900) Train Loss: 0.0753, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 09:38:25] (step=0158000) Train Loss: 0.0737, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 09:38:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 09:39:24] (step=0158100) Train Loss: 0.0750, Train Steps/Sec: 1.70, Samples/Sec: 61.18
+[2025-05-15 09:40:10] (step=0158200) Train Loss: 0.0762, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 09:40:57] (step=0158300) Train Loss: 0.0811, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 09:41:43] (step=0158400) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 09:42:29] (step=0158500) Train Loss: 0.0742, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 09:43:15] (step=0158600) Train Loss: 0.0711, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:44:01] (step=0158700) Train Loss: 0.0715, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 09:44:47] (step=0158800) Train Loss: 0.0726, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 09:45:33] (step=0158900) Train Loss: 0.0772, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 09:46:19] (step=0159000) Train Loss: 0.0743, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 09:47:05] (step=0159100) Train Loss: 0.0799, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 09:47:51] (step=0159200) Train Loss: 0.0778, Train Steps/Sec: 2.17, Samples/Sec: 78.19
+[2025-05-15 09:48:37] (step=0159300) Train Loss: 0.0674, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 09:49:23] (step=0159400) Train Loss: 0.0735, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 09:50:10] (step=0159500) Train Loss: 0.0744, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 09:50:56] (step=0159600) Train Loss: 0.0725, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 09:51:42] (step=0159700) Train Loss: 0.0770, Train Steps/Sec: 2.17, Samples/Sec: 78.18
+[2025-05-15 09:52:28] (step=0159800) Train Loss: 0.0716, Train Steps/Sec: 2.17, Samples/Sec: 78.17
+[2025-05-15 09:53:14] (step=0159900) Train Loss: 0.0743, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 09:54:00] (step=0160000) Train Loss: 0.0755, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 09:54:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0160000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 09:54:28] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 09:56:27] (step=0160000) Perceptual Loss: 0.2955, Eval Time: 122.53
+[2025-05-15 09:57:14] (step=0160100) Train Loss: 0.0733, Train Steps/Sec: 0.52, Samples/Sec: 18.59
+[2025-05-15 09:58:00] (step=0160200) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 09:58:46] (step=0160300) Train Loss: 0.0745, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 09:59:32] (step=0160400) Train Loss: 0.0709, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 10:00:18] (step=0160500) Train Loss: 0.0774, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 10:01:04] (step=0160600) Train Loss: 0.0747, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 10:01:50] (step=0160700) Train Loss: 0.0713, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 10:02:37] (step=0160800) Train Loss: 0.0735, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 10:03:23] (step=0160900) Train Loss: 0.1084, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-15 10:04:09] (step=0161000) Train Loss: 0.1558, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-15 10:04:55] (step=0161100) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 10:05:41] (step=0161200) Train Loss: 0.0819, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:06:27] (step=0161300) Train Loss: 0.0787, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:07:13] (step=0161400) Train Loss: 0.0748, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 10:08:00] (step=0161500) Train Loss: 0.0734, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 10:08:46] (step=0161600) Train Loss: 0.0722, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 10:08:46] Beginning epoch 40...
+[2025-05-15 10:09:32] (step=0161700) Train Loss: 0.0743, Train Steps/Sec: 2.15, Samples/Sec: 77.56
+[2025-05-15 10:10:18] (step=0161800) Train Loss: 0.0756, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:11:04] (step=0161900) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 10:11:50] (step=0162000) Train Loss: 0.0778, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 10:12:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 10:12:49] (step=0162100) Train Loss: 0.0740, Train Steps/Sec: 1.71, Samples/Sec: 61.71
+[2025-05-15 10:13:35] (step=0162200) Train Loss: 0.0743, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 10:14:21] (step=0162300) Train Loss: 0.0682, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 10:15:07] (step=0162400) Train Loss: 0.0765, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:15:53] (step=0162500) Train Loss: 0.0730, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:16:39] (step=0162600) Train Loss: 0.0765, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:17:25] (step=0162700) Train Loss: 0.0766, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 10:18:12] (step=0162800) Train Loss: 0.0746, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 10:18:58] (step=0162900) Train Loss: 0.0735, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:19:44] (step=0163000) Train Loss: 0.0781, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:20:30] (step=0163100) Train Loss: 0.0771, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:21:16] (step=0163200) Train Loss: 0.0694, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:22:02] (step=0163300) Train Loss: 0.0753, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:22:48] (step=0163400) Train Loss: 0.0693, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-15 10:23:34] (step=0163500) Train Loss: 0.0727, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 10:24:20] (step=0163600) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 10:25:07] (step=0163700) Train Loss: 0.0721, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:25:53] (step=0163800) Train Loss: 0.0740, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:26:39] (step=0163900) Train Loss: 0.0707, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:27:25] (step=0164000) Train Loss: 0.0718, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:27:37] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 10:28:23] (step=0164100) Train Loss: 0.0806, Train Steps/Sec: 1.71, Samples/Sec: 61.39
+[2025-05-15 10:29:10] (step=0164200) Train Loss: 0.0720, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 10:29:56] (step=0164300) Train Loss: 0.0705, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 10:30:42] (step=0164400) Train Loss: 0.0704, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 10:31:28] (step=0164500) Train Loss: 0.0688, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 10:32:14] (step=0164600) Train Loss: 0.0729, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 10:33:00] (step=0164700) Train Loss: 0.0699, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 10:33:46] (step=0164800) Train Loss: 0.0726, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 10:34:32] (step=0164900) Train Loss: 0.0721, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 10:35:18] (step=0165000) Train Loss: 0.0789, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 10:36:04] (step=0165100) Train Loss: 0.0765, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 10:36:50] (step=0165200) Train Loss: 0.0742, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 10:37:37] (step=0165300) Train Loss: 0.0741, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 10:38:23] (step=0165400) Train Loss: 0.0701, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 10:39:09] (step=0165500) Train Loss: 0.0734, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 10:39:55] (step=0165600) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 10:40:13] Beginning epoch 41...
+[2025-05-15 10:40:41] (step=0165700) Train Loss: 0.0762, Train Steps/Sec: 2.16, Samples/Sec: 77.63
+[2025-05-15 10:41:27] (step=0165800) Train Loss: 0.0769, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:42:14] (step=0165900) Train Loss: 0.0705, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:43:00] (step=0166000) Train Loss: 0.0720, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 10:43:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 10:43:59] (step=0166100) Train Loss: 0.0713, Train Steps/Sec: 1.69, Samples/Sec: 60.92
+[2025-05-15 10:44:45] (step=0166200) Train Loss: 0.0706, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 10:45:31] (step=0166300) Train Loss: 0.0747, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-15 10:46:17] (step=0166400) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-15 10:47:03] (step=0166500) Train Loss: 0.0726, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 10:47:49] (step=0166600) Train Loss: 0.0712, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:48:35] (step=0166700) Train Loss: 0.0669, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 10:49:22] (step=0166800) Train Loss: 0.0738, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-15 10:50:08] (step=0166900) Train Loss: 0.0754, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 10:50:54] (step=0167000) Train Loss: 0.0728, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 10:51:40] (step=0167100) Train Loss: 0.0755, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:52:26] (step=0167200) Train Loss: 0.0782, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 10:53:12] (step=0167300) Train Loss: 0.0767, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 10:53:59] (step=0167400) Train Loss: 0.0804, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 10:54:45] (step=0167500) Train Loss: 0.0738, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 10:55:31] (step=0167600) Train Loss: 0.0695, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 10:56:17] (step=0167700) Train Loss: 0.0725, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 10:57:03] (step=0167800) Train Loss: 0.0778, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 10:57:49] (step=0167900) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 10:58:35] (step=0168000) Train Loss: 0.0793, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 10:58:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 10:59:34] (step=0168100) Train Loss: 0.0713, Train Steps/Sec: 1.70, Samples/Sec: 61.15
+[2025-05-15 11:00:20] (step=0168200) Train Loss: 0.0762, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 11:01:06] (step=0168300) Train Loss: 0.0735, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:01:52] (step=0168400) Train Loss: 0.0749, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 11:02:39] (step=0168500) Train Loss: 0.0691, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 11:03:25] (step=0168600) Train Loss: 0.0724, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:04:11] (step=0168700) Train Loss: 0.0731, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 11:04:57] (step=0168800) Train Loss: 0.0726, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 11:05:43] (step=0168900) Train Loss: 0.0731, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 11:06:29] (step=0169000) Train Loss: 0.0762, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:07:15] (step=0169100) Train Loss: 0.0702, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:08:01] (step=0169200) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:08:47] (step=0169300) Train Loss: 0.0768, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:09:33] (step=0169400) Train Loss: 0.0757, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 11:10:20] (step=0169500) Train Loss: 0.0714, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 11:11:06] (step=0169600) Train Loss: 0.0735, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 11:11:43] Beginning epoch 42...
+[2025-05-15 11:11:52] (step=0169700) Train Loss: 0.0723, Train Steps/Sec: 2.16, Samples/Sec: 77.69
+[2025-05-15 11:12:38] (step=0169800) Train Loss: 0.0765, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:13:24] (step=0169900) Train Loss: 0.0733, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 11:14:10] (step=0170000) Train Loss: 0.0696, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 11:14:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 11:14:25] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 11:16:25] (step=0170000) Perceptual Loss: 0.3114, Eval Time: 122.12
+[2025-05-15 11:17:11] (step=0170100) Train Loss: 0.0699, Train Steps/Sec: 0.55, Samples/Sec: 19.92
+[2025-05-15 11:17:57] (step=0170200) Train Loss: 0.0725, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:18:43] (step=0170300) Train Loss: 0.0719, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:19:29] (step=0170400) Train Loss: 0.0768, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 11:20:15] (step=0170500) Train Loss: 0.0712, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 11:21:01] (step=0170600) Train Loss: 0.0737, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:21:48] (step=0170700) Train Loss: 0.0770, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 11:22:34] (step=0170800) Train Loss: 0.0684, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 11:23:20] (step=0170900) Train Loss: 0.0704, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:24:06] (step=0171000) Train Loss: 0.0786, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:24:52] (step=0171100) Train Loss: 0.0728, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:25:38] (step=0171200) Train Loss: 0.0691, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:26:24] (step=0171300) Train Loss: 0.0716, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:27:10] (step=0171400) Train Loss: 0.0746, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 11:27:56] (step=0171500) Train Loss: 0.0714, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 11:28:43] (step=0171600) Train Loss: 0.0775, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:29:29] (step=0171700) Train Loss: 0.0772, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:30:15] (step=0171800) Train Loss: 0.0757, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:31:01] (step=0171900) Train Loss: 0.0681, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:31:47] (step=0172000) Train Loss: 0.0692, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:31:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 11:32:45] (step=0172100) Train Loss: 0.0723, Train Steps/Sec: 1.71, Samples/Sec: 61.48
+[2025-05-15 11:33:32] (step=0172200) Train Loss: 0.0747, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 11:34:18] (step=0172300) Train Loss: 0.0712, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:35:04] (step=0172400) Train Loss: 0.0773, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:35:50] (step=0172500) Train Loss: 0.0710, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:36:36] (step=0172600) Train Loss: 0.0729, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 11:37:22] (step=0172700) Train Loss: 0.0714, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 11:38:08] (step=0172800) Train Loss: 0.0743, Train Steps/Sec: 2.16, Samples/Sec: 77.74
+[2025-05-15 11:38:54] (step=0172900) Train Loss: 0.0735, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 11:39:40] (step=0173000) Train Loss: 0.0768, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 11:40:26] (step=0173100) Train Loss: 0.0671, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 11:41:13] (step=0173200) Train Loss: 0.0688, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:41:59] (step=0173300) Train Loss: 0.0722, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:42:45] (step=0173400) Train Loss: 0.0762, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 11:43:31] (step=0173500) Train Loss: 0.0691, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 11:44:17] (step=0173600) Train Loss: 0.0724, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:45:03] (step=0173700) Train Loss: 0.0718, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:45:12] Beginning epoch 43...
+[2025-05-15 11:45:49] (step=0173800) Train Loss: 0.0687, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-05-15 11:46:36] (step=0173900) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:47:22] (step=0174000) Train Loss: 0.0715, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:47:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 11:48:20] (step=0174100) Train Loss: 0.0685, Train Steps/Sec: 1.71, Samples/Sec: 61.45
+[2025-05-15 11:49:06] (step=0174200) Train Loss: 0.0748, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 11:49:52] (step=0174300) Train Loss: 0.0736, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 11:50:38] (step=0174400) Train Loss: 0.0761, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 11:51:25] (step=0174500) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 11:52:11] (step=0174600) Train Loss: 0.0679, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:52:57] (step=0174700) Train Loss: 0.0749, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 11:53:43] (step=0174800) Train Loss: 0.0689, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 11:54:29] (step=0174900) Train Loss: 0.0713, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 11:55:15] (step=0175000) Train Loss: 0.0767, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:56:01] (step=0175100) Train Loss: 0.0673, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:56:47] (step=0175200) Train Loss: 0.0734, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 11:57:33] (step=0175300) Train Loss: 0.0704, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 11:58:20] (step=0175400) Train Loss: 0.0744, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 11:59:06] (step=0175500) Train Loss: 0.0688, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 11:59:52] (step=0175600) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:00:38] (step=0175700) Train Loss: 0.0712, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:01:24] (step=0175800) Train Loss: 0.0686, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 12:02:10] (step=0175900) Train Loss: 0.0694, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 12:02:56] (step=0176000) Train Loss: 0.0766, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 12:03:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 12:03:55] (step=0176100) Train Loss: 0.0735, Train Steps/Sec: 1.71, Samples/Sec: 61.43
+[2025-05-15 12:04:41] (step=0176200) Train Loss: 0.0701, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 12:05:27] (step=0176300) Train Loss: 0.0726, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 12:06:13] (step=0176400) Train Loss: 0.0766, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 12:06:59] (step=0176500) Train Loss: 0.0702, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 12:07:45] (step=0176600) Train Loss: 0.0712, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:08:31] (step=0176700) Train Loss: 0.0748, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:09:18] (step=0176800) Train Loss: 0.0721, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 12:10:04] (step=0176900) Train Loss: 0.0704, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 12:10:50] (step=0177000) Train Loss: 0.0722, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:11:36] (step=0177100) Train Loss: 0.0725, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:12:22] (step=0177200) Train Loss: 0.0722, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:13:08] (step=0177300) Train Loss: 0.0641, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 12:13:54] (step=0177400) Train Loss: 0.0749, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 12:14:40] (step=0177500) Train Loss: 0.0742, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 12:15:26] (step=0177600) Train Loss: 0.0661, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 12:16:12] (step=0177700) Train Loss: 0.0701, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:16:40] Beginning epoch 44...
+[2025-05-15 12:16:59] (step=0177800) Train Loss: 0.0709, Train Steps/Sec: 2.16, Samples/Sec: 77.64
+[2025-05-15 12:17:45] (step=0177900) Train Loss: 0.0728, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:18:31] (step=0178000) Train Loss: 0.0684, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:18:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 12:19:30] (step=0178100) Train Loss: 0.0733, Train Steps/Sec: 1.71, Samples/Sec: 61.39
+[2025-05-15 12:20:16] (step=0178200) Train Loss: 0.0703, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 12:21:02] (step=0178300) Train Loss: 0.0732, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:21:48] (step=0178400) Train Loss: 0.0726, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:22:34] (step=0178500) Train Loss: 0.0744, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 12:23:20] (step=0178600) Train Loss: 0.0704, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 12:24:06] (step=0178700) Train Loss: 0.0660, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 12:24:53] (step=0178800) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 12:25:39] (step=0178900) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 12:26:25] (step=0179000) Train Loss: 0.0746, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:27:11] (step=0179100) Train Loss: 0.0683, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 12:27:57] (step=0179200) Train Loss: 0.0714, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:28:43] (step=0179300) Train Loss: 0.0712, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:29:29] (step=0179400) Train Loss: 0.0731, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 12:30:15] (step=0179500) Train Loss: 0.0677, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 12:31:02] (step=0179600) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 12:31:48] (step=0179700) Train Loss: 0.0714, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 12:32:34] (step=0179800) Train Loss: 0.0775, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 12:33:20] (step=0179900) Train Loss: 0.0669, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 12:34:06] (step=0180000) Train Loss: 0.0764, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 12:34:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0180000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 12:34:33] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 12:36:32] (step=0180000) Perceptual Loss: 0.2853, Eval Time: 122.42
+[2025-05-15 12:37:19] (step=0180100) Train Loss: 0.0686, Train Steps/Sec: 0.52, Samples/Sec: 18.68
+[2025-05-15 12:38:05] (step=0180200) Train Loss: 0.0745, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-15 12:38:51] (step=0180300) Train Loss: 0.0754, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-15 12:39:37] (step=0180400) Train Loss: 0.0729, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 12:40:23] (step=0180500) Train Loss: 0.0700, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 12:41:09] (step=0180600) Train Loss: 0.0710, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:41:55] (step=0180700) Train Loss: 0.0711, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 12:42:42] (step=0180800) Train Loss: 0.0678, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 12:43:28] (step=0180900) Train Loss: 0.0732, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:44:14] (step=0181000) Train Loss: 0.0696, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 12:45:00] (step=0181100) Train Loss: 0.0766, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:45:46] (step=0181200) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 12:46:32] (step=0181300) Train Loss: 0.0760, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:47:18] (step=0181400) Train Loss: 0.0702, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 12:48:04] (step=0181500) Train Loss: 0.0698, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 12:48:51] (step=0181600) Train Loss: 0.0676, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 12:49:37] (step=0181700) Train Loss: 0.0735, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 12:50:23] (step=0181800) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 12:50:23] Beginning epoch 45...
+[2025-05-15 12:51:09] (step=0181900) Train Loss: 0.0725, Train Steps/Sec: 2.16, Samples/Sec: 77.64
+[2025-05-15 12:51:55] (step=0182000) Train Loss: 0.0744, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-15 12:52:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 12:52:54] (step=0182100) Train Loss: 0.0665, Train Steps/Sec: 1.70, Samples/Sec: 61.28
+[2025-05-15 12:53:40] (step=0182200) Train Loss: 0.0741, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 12:54:26] (step=0182300) Train Loss: 0.0699, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 12:55:12] (step=0182400) Train Loss: 0.0722, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 12:55:59] (step=0182500) Train Loss: 0.0756, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 12:56:45] (step=0182600) Train Loss: 0.0697, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 12:57:31] (step=0182700) Train Loss: 0.0732, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 12:58:17] (step=0182800) Train Loss: 0.0753, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-05-15 12:59:03] (step=0182900) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 12:59:50] (step=0183000) Train Loss: 0.0752, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 13:00:36] (step=0183100) Train Loss: 0.0696, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 13:01:22] (step=0183200) Train Loss: 0.0723, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:02:08] (step=0183300) Train Loss: 0.0724, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 13:02:54] (step=0183400) Train Loss: 0.0687, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 13:03:40] (step=0183500) Train Loss: 0.0721, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 13:04:26] (step=0183600) Train Loss: 0.0709, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 13:05:13] (step=0183700) Train Loss: 0.0684, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 13:05:59] (step=0183800) Train Loss: 0.0663, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:06:45] (step=0183900) Train Loss: 0.0704, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:07:31] (step=0184000) Train Loss: 0.0686, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:07:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 13:08:30] (step=0184100) Train Loss: 0.0708, Train Steps/Sec: 1.68, Samples/Sec: 60.57
+[2025-05-15 13:09:16] (step=0184200) Train Loss: 0.0685, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 13:10:02] (step=0184300) Train Loss: 0.0688, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:10:49] (step=0184400) Train Loss: 0.0658, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:11:35] (step=0184500) Train Loss: 0.0733, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 13:12:21] (step=0184600) Train Loss: 0.0681, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 13:13:07] (step=0184700) Train Loss: 0.0738, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 13:13:53] (step=0184800) Train Loss: 0.0748, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 13:14:39] (step=0184900) Train Loss: 0.0701, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 13:15:25] (step=0185000) Train Loss: 0.0707, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 13:16:11] (step=0185100) Train Loss: 0.0679, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:16:57] (step=0185200) Train Loss: 0.0665, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:17:43] (step=0185300) Train Loss: 0.0732, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 13:18:30] (step=0185400) Train Loss: 0.0711, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 13:19:16] (step=0185500) Train Loss: 0.0760, Train Steps/Sec: 2.16, Samples/Sec: 77.89
+[2025-05-15 13:20:02] (step=0185600) Train Loss: 0.0724, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:20:48] (step=0185700) Train Loss: 0.0702, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 13:21:34] (step=0185800) Train Loss: 0.0732, Train Steps/Sec: 2.17, Samples/Sec: 78.15
+[2025-05-15 13:21:52] Beginning epoch 46...
+[2025-05-15 13:22:20] (step=0185900) Train Loss: 0.0712, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-05-15 13:23:06] (step=0186000) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 13:23:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 13:24:05] (step=0186100) Train Loss: 0.0696, Train Steps/Sec: 1.70, Samples/Sec: 61.28
+[2025-05-15 13:24:51] (step=0186200) Train Loss: 0.0678, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 13:25:37] (step=0186300) Train Loss: 0.0697, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 13:26:23] (step=0186400) Train Loss: 0.0659, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 13:27:10] (step=0186500) Train Loss: 0.0633, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 13:27:56] (step=0186600) Train Loss: 0.0689, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 13:28:42] (step=0186700) Train Loss: 0.0742, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 13:29:28] (step=0186800) Train Loss: 0.0688, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 13:30:14] (step=0186900) Train Loss: 0.0760, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 13:31:00] (step=0187000) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 13:31:46] (step=0187100) Train Loss: 0.0687, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 13:32:32] (step=0187200) Train Loss: 0.0722, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 13:33:19] (step=0187300) Train Loss: 0.0689, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 13:34:05] (step=0187400) Train Loss: 0.0706, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 13:34:51] (step=0187500) Train Loss: 0.0713, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 13:35:37] (step=0187600) Train Loss: 0.0714, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 13:36:23] (step=0187700) Train Loss: 0.0734, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 13:37:09] (step=0187800) Train Loss: 0.0730, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 13:37:55] (step=0187900) Train Loss: 0.0706, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 13:38:41] (step=0188000) Train Loss: 0.0657, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 13:38:53] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 13:39:39] (step=0188100) Train Loss: 0.0668, Train Steps/Sec: 1.73, Samples/Sec: 62.23
+[2025-05-15 13:40:26] (step=0188200) Train Loss: 0.0724, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 13:41:12] (step=0188300) Train Loss: 0.0665, Train Steps/Sec: 2.17, Samples/Sec: 78.11
+[2025-05-15 13:41:58] (step=0188400) Train Loss: 0.0702, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 13:42:44] (step=0188500) Train Loss: 0.0709, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 13:43:30] (step=0188600) Train Loss: 0.0689, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 13:44:16] (step=0188700) Train Loss: 0.0709, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 13:45:03] (step=0188800) Train Loss: 0.0739, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-15 13:45:49] (step=0188900) Train Loss: 0.0678, Train Steps/Sec: 2.16, Samples/Sec: 77.80
+[2025-05-15 13:46:35] (step=0189000) Train Loss: 0.0673, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 13:47:21] (step=0189100) Train Loss: 0.0684, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-15 13:48:07] (step=0189200) Train Loss: 0.0733, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 13:48:54] (step=0189300) Train Loss: 0.0759, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 13:49:40] (step=0189400) Train Loss: 0.0661, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 13:50:26] (step=0189500) Train Loss: 0.0701, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 13:51:12] (step=0189600) Train Loss: 0.0657, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-15 13:51:58] (step=0189700) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 13:52:44] (step=0189800) Train Loss: 0.0662, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 13:53:21] Beginning epoch 47...
+[2025-05-15 13:53:31] (step=0189900) Train Loss: 0.0665, Train Steps/Sec: 2.15, Samples/Sec: 77.52
+[2025-05-15 13:54:17] (step=0190000) Train Loss: 0.0727, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 13:54:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 13:54:33] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 13:56:32] (step=0190000) Perceptual Loss: 0.2926, Eval Time: 122.43
+[2025-05-15 13:57:19] (step=0190100) Train Loss: 0.0692, Train Steps/Sec: 0.55, Samples/Sec: 19.83
+[2025-05-15 13:58:05] (step=0190200) Train Loss: 0.0724, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 13:58:51] (step=0190300) Train Loss: 0.0710, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 13:59:37] (step=0190400) Train Loss: 0.0678, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:00:23] (step=0190500) Train Loss: 0.0670, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 14:01:09] (step=0190600) Train Loss: 0.0722, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 14:01:56] (step=0190700) Train Loss: 0.0709, Train Steps/Sec: 2.16, Samples/Sec: 77.66
+[2025-05-15 14:02:42] (step=0190800) Train Loss: 0.0688, Train Steps/Sec: 2.16, Samples/Sec: 77.81
+[2025-05-15 14:03:28] (step=0190900) Train Loss: 0.0737, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-15 14:04:14] (step=0191000) Train Loss: 0.0695, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:05:01] (step=0191100) Train Loss: 0.0699, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:05:47] (step=0191200) Train Loss: 0.0726, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 14:06:33] (step=0191300) Train Loss: 0.0651, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 14:07:19] (step=0191400) Train Loss: 0.0725, Train Steps/Sec: 2.16, Samples/Sec: 77.71
+[2025-05-15 14:08:06] (step=0191500) Train Loss: 0.0686, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-15 14:08:52] (step=0191600) Train Loss: 0.0677, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 14:09:38] (step=0191700) Train Loss: 0.0704, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 14:10:24] (step=0191800) Train Loss: 0.0673, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:11:10] (step=0191900) Train Loss: 0.0672, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 14:11:56] (step=0192000) Train Loss: 0.0686, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:12:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 14:12:55] (step=0192100) Train Loss: 0.0703, Train Steps/Sec: 1.72, Samples/Sec: 61.90
+[2025-05-15 14:13:41] (step=0192200) Train Loss: 0.0699, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 14:14:27] (step=0192300) Train Loss: 0.0645, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:15:13] (step=0192400) Train Loss: 0.0708, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 14:15:59] (step=0192500) Train Loss: 0.0687, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 14:16:46] (step=0192600) Train Loss: 0.0677, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 14:17:32] (step=0192700) Train Loss: 0.0753, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 14:18:18] (step=0192800) Train Loss: 0.0673, Train Steps/Sec: 2.16, Samples/Sec: 77.72
+[2025-05-15 14:19:04] (step=0192900) Train Loss: 0.0660, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:19:50] (step=0193000) Train Loss: 0.0685, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 14:20:37] (step=0193100) Train Loss: 0.0631, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 14:21:23] (step=0193200) Train Loss: 0.0713, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 14:22:09] (step=0193300) Train Loss: 0.0649, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 14:22:55] (step=0193400) Train Loss: 0.0700, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 14:23:41] (step=0193500) Train Loss: 0.0635, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 14:24:27] (step=0193600) Train Loss: 0.0723, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 14:25:14] (step=0193700) Train Loss: 0.0726, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 14:26:00] (step=0193800) Train Loss: 0.0703, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 14:26:46] (step=0193900) Train Loss: 0.0707, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 14:26:55] Beginning epoch 48...
+[2025-05-15 14:27:32] (step=0194000) Train Loss: 0.0704, Train Steps/Sec: 2.16, Samples/Sec: 77.64
+[2025-05-15 14:27:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 14:28:30] (step=0194100) Train Loss: 0.0683, Train Steps/Sec: 1.72, Samples/Sec: 61.87
+[2025-05-15 14:29:17] (step=0194200) Train Loss: 0.0681, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 14:30:03] (step=0194300) Train Loss: 0.0651, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 14:30:49] (step=0194400) Train Loss: 0.0730, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 14:31:35] (step=0194500) Train Loss: 0.0661, Train Steps/Sec: 2.17, Samples/Sec: 78.08
+[2025-05-15 14:32:21] (step=0194600) Train Loss: 0.0728, Train Steps/Sec: 2.17, Samples/Sec: 78.10
+[2025-05-15 14:33:07] (step=0194700) Train Loss: 0.0705, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:33:53] (step=0194800) Train Loss: 0.0702, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 14:34:40] (step=0194900) Train Loss: 0.0741, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-15 14:35:26] (step=0195000) Train Loss: 0.0715, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 14:36:12] (step=0195100) Train Loss: 0.0692, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 14:36:58] (step=0195200) Train Loss: 0.0664, Train Steps/Sec: 2.17, Samples/Sec: 78.06
+[2025-05-15 14:37:44] (step=0195300) Train Loss: 0.0704, Train Steps/Sec: 2.16, Samples/Sec: 77.93
+[2025-05-15 14:38:31] (step=0195400) Train Loss: 0.0649, Train Steps/Sec: 2.16, Samples/Sec: 77.82
+[2025-05-15 14:39:17] (step=0195500) Train Loss: 0.0691, Train Steps/Sec: 2.16, Samples/Sec: 77.69
+[2025-05-15 14:40:03] (step=0195600) Train Loss: 0.0673, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 14:40:49] (step=0195700) Train Loss: 0.0713, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 14:41:35] (step=0195800) Train Loss: 0.0680, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 14:42:22] (step=0195900) Train Loss: 0.0681, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 14:43:08] (step=0196000) Train Loss: 0.0700, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 14:43:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 14:44:06] (step=0196100) Train Loss: 0.0688, Train Steps/Sec: 1.72, Samples/Sec: 61.91
+[2025-05-15 14:44:52] (step=0196200) Train Loss: 0.0640, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 14:45:38] (step=0196300) Train Loss: 0.0665, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 14:46:25] (step=0196400) Train Loss: 0.0725, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 14:47:11] (step=0196500) Train Loss: 0.0671, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 14:47:57] (step=0196600) Train Loss: 0.0646, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 14:48:43] (step=0196700) Train Loss: 0.0723, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-15 14:49:29] (step=0196800) Train Loss: 0.0710, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 14:50:16] (step=0196900) Train Loss: 0.0617, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-15 14:51:02] (step=0197000) Train Loss: 0.0752, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 14:51:48] (step=0197100) Train Loss: 0.0653, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 14:52:34] (step=0197200) Train Loss: 0.0665, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:53:20] (step=0197300) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:54:07] (step=0197400) Train Loss: 0.0689, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 14:54:53] (step=0197500) Train Loss: 0.0715, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 14:55:39] (step=0197600) Train Loss: 0.0678, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 14:56:25] (step=0197700) Train Loss: 0.0688, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 14:57:11] (step=0197800) Train Loss: 0.0709, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 14:57:58] (step=0197900) Train Loss: 0.0689, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 14:58:25] Beginning epoch 49...
+[2025-05-15 14:58:44] (step=0198000) Train Loss: 0.0738, Train Steps/Sec: 2.15, Samples/Sec: 77.53
+[2025-05-15 14:58:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 14:59:43] (step=0198100) Train Loss: 0.0692, Train Steps/Sec: 1.69, Samples/Sec: 60.93
+[2025-05-15 15:00:29] (step=0198200) Train Loss: 0.0702, Train Steps/Sec: 2.16, Samples/Sec: 77.70
+[2025-05-15 15:01:16] (step=0198300) Train Loss: 0.0718, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 15:02:02] (step=0198400) Train Loss: 0.0725, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:02:48] (step=0198500) Train Loss: 0.0712, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 15:03:34] (step=0198600) Train Loss: 0.0671, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+[2025-05-15 15:04:20] (step=0198700) Train Loss: 0.0648, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 15:05:06] (step=0198800) Train Loss: 0.0643, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 15:05:53] (step=0198900) Train Loss: 0.0686, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 15:06:39] (step=0199000) Train Loss: 0.0749, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 15:07:25] (step=0199100) Train Loss: 0.0679, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 15:08:11] (step=0199200) Train Loss: 0.0673, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 15:08:57] (step=0199300) Train Loss: 0.0658, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 15:09:44] (step=0199400) Train Loss: 0.0683, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 15:10:30] (step=0199500) Train Loss: 0.0709, Train Steps/Sec: 2.16, Samples/Sec: 77.78
+[2025-05-15 15:11:16] (step=0199600) Train Loss: 0.0667, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-15 15:12:02] (step=0199700) Train Loss: 0.0661, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 15:12:49] (step=0199800) Train Loss: 0.0718, Train Steps/Sec: 2.16, Samples/Sec: 77.92
+[2025-05-15 15:13:35] (step=0199900) Train Loss: 0.0751, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:14:21] (step=0200000) Train Loss: 0.0621, Train Steps/Sec: 2.17, Samples/Sec: 78.02
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 15:14:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0200000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 15:14:47] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 15:16:47] (step=0200000) Perceptual Loss: 0.2885, Eval Time: 122.34
+[2025-05-15 15:17:33] (step=0200100) Train Loss: 0.0706, Train Steps/Sec: 0.52, Samples/Sec: 18.73
+[2025-05-15 15:18:19] (step=0200200) Train Loss: 0.0679, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-15 15:19:06] (step=0200300) Train Loss: 0.0684, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 15:19:52] (step=0200400) Train Loss: 0.0697, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 15:20:38] (step=0200500) Train Loss: 0.0694, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 15:21:24] (step=0200600) Train Loss: 0.0654, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 15:22:10] (step=0200700) Train Loss: 0.0714, Train Steps/Sec: 2.16, Samples/Sec: 77.70
+[2025-05-15 15:22:57] (step=0200800) Train Loss: 0.0672, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-15 15:23:43] (step=0200900) Train Loss: 0.0682, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 15:24:29] (step=0201000) Train Loss: 0.0708, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 15:25:15] (step=0201100) Train Loss: 0.0702, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 15:26:01] (step=0201200) Train Loss: 0.0756, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 15:26:48] (step=0201300) Train Loss: 0.0689, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 15:27:34] (step=0201400) Train Loss: 0.0663, Train Steps/Sec: 2.16, Samples/Sec: 77.70
+[2025-05-15 15:28:20] (step=0201500) Train Loss: 0.0719, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 15:29:06] (step=0201600) Train Loss: 0.0682, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:29:52] (step=0201700) Train Loss: 0.0739, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 15:30:39] (step=0201800) Train Loss: 0.0646, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 15:31:25] (step=0201900) Train Loss: 0.0733, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 15:32:11] (step=0202000) Train Loss: 0.0699, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 15:32:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 15:32:24] Beginning epoch 50...
+[2025-05-15 15:33:10] (step=0202100) Train Loss: 0.0713, Train Steps/Sec: 1.69, Samples/Sec: 60.95
+[2025-05-15 15:33:56] (step=0202200) Train Loss: 0.0709, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 15:34:42] (step=0202300) Train Loss: 0.0675, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 15:35:28] (step=0202400) Train Loss: 0.0718, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 15:36:15] (step=0202500) Train Loss: 0.0664, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 15:37:01] (step=0202600) Train Loss: 0.0663, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 15:37:47] (step=0202700) Train Loss: 0.0686, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 15:38:33] (step=0202800) Train Loss: 0.0681, Train Steps/Sec: 2.16, Samples/Sec: 77.69
+[2025-05-15 15:39:19] (step=0202900) Train Loss: 0.0691, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 15:40:06] (step=0203000) Train Loss: 0.0694, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 15:40:52] (step=0203100) Train Loss: 0.0698, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 15:41:38] (step=0203200) Train Loss: 0.0708, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 15:42:24] (step=0203300) Train Loss: 0.0646, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 15:43:11] (step=0203400) Train Loss: 0.0737, Train Steps/Sec: 2.16, Samples/Sec: 77.71
+[2025-05-15 15:43:57] (step=0203500) Train Loss: 0.0677, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 15:44:43] (step=0203600) Train Loss: 0.0724, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 15:45:29] (step=0203700) Train Loss: 0.0637, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 15:46:15] (step=0203800) Train Loss: 0.0670, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 15:47:01] (step=0203900) Train Loss: 0.0665, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 15:47:47] (step=0204000) Train Loss: 0.0696, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 15:47:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 15:48:45] (step=0204100) Train Loss: 0.0750, Train Steps/Sec: 1.72, Samples/Sec: 61.91
+[2025-05-15 15:49:32] (step=0204200) Train Loss: 0.0653, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 15:50:18] (step=0204300) Train Loss: 0.0633, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:51:04] (step=0204400) Train Loss: 0.0642, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:51:50] (step=0204500) Train Loss: 0.0662, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:52:36] (step=0204600) Train Loss: 0.0681, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 15:53:23] (step=0204700) Train Loss: 0.0691, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 15:54:09] (step=0204800) Train Loss: 0.0614, Train Steps/Sec: 2.16, Samples/Sec: 77.73
+[2025-05-15 15:54:55] (step=0204900) Train Loss: 0.0681, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 15:55:41] (step=0205000) Train Loss: 0.0694, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:56:27] (step=0205100) Train Loss: 0.0655, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:57:14] (step=0205200) Train Loss: 0.0671, Train Steps/Sec: 2.17, Samples/Sec: 77.99
+[2025-05-15 15:58:00] (step=0205300) Train Loss: 0.0661, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 15:58:46] (step=0205400) Train Loss: 0.0674, Train Steps/Sec: 2.16, Samples/Sec: 77.85
+[2025-05-15 15:59:32] (step=0205500) Train Loss: 0.0686, Train Steps/Sec: 2.16, Samples/Sec: 77.77
+[2025-05-15 16:00:18] (step=0205600) Train Loss: 0.0753, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 16:01:05] (step=0205700) Train Loss: 0.0665, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 16:01:51] (step=0205800) Train Loss: 0.0633, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 16:02:37] (step=0205900) Train Loss: 0.0668, Train Steps/Sec: 2.17, Samples/Sec: 78.01
+[2025-05-15 16:03:23] (step=0206000) Train Loss: 0.0704, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 16:03:35] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 16:03:53] Beginning epoch 51...
+[2025-05-15 16:04:21] (step=0206100) Train Loss: 0.0716, Train Steps/Sec: 1.72, Samples/Sec: 61.87
+[2025-05-15 16:05:07] (step=0206200) Train Loss: 0.0675, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 16:05:53] (step=0206300) Train Loss: 0.0709, Train Steps/Sec: 2.17, Samples/Sec: 78.09
+[2025-05-15 16:06:40] (step=0206400) Train Loss: 0.0711, Train Steps/Sec: 2.17, Samples/Sec: 78.13
+[2025-05-15 16:07:26] (step=0206500) Train Loss: 0.0670, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 16:08:12] (step=0206600) Train Loss: 0.0728, Train Steps/Sec: 2.17, Samples/Sec: 78.14
+[2025-05-15 16:08:58] (step=0206700) Train Loss: 0.0626, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 16:09:44] (step=0206800) Train Loss: 0.0728, Train Steps/Sec: 2.16, Samples/Sec: 77.75
+[2025-05-15 16:10:30] (step=0206900) Train Loss: 0.0733, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 16:11:16] (step=0207000) Train Loss: 0.0674, Train Steps/Sec: 2.17, Samples/Sec: 78.00
+[2025-05-15 16:12:03] (step=0207100) Train Loss: 0.0653, Train Steps/Sec: 2.17, Samples/Sec: 77.98
+[2025-05-15 16:12:49] (step=0207200) Train Loss: 0.0697, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 16:13:35] (step=0207300) Train Loss: 0.0693, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 16:14:21] (step=0207400) Train Loss: 0.0652, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 16:15:07] (step=0207500) Train Loss: 0.0730, Train Steps/Sec: 2.16, Samples/Sec: 77.86
+[2025-05-15 16:15:54] (step=0207600) Train Loss: 0.0645, Train Steps/Sec: 2.16, Samples/Sec: 77.91
+[2025-05-15 16:16:40] (step=0207700) Train Loss: 0.0701, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 16:17:26] (step=0207800) Train Loss: 0.0674, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 16:18:12] (step=0207900) Train Loss: 0.0638, Train Steps/Sec: 2.17, Samples/Sec: 78.03
+[2025-05-15 16:18:58] (step=0208000) Train Loss: 0.0614, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 16:19:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 16:19:56] (step=0208100) Train Loss: 0.0661, Train Steps/Sec: 1.72, Samples/Sec: 62.06
+[2025-05-15 16:20:42] (step=0208200) Train Loss: 0.0678, Train Steps/Sec: 2.16, Samples/Sec: 77.88
+[2025-05-15 16:21:29] (step=0208300) Train Loss: 0.0600, Train Steps/Sec: 2.16, Samples/Sec: 77.87
+[2025-05-15 16:22:15] (step=0208400) Train Loss: 0.0680, Train Steps/Sec: 2.17, Samples/Sec: 78.05
+[2025-05-15 16:23:01] (step=0208500) Train Loss: 0.0702, Train Steps/Sec: 2.17, Samples/Sec: 78.04
+[2025-05-15 16:23:47] (step=0208600) Train Loss: 0.0684, Train Steps/Sec: 2.17, Samples/Sec: 78.12
+[2025-05-15 16:24:33] (step=0208700) Train Loss: 0.0661, Train Steps/Sec: 2.17, Samples/Sec: 78.16
+[2025-05-15 16:25:19] (step=0208800) Train Loss: 0.0701, Train Steps/Sec: 2.16, Samples/Sec: 77.90
+[2025-05-15 16:26:05] (step=0208900) Train Loss: 0.0679, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 16:26:52] (step=0209000) Train Loss: 0.0666, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 16:27:38] (step=0209100) Train Loss: 0.0653, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 16:28:24] (step=0209200) Train Loss: 0.0717, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 16:29:10] (step=0209300) Train Loss: 0.0694, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 16:29:56] (step=0209400) Train Loss: 0.0708, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 16:30:43] (step=0209500) Train Loss: 0.0708, Train Steps/Sec: 2.16, Samples/Sec: 77.84
+[2025-05-15 16:31:29] (step=0209600) Train Loss: 0.0650, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-15 16:32:15] (step=0209700) Train Loss: 0.0691, Train Steps/Sec: 2.17, Samples/Sec: 77.96
+[2025-05-15 16:33:01] (step=0209800) Train Loss: 0.0642, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 16:33:47] (step=0209900) Train Loss: 0.0692, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+[2025-05-15 16:34:33] (step=0210000) Train Loss: 0.0617, Train Steps/Sec: 2.17, Samples/Sec: 78.07
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 16:34:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 16:34:48] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 16:36:47] (step=0210000) Perceptual Loss: 0.3152, Eval Time: 122.15
+[2025-05-15 16:37:25] Beginning epoch 52...
+[2025-05-15 16:37:34] (step=0210100) Train Loss: 0.0647, Train Steps/Sec: 0.55, Samples/Sec: 19.94
+[2025-05-15 16:38:20] (step=0210200) Train Loss: 0.0622, Train Steps/Sec: 2.16, Samples/Sec: 77.83
+[2025-05-15 16:39:06] (step=0210300) Train Loss: 0.0647, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 16:39:53] (step=0210400) Train Loss: 0.0668, Train Steps/Sec: 2.17, Samples/Sec: 77.97
+[2025-05-15 16:40:39] (step=0210500) Train Loss: 0.0683, Train Steps/Sec: 2.16, Samples/Sec: 77.94
+[2025-05-15 16:41:25] (step=0210600) Train Loss: 0.0713, Train Steps/Sec: 2.17, Samples/Sec: 77.95
+[2025-05-15 16:42:11] (step=0210700) Train Loss: 0.0706, Train Steps/Sec: 2.16, Samples/Sec: 77.79
+[2025-05-15 16:42:58] (step=0210800) Train Loss: 0.0664, Train Steps/Sec: 2.16, Samples/Sec: 77.67
+[2025-05-15 16:43:43] (step=0210900) Train Loss: 0.0701, Train Steps/Sec: 2.21, Samples/Sec: 79.63
+[2025-05-15 16:44:23] (step=0211000) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 16:45:04] (step=0211100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-15 16:45:44] (step=0211200) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 16:46:24] (step=0211300) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 16:47:05] (step=0211400) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-15 16:47:45] (step=0211500) Train Loss: 0.0608, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-15 16:48:26] (step=0211600) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 16:49:06] (step=0211700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 16:49:46] (step=0211800) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 16:50:27] (step=0211900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 16:51:07] (step=0212000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 16:51:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 16:52:00] (step=0212100) Train Loss: 0.0623, Train Steps/Sec: 1.90, Samples/Sec: 68.31
+[2025-05-15 16:52:40] (step=0212200) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 16:53:20] (step=0212300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 16:54:01] (step=0212400) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 16:54:41] (step=0212500) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 16:55:21] (step=0212600) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-15 16:56:02] (step=0212700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 16:56:42] (step=0212800) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-15 16:57:23] (step=0212900) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-15 16:58:03] (step=0213000) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 16:58:44] (step=0213100) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-15 16:59:24] (step=0213200) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-15 17:00:05] (step=0213300) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-15 17:00:45] (step=0213400) Train Loss: 0.0685, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-15 17:01:26] (step=0213500) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 17:02:06] (step=0213600) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-15 17:02:47] (step=0213700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 17:03:27] (step=0213800) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:04:07] (step=0213900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-15 17:04:48] (step=0214000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 17:05:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 17:05:41] (step=0214100) Train Loss: 0.0650, Train Steps/Sec: 1.87, Samples/Sec: 67.25
+[2025-05-15 17:05:49] Beginning epoch 53...
+[2025-05-15 17:06:22] (step=0214200) Train Loss: 0.0736, Train Steps/Sec: 2.46, Samples/Sec: 88.49
+[2025-05-15 17:07:02] (step=0214300) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-15 17:07:43] (step=0214400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 17:08:23] (step=0214500) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:09:03] (step=0214600) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 17:09:44] (step=0214700) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-15 17:10:24] (step=0214800) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-15 17:11:05] (step=0214900) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-15 17:11:45] (step=0215000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:12:26] (step=0215100) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 17:13:06] (step=0215200) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 17:13:46] (step=0215300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 17:14:27] (step=0215400) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-15 17:15:07] (step=0215500) Train Loss: 0.0721, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 17:15:48] (step=0215600) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-15 17:16:28] (step=0215700) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 17:17:08] (step=0215800) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:17:49] (step=0215900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-15 17:18:29] (step=0216000) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 17:18:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 17:19:22] (step=0216100) Train Loss: 0.0685, Train Steps/Sec: 1.90, Samples/Sec: 68.41
+[2025-05-15 17:20:02] (step=0216200) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 17:20:43] (step=0216300) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 17:21:23] (step=0216400) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 17:22:03] (step=0216500) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 17:22:44] (step=0216600) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 17:23:24] (step=0216700) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 17:24:05] (step=0216800) Train Loss: 0.0715, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-15 17:24:46] (step=0216900) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-15 17:25:26] (step=0217000) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-15 17:26:06] (step=0217100) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-15 17:26:47] (step=0217200) Train Loss: 0.0615, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 17:27:27] (step=0217300) Train Loss: 0.0600, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-15 17:28:08] (step=0217400) Train Loss: 0.0713, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-15 17:28:48] (step=0217500) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 17:29:29] (step=0217600) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-15 17:30:09] (step=0217700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:30:50] (step=0217800) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-15 17:31:30] (step=0217900) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:32:10] (step=0218000) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 17:32:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 17:33:04] (step=0218100) Train Loss: 0.0644, Train Steps/Sec: 1.87, Samples/Sec: 67.41
+[2025-05-15 17:33:28] Beginning epoch 54...
+[2025-05-15 17:33:44] (step=0218200) Train Loss: 0.0643, Train Steps/Sec: 2.45, Samples/Sec: 88.34
+[2025-05-15 17:34:25] (step=0218300) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-15 17:35:05] (step=0218400) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 17:35:46] (step=0218500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 17:36:26] (step=0218600) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-15 17:37:06] (step=0218700) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 17:37:47] (step=0218800) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-15 17:38:27] (step=0218900) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-15 17:39:08] (step=0219000) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 17:39:48] (step=0219100) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:40:28] (step=0219200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 17:41:09] (step=0219300) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:41:49] (step=0219400) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 17:42:30] (step=0219500) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-15 17:43:10] (step=0219600) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 17:43:51] (step=0219700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 17:44:31] (step=0219800) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 17:45:11] (step=0219900) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 17:45:52] (step=0220000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 17:46:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0220000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 17:46:20] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 17:48:19] (step=0220000) Perceptual Loss: 0.3018, Eval Time: 122.33
+[2025-05-15 17:49:00] (step=0220100) Train Loss: 0.0679, Train Steps/Sec: 0.53, Samples/Sec: 19.14
+[2025-05-15 17:49:40] (step=0220200) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-15 17:50:21] (step=0220300) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 17:51:01] (step=0220400) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 17:51:42] (step=0220500) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 17:52:22] (step=0220600) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-15 17:53:03] (step=0220700) Train Loss: 0.0664, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-15 17:53:43] (step=0220800) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-15 17:54:24] (step=0220900) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-15 17:55:04] (step=0221000) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-15 17:55:44] (step=0221100) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 17:56:25] (step=0221200) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 17:57:06] (step=0221300) Train Loss: 0.0691, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-15 17:57:46] (step=0221400) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-15 17:58:27] (step=0221500) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-15 17:59:07] (step=0221600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 17:59:47] (step=0221700) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 18:00:28] (step=0221800) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 18:01:08] (step=0221900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 18:01:48] (step=0222000) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 18:02:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 18:02:42] (step=0222100) Train Loss: 0.0675, Train Steps/Sec: 1.87, Samples/Sec: 67.40
+[2025-05-15 18:03:22] (step=0222200) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 18:03:22] Beginning epoch 55...
+[2025-05-15 18:04:03] (step=0222300) Train Loss: 0.0651, Train Steps/Sec: 2.46, Samples/Sec: 88.63
+[2025-05-15 18:04:43] (step=0222400) Train Loss: 0.0604, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 18:05:23] (step=0222500) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-15 18:06:04] (step=0222600) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 18:06:44] (step=0222700) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 18:07:25] (step=0222800) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 18:08:05] (step=0222900) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-15 18:08:45] (step=0223000) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 18:09:26] (step=0223100) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-15 18:10:06] (step=0223200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 18:10:46] (step=0223300) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 18:11:27] (step=0223400) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-15 18:12:07] (step=0223500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 18:12:48] (step=0223600) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-15 18:13:28] (step=0223700) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-15 18:14:08] (step=0223800) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 18:14:49] (step=0223900) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 18:15:29] (step=0224000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 18:15:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 18:16:22] (step=0224100) Train Loss: 0.0709, Train Steps/Sec: 1.89, Samples/Sec: 68.09
+[2025-05-15 18:17:02] (step=0224200) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 18:17:43] (step=0224300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 18:18:23] (step=0224400) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 18:19:03] (step=0224500) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 18:19:44] (step=0224600) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 18:20:24] (step=0224700) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-15 18:21:05] (step=0224800) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 18:21:45] (step=0224900) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-15 18:22:25] (step=0225000) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 18:23:06] (step=0225100) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 18:23:46] (step=0225200) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 18:24:26] (step=0225300) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 18:25:07] (step=0225400) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 18:25:47] (step=0225500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-15 18:26:28] (step=0225600) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 18:27:08] (step=0225700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 18:27:48] (step=0225800) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 18:28:29] (step=0225900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 18:29:09] (step=0226000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 18:29:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 18:30:02] (step=0226100) Train Loss: 0.0625, Train Steps/Sec: 1.88, Samples/Sec: 67.69
+[2025-05-15 18:30:43] (step=0226200) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 18:30:59] Beginning epoch 56...
+[2025-05-15 18:31:23] (step=0226300) Train Loss: 0.0665, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-15 18:32:04] (step=0226400) Train Loss: 0.0754, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 18:32:44] (step=0226500) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 18:33:24] (step=0226600) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 18:34:05] (step=0226700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 18:34:45] (step=0226800) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-15 18:35:25] (step=0226900) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 18:36:06] (step=0227000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 18:36:46] (step=0227100) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 18:37:26] (step=0227200) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 18:38:07] (step=0227300) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 18:38:47] (step=0227400) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 18:39:28] (step=0227500) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 18:40:08] (step=0227600) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 18:40:48] (step=0227700) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 18:41:29] (step=0227800) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 18:42:09] (step=0227900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 18:42:49] (step=0228000) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 18:43:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 18:43:42] (step=0228100) Train Loss: 0.0701, Train Steps/Sec: 1.89, Samples/Sec: 67.96
+[2025-05-15 18:44:23] (step=0228200) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-15 18:45:03] (step=0228300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 18:45:43] (step=0228400) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 18:46:24] (step=0228500) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 18:47:04] (step=0228600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 18:47:44] (step=0228700) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 18:48:25] (step=0228800) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-15 18:49:05] (step=0228900) Train Loss: 0.0743, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 18:49:46] (step=0229000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 18:50:26] (step=0229100) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 18:51:06] (step=0229200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 18:51:47] (step=0229300) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 18:52:27] (step=0229400) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 18:53:07] (step=0229500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 18:53:48] (step=0229600) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 18:54:28] (step=0229700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 18:55:08] (step=0229800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 18:55:49] (step=0229900) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 18:56:29] (step=0230000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 18:56:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 18:56:44] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 18:58:44] (step=0230000) Perceptual Loss: 0.3148, Eval Time: 122.46
+[2025-05-15 18:59:25] (step=0230100) Train Loss: 0.0671, Train Steps/Sec: 0.57, Samples/Sec: 20.50
+[2025-05-15 19:00:05] (step=0230200) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 19:00:37] Beginning epoch 57...
+[2025-05-15 19:00:46] (step=0230300) Train Loss: 0.0679, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-15 19:01:26] (step=0230400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 19:02:06] (step=0230500) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 19:02:47] (step=0230600) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 19:03:27] (step=0230700) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-15 19:04:08] (step=0230800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 19:04:48] (step=0230900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 19:05:28] (step=0231000) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 19:06:08] (step=0231100) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 19:06:49] (step=0231200) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 19:07:29] (step=0231300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 19:08:10] (step=0231400) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-15 19:08:50] (step=0231500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 19:09:30] (step=0231600) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 19:10:11] (step=0231700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 19:10:51] (step=0231800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 19:11:31] (step=0231900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 19:12:11] (step=0232000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 19:12:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 19:13:04] (step=0232100) Train Loss: 0.0678, Train Steps/Sec: 1.89, Samples/Sec: 68.11
+[2025-05-15 19:13:45] (step=0232200) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 19:14:25] (step=0232300) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 19:15:05] (step=0232400) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 19:15:46] (step=0232500) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 19:16:26] (step=0232600) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 19:17:06] (step=0232700) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 19:17:47] (step=0232800) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 19:18:27] (step=0232900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 19:19:08] (step=0233000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 19:19:48] (step=0233100) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 19:20:28] (step=0233200) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 19:21:08] (step=0233300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 19:21:49] (step=0233400) Train Loss: 0.0733, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-15 19:22:29] (step=0233500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 19:23:10] (step=0233600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 19:23:50] (step=0233700) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 19:24:30] (step=0233800) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 19:25:11] (step=0233900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 19:25:51] (step=0234000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 19:26:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 19:26:44] (step=0234100) Train Loss: 0.0727, Train Steps/Sec: 1.88, Samples/Sec: 67.66
+[2025-05-15 19:27:25] (step=0234200) Train Loss: 0.0744, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 19:28:05] (step=0234300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 19:28:13] Beginning epoch 58...
+[2025-05-15 19:28:46] (step=0234400) Train Loss: 0.0650, Train Steps/Sec: 2.46, Samples/Sec: 88.43
+[2025-05-15 19:29:26] (step=0234500) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 19:30:06] (step=0234600) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 19:30:47] (step=0234700) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-15 19:31:27] (step=0234800) Train Loss: 0.0700, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-15 19:32:08] (step=0234900) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-15 19:32:48] (step=0235000) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 19:33:29] (step=0235100) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-15 19:34:09] (step=0235200) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-15 19:34:50] (step=0235300) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 19:35:30] (step=0235400) Train Loss: 0.0731, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-15 19:36:11] (step=0235500) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-15 19:36:51] (step=0235600) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-15 19:37:32] (step=0235700) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 19:38:12] (step=0235800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 19:38:52] (step=0235900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 19:39:33] (step=0236000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 19:39:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 19:40:25] (step=0236100) Train Loss: 0.0685, Train Steps/Sec: 1.90, Samples/Sec: 68.22
+[2025-05-15 19:41:06] (step=0236200) Train Loss: 0.0613, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 19:41:46] (step=0236300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 19:42:27] (step=0236400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 19:43:07] (step=0236500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 19:43:47] (step=0236600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 19:44:28] (step=0236700) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 19:45:08] (step=0236800) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-15 19:45:49] (step=0236900) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 19:46:29] (step=0237000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 19:47:09] (step=0237100) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 19:47:50] (step=0237200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 19:48:30] (step=0237300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 19:49:10] (step=0237400) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-15 19:49:51] (step=0237500) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 19:50:31] (step=0237600) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 19:51:12] (step=0237700) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 19:51:52] (step=0237800) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 19:52:32] (step=0237900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-15 19:53:13] (step=0238000) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 19:53:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 19:54:06] (step=0238100) Train Loss: 0.0672, Train Steps/Sec: 1.88, Samples/Sec: 67.62
+[2025-05-15 19:54:46] (step=0238200) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 19:55:27] (step=0238300) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 19:55:51] Beginning epoch 59...
+[2025-05-15 19:56:07] (step=0238400) Train Loss: 0.0627, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-15 19:56:48] (step=0238500) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 19:57:28] (step=0238600) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 19:58:08] (step=0238700) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 19:58:49] (step=0238800) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-15 19:59:29] (step=0238900) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-15 20:00:10] (step=0239000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 20:00:50] (step=0239100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 20:01:30] (step=0239200) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 20:02:11] (step=0239300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 20:02:51] (step=0239400) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 20:03:32] (step=0239500) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-15 20:04:12] (step=0239600) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-15 20:04:52] (step=0239700) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 20:05:33] (step=0239800) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:06:13] (step=0239900) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:06:53] (step=0240000) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 20:07:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0240000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 20:07:25] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 20:09:24] (step=0240000) Perceptual Loss: 0.2962, Eval Time: 122.69
+[2025-05-15 20:10:05] (step=0240100) Train Loss: 0.0656, Train Steps/Sec: 0.52, Samples/Sec: 18.79
+[2025-05-15 20:10:45] (step=0240200) Train Loss: 0.0711, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-15 20:11:26] (step=0240300) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 20:12:06] (step=0240400) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:12:46] (step=0240500) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 20:13:27] (step=0240600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 20:14:07] (step=0240700) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-15 20:14:48] (step=0240800) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 20:15:28] (step=0240900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 20:16:08] (step=0241000) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:16:49] (step=0241100) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 20:17:29] (step=0241200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 20:18:09] (step=0241300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 20:18:50] (step=0241400) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-15 20:19:30] (step=0241500) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-15 20:20:11] (step=0241600) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 20:20:51] (step=0241700) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 20:21:32] (step=0241800) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 20:22:12] (step=0241900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 20:22:52] (step=0242000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 20:23:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 20:23:45] (step=0242100) Train Loss: 0.0655, Train Steps/Sec: 1.90, Samples/Sec: 68.22
+[2025-05-15 20:24:26] (step=0242200) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-15 20:25:06] (step=0242300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 20:25:46] (step=0242400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-15 20:25:46] Beginning epoch 60...
+[2025-05-15 20:26:27] (step=0242500) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-15 20:27:07] (step=0242600) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 20:27:48] (step=0242700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:28:28] (step=0242800) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-15 20:29:08] (step=0242900) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 20:29:49] (step=0243000) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 20:30:29] (step=0243100) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:31:09] (step=0243200) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 20:31:50] (step=0243300) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 20:32:30] (step=0243400) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-15 20:33:11] (step=0243500) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 20:33:51] (step=0243600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:34:31] (step=0243700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 20:35:12] (step=0243800) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 20:35:52] (step=0243900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 20:36:32] (step=0244000) Train Loss: 0.0614, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 20:36:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 20:37:25] (step=0244100) Train Loss: 0.0692, Train Steps/Sec: 1.89, Samples/Sec: 68.13
+[2025-05-15 20:38:05] (step=0244200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 20:38:46] (step=0244300) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:39:26] (step=0244400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:40:06] (step=0244500) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 20:40:47] (step=0244600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 20:41:27] (step=0244700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 20:42:08] (step=0244800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-15 20:42:48] (step=0244900) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-15 20:43:28] (step=0245000) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 20:44:09] (step=0245100) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 20:44:49] (step=0245200) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 20:45:29] (step=0245300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 20:46:10] (step=0245400) Train Loss: 0.0719, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 20:46:50] (step=0245500) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 20:47:31] (step=0245600) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 20:48:11] (step=0245700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-15 20:48:52] (step=0245800) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 20:49:32] (step=0245900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 20:50:12] (step=0246000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 20:50:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 20:51:05] (step=0246100) Train Loss: 0.0592, Train Steps/Sec: 1.89, Samples/Sec: 68.05
+[2025-05-15 20:51:46] (step=0246200) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-15 20:52:26] (step=0246300) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 20:53:06] (step=0246400) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 20:53:23] Beginning epoch 61...
+[2025-05-15 20:53:47] (step=0246500) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-15 20:54:27] (step=0246600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 20:55:08] (step=0246700) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-15 20:55:48] (step=0246800) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-15 20:56:29] (step=0246900) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 20:57:09] (step=0247000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 20:57:50] (step=0247100) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-15 20:58:30] (step=0247200) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-15 20:59:10] (step=0247300) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 20:59:51] (step=0247400) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-15 21:00:31] (step=0247500) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 21:01:12] (step=0247600) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 21:01:52] (step=0247700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 21:02:32] (step=0247800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:03:13] (step=0247900) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:03:53] (step=0248000) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 21:04:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 21:04:46] (step=0248100) Train Loss: 0.0709, Train Steps/Sec: 1.90, Samples/Sec: 68.35
+[2025-05-15 21:05:26] (step=0248200) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 21:06:07] (step=0248300) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 21:06:47] (step=0248400) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-15 21:07:28] (step=0248500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:08:08] (step=0248600) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-15 21:08:48] (step=0248700) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 21:09:29] (step=0248800) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-15 21:10:09] (step=0248900) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 21:10:50] (step=0249000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 21:11:30] (step=0249100) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-15 21:12:10] (step=0249200) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 21:12:51] (step=0249300) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 21:13:31] (step=0249400) Train Loss: 0.0633, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-15 21:14:12] (step=0249500) Train Loss: 0.0715, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-15 21:14:53] (step=0249600) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-15 21:15:33] (step=0249700) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-15 21:16:13] (step=0249800) Train Loss: 0.0732, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-15 21:16:54] (step=0249900) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-15 21:17:34] (step=0250000) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 21:17:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 21:17:50] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 21:19:49] (step=0250000) Perceptual Loss: 0.3199, Eval Time: 122.43
+[2025-05-15 21:20:30] (step=0250100) Train Loss: 0.0679, Train Steps/Sec: 0.57, Samples/Sec: 20.53
+[2025-05-15 21:21:10] (step=0250200) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-15 21:21:51] (step=0250300) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-15 21:22:31] (step=0250400) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-15 21:23:04] Beginning epoch 62...
+[2025-05-15 21:23:12] (step=0250500) Train Loss: 0.0721, Train Steps/Sec: 2.46, Samples/Sec: 88.38
+[2025-05-15 21:23:53] (step=0250600) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-15 21:24:33] (step=0250700) Train Loss: 0.0699, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-15 21:25:14] (step=0250800) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-15 21:25:54] (step=0250900) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-15 21:26:35] (step=0251000) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-15 21:27:15] (step=0251100) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-15 21:27:56] (step=0251200) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-15 21:28:36] (step=0251300) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-15 21:29:17] (step=0251400) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-15 21:29:57] (step=0251500) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-15 21:30:38] (step=0251600) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 21:31:18] (step=0251700) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-15 21:31:58] (step=0251800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 21:32:39] (step=0251900) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 21:33:19] (step=0252000) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 21:33:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 21:34:12] (step=0252100) Train Loss: 0.0640, Train Steps/Sec: 1.89, Samples/Sec: 68.05
+[2025-05-15 21:34:53] (step=0252200) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-15 21:35:33] (step=0252300) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 21:36:13] (step=0252400) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 21:36:54] (step=0252500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-15 21:37:34] (step=0252600) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 21:38:15] (step=0252700) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 21:38:55] (step=0252800) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-15 21:39:35] (step=0252900) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 21:40:16] (step=0253000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 21:40:56] (step=0253100) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 21:41:36] (step=0253200) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-15 21:42:17] (step=0253300) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 21:42:57] (step=0253400) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-15 21:43:38] (step=0253500) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-15 21:44:18] (step=0253600) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 21:44:58] (step=0253700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 21:45:39] (step=0253800) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:46:19] (step=0253900) Train Loss: 0.0605, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 21:47:00] (step=0254000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 21:47:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 21:47:52] (step=0254100) Train Loss: 0.0641, Train Steps/Sec: 1.90, Samples/Sec: 68.48
+[2025-05-15 21:48:33] (step=0254200) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 21:49:13] (step=0254300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:49:53] (step=0254400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 21:50:34] (step=0254500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:50:42] Beginning epoch 63...
+[2025-05-15 21:51:14] (step=0254600) Train Loss: 0.0689, Train Steps/Sec: 2.46, Samples/Sec: 88.51
+[2025-05-15 21:51:55] (step=0254700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 21:52:35] (step=0254800) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-15 21:53:16] (step=0254900) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 21:53:56] (step=0255000) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:54:36] (step=0255100) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:55:17] (step=0255200) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:55:57] (step=0255300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 21:56:37] (step=0255400) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 21:57:18] (step=0255500) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-15 21:57:58] (step=0255600) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 21:58:39] (step=0255700) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:59:19] (step=0255800) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 21:59:59] (step=0255900) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 22:00:40] (step=0256000) Train Loss: 0.0610, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 22:00:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 22:01:38] (step=0256100) Train Loss: 0.0728, Train Steps/Sec: 1.72, Samples/Sec: 62.10
+[2025-05-15 22:02:18] (step=0256200) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-15 22:02:58] (step=0256300) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:03:39] (step=0256400) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 22:04:19] (step=0256500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 22:04:59] (step=0256600) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-15 22:05:40] (step=0256700) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-15 22:06:20] (step=0256800) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-15 22:07:01] (step=0256900) Train Loss: 0.0723, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-15 22:07:41] (step=0257000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 22:08:21] (step=0257100) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 22:09:02] (step=0257200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:09:42] (step=0257300) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:10:22] (step=0257400) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-15 22:11:03] (step=0257500) Train Loss: 0.0605, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-15 22:11:43] (step=0257600) Train Loss: 0.0723, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 22:12:24] (step=0257700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-15 22:13:04] (step=0257800) Train Loss: 0.0588, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:13:44] (step=0257900) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:14:25] (step=0258000) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:14:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 22:15:17] (step=0258100) Train Loss: 0.0653, Train Steps/Sec: 1.92, Samples/Sec: 69.10
+[2025-05-15 22:15:57] (step=0258200) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 22:16:37] (step=0258300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 22:17:18] (step=0258400) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 22:17:58] (step=0258500) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 22:18:22] Beginning epoch 64...
+[2025-05-15 22:18:39] (step=0258600) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-15 22:19:19] (step=0258700) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 22:19:59] (step=0258800) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 22:20:40] (step=0258900) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 22:21:20] (step=0259000) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 22:22:00] (step=0259100) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 22:22:41] (step=0259200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:23:21] (step=0259300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:24:01] (step=0259400) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 22:24:42] (step=0259500) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 22:25:22] (step=0259600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 22:26:03] (step=0259700) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-15 22:26:43] (step=0259800) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 22:27:23] (step=0259900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-15 22:28:03] (step=0260000) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 22:28:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0260000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 22:28:31] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 22:30:30] (step=0260000) Perceptual Loss: 0.3122, Eval Time: 122.37
+[2025-05-15 22:31:11] (step=0260100) Train Loss: 0.0730, Train Steps/Sec: 0.53, Samples/Sec: 19.20
+[2025-05-15 22:31:51] (step=0260200) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 22:32:32] (step=0260300) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-15 22:33:12] (step=0260400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 22:33:52] (step=0260500) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 22:34:33] (step=0260600) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 22:35:13] (step=0260700) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-15 22:35:53] (step=0260800) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 22:36:34] (step=0260900) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 22:37:14] (step=0261000) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 22:37:54] (step=0261100) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 22:38:35] (step=0261200) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 22:39:15] (step=0261300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 22:39:55] (step=0261400) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-15 22:40:36] (step=0261500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 22:41:16] (step=0261600) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 22:41:57] (step=0261700) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 22:42:37] (step=0261800) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 22:43:17] (step=0261900) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 22:43:57] (step=0262000) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 22:44:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 22:44:51] (step=0262100) Train Loss: 0.0641, Train Steps/Sec: 1.87, Samples/Sec: 67.44
+[2025-05-15 22:45:31] (step=0262200) Train Loss: 0.0740, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 22:46:12] (step=0262300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 22:46:52] (step=0262400) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 22:47:32] (step=0262500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 22:48:13] (step=0262600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-15 22:48:13] Beginning epoch 65...
+[2025-05-15 22:48:53] (step=0262700) Train Loss: 0.0642, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-15 22:49:34] (step=0262800) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-15 22:50:14] (step=0262900) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 22:50:54] (step=0263000) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 22:51:35] (step=0263100) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-15 22:52:15] (step=0263200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 22:52:55] (step=0263300) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 22:53:36] (step=0263400) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-15 22:54:16] (step=0263500) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:54:57] (step=0263600) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 22:55:37] (step=0263700) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-15 22:56:17] (step=0263800) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 22:56:58] (step=0263900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:57:38] (step=0264000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 22:57:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 22:58:31] (step=0264100) Train Loss: 0.0639, Train Steps/Sec: 1.89, Samples/Sec: 68.00
+[2025-05-15 22:59:11] (step=0264200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 22:59:52] (step=0264300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:00:32] (step=0264400) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:01:12] (step=0264500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 23:01:53] (step=0264600) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:02:33] (step=0264700) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-15 23:03:13] (step=0264800) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 23:03:54] (step=0264900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 23:04:34] (step=0265000) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:05:14] (step=0265100) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 23:05:55] (step=0265200) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:06:35] (step=0265300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:07:15] (step=0265400) Train Loss: 0.0711, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-15 23:07:56] (step=0265500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 23:08:36] (step=0265600) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 23:09:17] (step=0265700) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:09:57] (step=0265800) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:10:37] (step=0265900) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:11:17] (step=0266000) Train Loss: 0.0601, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 23:11:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 23:12:10] (step=0266100) Train Loss: 0.0653, Train Steps/Sec: 1.89, Samples/Sec: 67.92
+[2025-05-15 23:12:51] (step=0266200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:13:31] (step=0266300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 23:14:12] (step=0266400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-15 23:14:52] (step=0266500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:15:32] (step=0266600) Train Loss: 0.0761, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:15:48] Beginning epoch 66...
+[2025-05-15 23:16:13] (step=0266700) Train Loss: 0.0621, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-15 23:16:53] (step=0266800) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 23:17:34] (step=0266900) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 23:18:14] (step=0267000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 23:18:54] (step=0267100) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 23:19:34] (step=0267200) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-15 23:20:15] (step=0267300) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-15 23:20:55] (step=0267400) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-15 23:21:36] (step=0267500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 23:22:16] (step=0267600) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 23:22:56] (step=0267700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-15 23:23:37] (step=0267800) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:24:17] (step=0267900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:24:57] (step=0268000) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:25:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 23:25:50] (step=0268100) Train Loss: 0.0668, Train Steps/Sec: 1.89, Samples/Sec: 68.03
+[2025-05-15 23:26:31] (step=0268200) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 23:27:11] (step=0268300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 23:27:51] (step=0268400) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:28:32] (step=0268500) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:29:12] (step=0268600) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:29:52] (step=0268700) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-15 23:30:33] (step=0268800) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-15 23:31:13] (step=0268900) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:31:53] (step=0269000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 23:32:34] (step=0269100) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:33:14] (step=0269200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:33:54] (step=0269300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-15 23:34:35] (step=0269400) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 23:35:15] (step=0269500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-15 23:35:55] (step=0269600) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 23:36:36] (step=0269700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:37:16] (step=0269800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:37:56] (step=0269900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:38:37] (step=0270000) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 23:38:49] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-15 23:38:52] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-15 23:40:52] (step=0270000) Perceptual Loss: 0.2977, Eval Time: 122.28
+[2025-05-15 23:41:32] (step=0270100) Train Loss: 0.0683, Train Steps/Sec: 0.57, Samples/Sec: 20.51
+[2025-05-15 23:42:13] (step=0270200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-15 23:42:53] (step=0270300) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:43:33] (step=0270400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-15 23:44:14] (step=0270500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-15 23:44:54] (step=0270600) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:45:26] Beginning epoch 67...
+[2025-05-15 23:45:34] (step=0270700) Train Loss: 0.0650, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-15 23:46:15] (step=0270800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-15 23:46:55] (step=0270900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-15 23:47:36] (step=0271000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:48:16] (step=0271100) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:48:56] (step=0271200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:49:36] (step=0271300) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:50:17] (step=0271400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-15 23:50:57] (step=0271500) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-15 23:51:38] (step=0271600) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:52:18] (step=0271700) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:52:58] (step=0271800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:53:39] (step=0271900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:54:19] (step=0272000) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-15 23:54:31] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-15 23:55:11] (step=0272100) Train Loss: 0.0666, Train Steps/Sec: 1.92, Samples/Sec: 68.96
+[2025-05-15 23:55:51] (step=0272200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-15 23:56:32] (step=0272300) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:57:12] (step=0272400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:57:52] (step=0272500) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-15 23:58:33] (step=0272600) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-15 23:59:13] (step=0272700) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-15 23:59:53] (step=0272800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 00:00:34] (step=0272900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 00:01:14] (step=0273000) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 00:01:54] (step=0273100) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 00:02:35] (step=0273200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 00:03:15] (step=0273300) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 00:03:56] (step=0273400) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 00:04:36] (step=0273500) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 00:05:16] (step=0273600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 00:05:57] (step=0273700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 00:06:37] (step=0273800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 00:07:17] (step=0273900) Train Loss: 0.0749, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 00:07:57] (step=0274000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 00:08:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 00:08:51] (step=0274100) Train Loss: 0.0702, Train Steps/Sec: 1.87, Samples/Sec: 67.39
+[2025-05-16 00:09:31] (step=0274200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 00:10:12] (step=0274300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 00:10:52] (step=0274400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 00:11:32] (step=0274500) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 00:12:13] (step=0274600) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 00:12:53] (step=0274700) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 00:13:01] Beginning epoch 68...
+[2025-05-16 00:13:34] (step=0274800) Train Loss: 0.0691, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-16 00:14:14] (step=0274900) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 00:14:54] (step=0275000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 00:15:35] (step=0275100) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 00:16:15] (step=0275200) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 00:16:55] (step=0275300) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 00:17:36] (step=0275400) Train Loss: 0.0734, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 00:18:16] (step=0275500) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 00:18:57] (step=0275600) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 00:19:37] (step=0275700) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 00:20:18] (step=0275800) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 00:20:58] (step=0275900) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 00:21:38] (step=0276000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 00:21:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 00:22:31] (step=0276100) Train Loss: 0.0711, Train Steps/Sec: 1.88, Samples/Sec: 67.70
+[2025-05-16 00:23:12] (step=0276200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 00:23:52] (step=0276300) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 00:24:33] (step=0276400) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 00:25:13] (step=0276500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 00:25:53] (step=0276600) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 00:26:34] (step=0276700) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 00:27:14] (step=0276800) Train Loss: 0.0647, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-16 00:27:55] (step=0276900) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 00:28:35] (step=0277000) Train Loss: 0.0711, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 00:29:16] (step=0277100) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 00:29:56] (step=0277200) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 00:30:36] (step=0277300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 00:31:17] (step=0277400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 00:31:57] (step=0277500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 00:32:37] (step=0277600) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 00:33:18] (step=0277700) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 00:33:58] (step=0277800) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 00:34:38] (step=0277900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 00:35:19] (step=0278000) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 00:35:31] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 00:36:12] (step=0278100) Train Loss: 0.0645, Train Steps/Sec: 1.88, Samples/Sec: 67.79
+[2025-05-16 00:36:52] (step=0278200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 00:37:33] (step=0278300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 00:38:13] (step=0278400) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 00:38:53] (step=0278500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 00:39:33] (step=0278600) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 00:40:14] (step=0278700) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 00:40:38] Beginning epoch 69...
+[2025-05-16 00:40:54] (step=0278800) Train Loss: 0.0662, Train Steps/Sec: 2.45, Samples/Sec: 88.38
+[2025-05-16 00:41:35] (step=0278900) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 00:42:15] (step=0279000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 00:42:55] (step=0279100) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 00:43:36] (step=0279200) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 00:44:16] (step=0279300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 00:44:56] (step=0279400) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 00:45:37] (step=0279500) Train Loss: 0.0606, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 00:46:17] (step=0279600) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 00:46:58] (step=0279700) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 00:47:38] (step=0279800) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 00:48:18] (step=0279900) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 00:48:58] (step=0280000) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 00:49:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0280000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 00:49:27] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 00:51:27] (step=0280000) Perceptual Loss: 0.3162, Eval Time: 122.65
+[2025-05-16 00:52:07] (step=0280100) Train Loss: 0.0659, Train Steps/Sec: 0.53, Samples/Sec: 19.09
+[2025-05-16 00:52:48] (step=0280200) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 00:53:28] (step=0280300) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 00:54:08] (step=0280400) Train Loss: 0.0606, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 00:54:49] (step=0280500) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 00:55:29] (step=0280600) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 00:56:10] (step=0280700) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-16 00:56:50] (step=0280800) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 00:57:31] (step=0280900) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 00:58:11] (step=0281000) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 00:58:51] (step=0281100) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 00:59:32] (step=0281200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 01:00:12] (step=0281300) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 01:00:53] (step=0281400) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-16 01:01:33] (step=0281500) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 01:02:14] (step=0281600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 01:02:54] (step=0281700) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 01:03:34] (step=0281800) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 01:04:15] (step=0281900) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:04:55] (step=0282000) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 01:05:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 01:05:48] (step=0282100) Train Loss: 0.0665, Train Steps/Sec: 1.89, Samples/Sec: 67.91
+[2025-05-16 01:06:29] (step=0282200) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 01:07:09] (step=0282300) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 01:07:49] (step=0282400) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 01:08:30] (step=0282500) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 01:09:10] (step=0282600) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 01:09:50] (step=0282700) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 01:10:31] (step=0282800) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:10:31] Beginning epoch 70...
+[2025-05-16 01:11:11] (step=0282900) Train Loss: 0.0660, Train Steps/Sec: 2.46, Samples/Sec: 88.49
+[2025-05-16 01:11:52] (step=0283000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:12:32] (step=0283100) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 01:13:13] (step=0283200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 01:13:53] (step=0283300) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 01:14:33] (step=0283400) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-16 01:15:14] (step=0283500) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 01:15:54] (step=0283600) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 01:16:35] (step=0283700) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 01:17:15] (step=0283800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 01:17:55] (step=0283900) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 01:18:36] (step=0284000) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:18:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 01:19:29] (step=0284100) Train Loss: 0.0667, Train Steps/Sec: 1.89, Samples/Sec: 67.94
+[2025-05-16 01:20:09] (step=0284200) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 01:20:49] (step=0284300) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:21:30] (step=0284400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:22:10] (step=0284500) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 01:22:50] (step=0284600) Train Loss: 0.0606, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 01:23:31] (step=0284700) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 01:24:11] (step=0284800) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 01:24:52] (step=0284900) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 01:25:32] (step=0285000) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 01:26:12] (step=0285100) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:26:53] (step=0285200) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 01:27:33] (step=0285300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 01:28:14] (step=0285400) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 01:28:54] (step=0285500) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 01:29:34] (step=0285600) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 01:30:15] (step=0285700) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 01:30:55] (step=0285800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 01:31:35] (step=0285900) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 01:32:16] (step=0286000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 01:32:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 01:33:09] (step=0286100) Train Loss: 0.0644, Train Steps/Sec: 1.89, Samples/Sec: 67.86
+[2025-05-16 01:33:49] (step=0286200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 01:34:30] (step=0286300) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 01:35:10] (step=0286400) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:35:50] (step=0286500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 01:36:31] (step=0286600) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 01:37:11] (step=0286700) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 01:37:52] (step=0286800) Train Loss: 0.0734, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 01:38:08] Beginning epoch 71...
+[2025-05-16 01:38:32] (step=0286900) Train Loss: 0.0627, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-16 01:39:13] (step=0287000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 01:39:53] (step=0287100) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 01:40:33] (step=0287200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 01:41:14] (step=0287300) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 01:41:54] (step=0287400) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 01:42:34] (step=0287500) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 01:43:15] (step=0287600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 01:43:55] (step=0287700) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 01:44:35] (step=0287800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 01:45:16] (step=0287900) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 01:45:56] (step=0288000) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 01:46:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 01:46:49] (step=0288100) Train Loss: 0.0701, Train Steps/Sec: 1.89, Samples/Sec: 68.13
+[2025-05-16 01:47:29] (step=0288200) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 01:48:10] (step=0288300) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 01:48:50] (step=0288400) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 01:49:30] (step=0288500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 01:50:11] (step=0288600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 01:50:51] (step=0288700) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 01:51:32] (step=0288800) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 01:52:12] (step=0288900) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 01:52:52] (step=0289000) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 01:53:33] (step=0289100) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 01:54:13] (step=0289200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 01:54:53] (step=0289300) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 01:55:34] (step=0289400) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 01:56:14] (step=0289500) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 01:56:54] (step=0289600) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 01:57:35] (step=0289700) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 01:58:15] (step=0289800) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 01:58:55] (step=0289900) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 01:59:36] (step=0290000) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 01:59:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 01:59:51] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 02:01:50] (step=0290000) Perceptual Loss: 0.3243, Eval Time: 122.49
+[2025-05-16 02:02:31] (step=0290100) Train Loss: 0.0736, Train Steps/Sec: 0.57, Samples/Sec: 20.55
+[2025-05-16 02:03:11] (step=0290200) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 02:03:52] (step=0290300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 02:04:32] (step=0290400) Train Loss: 0.0739, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 02:05:12] (step=0290500) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 02:05:53] (step=0290600) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 02:06:33] (step=0290700) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 02:07:13] (step=0290800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 02:07:46] Beginning epoch 72...
+[2025-05-16 02:07:54] (step=0290900) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-16 02:08:34] (step=0291000) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 02:09:15] (step=0291100) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 02:09:55] (step=0291200) Train Loss: 0.0607, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 02:10:35] (step=0291300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 02:11:16] (step=0291400) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-16 02:11:56] (step=0291500) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 02:12:37] (step=0291600) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 02:13:17] (step=0291700) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 02:13:57] (step=0291800) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 02:14:38] (step=0291900) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 02:15:18] (step=0292000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 02:15:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 02:16:10] (step=0292100) Train Loss: 0.0692, Train Steps/Sec: 1.91, Samples/Sec: 68.64
+[2025-05-16 02:16:51] (step=0292200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 02:17:31] (step=0292300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 02:18:11] (step=0292400) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 02:18:52] (step=0292500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 02:19:32] (step=0292600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 02:20:12] (step=0292700) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 02:20:53] (step=0292800) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 02:21:33] (step=0292900) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 02:22:14] (step=0293000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 02:22:54] (step=0293100) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 02:23:34] (step=0293200) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 02:24:15] (step=0293300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 02:24:55] (step=0293400) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 02:25:35] (step=0293500) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 02:26:16] (step=0293600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 02:26:56] (step=0293700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 02:27:36] (step=0293800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 02:28:17] (step=0293900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 02:28:57] (step=0294000) Train Loss: 0.0752, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 02:29:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 02:29:50] (step=0294100) Train Loss: 0.0759, Train Steps/Sec: 1.89, Samples/Sec: 67.88
+[2025-05-16 02:30:30] (step=0294200) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 02:31:11] (step=0294300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 02:31:51] (step=0294400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 02:32:31] (step=0294500) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 02:33:12] (step=0294600) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 02:33:52] (step=0294700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 02:34:33] (step=0294800) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 02:35:13] (step=0294900) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 02:35:21] Beginning epoch 73...
+[2025-05-16 02:35:54] (step=0295000) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-16 02:36:34] (step=0295100) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 02:37:14] (step=0295200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 02:37:55] (step=0295300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 02:38:35] (step=0295400) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 02:39:15] (step=0295500) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 02:39:56] (step=0295600) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 02:40:36] (step=0295700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 02:41:17] (step=0295800) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 02:41:57] (step=0295900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 02:42:37] (step=0296000) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 02:42:49] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 02:43:29] (step=0296100) Train Loss: 0.0694, Train Steps/Sec: 1.91, Samples/Sec: 68.86
+[2025-05-16 02:44:10] (step=0296200) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 02:44:50] (step=0296300) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 02:45:31] (step=0296400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 02:46:11] (step=0296500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 02:46:51] (step=0296600) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 02:47:32] (step=0296700) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 02:48:12] (step=0296800) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-16 02:48:53] (step=0296900) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 02:49:33] (step=0297000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 02:50:13] (step=0297100) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 02:50:54] (step=0297200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 02:51:34] (step=0297300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 02:52:15] (step=0297400) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 02:52:55] (step=0297500) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 02:53:35] (step=0297600) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 02:54:16] (step=0297700) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 02:54:56] (step=0297800) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 02:55:37] (step=0297900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 02:56:17] (step=0298000) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 02:56:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 02:57:09] (step=0298100) Train Loss: 0.0644, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-16 02:57:50] (step=0298200) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 02:58:30] (step=0298300) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 02:59:11] (step=0298400) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 02:59:51] (step=0298500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 03:00:31] (step=0298600) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 03:01:12] (step=0298700) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 03:01:52] (step=0298800) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-16 03:02:32] (step=0298900) Train Loss: 0.0739, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:02:57] Beginning epoch 74...
+[2025-05-16 03:03:13] (step=0299000) Train Loss: 0.0729, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-16 03:03:53] (step=0299100) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:04:34] (step=0299200) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:05:14] (step=0299300) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 03:05:55] (step=0299400) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 03:06:35] (step=0299500) Train Loss: 0.0596, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 03:07:15] (step=0299600) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 03:07:56] (step=0299700) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 03:08:36] (step=0299800) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:09:16] (step=0299900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 03:09:57] (step=0300000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 03:10:21] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0300000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 03:10:24] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 03:12:24] (step=0300000) Perceptual Loss: 0.2980, Eval Time: 122.56
+[2025-05-16 03:13:04] (step=0300100) Train Loss: 0.0656, Train Steps/Sec: 0.53, Samples/Sec: 19.23
+[2025-05-16 03:13:44] (step=0300200) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 03:14:25] (step=0300300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:15:05] (step=0300400) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 03:15:46] (step=0300500) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:16:26] (step=0300600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:17:06] (step=0300700) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-16 03:17:47] (step=0300800) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:18:27] (step=0300900) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 03:19:08] (step=0301000) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 03:19:48] (step=0301100) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 03:20:28] (step=0301200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 03:21:09] (step=0301300) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 03:21:49] (step=0301400) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-16 03:22:30] (step=0301500) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 03:23:10] (step=0301600) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 03:23:51] (step=0301700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 03:24:31] (step=0301800) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 03:25:11] (step=0301900) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 03:25:52] (step=0302000) Train Loss: 0.0749, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 03:26:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 03:26:45] (step=0302100) Train Loss: 0.0619, Train Steps/Sec: 1.89, Samples/Sec: 68.14
+[2025-05-16 03:27:25] (step=0302200) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 03:28:05] (step=0302300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 03:28:46] (step=0302400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 03:29:26] (step=0302500) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 03:30:07] (step=0302600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 03:30:47] (step=0302700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:31:28] (step=0302800) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-16 03:32:08] (step=0302900) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 03:32:48] (step=0303000) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 03:32:48] Beginning epoch 75...
+[2025-05-16 03:33:29] (step=0303100) Train Loss: 0.0675, Train Steps/Sec: 2.46, Samples/Sec: 88.47
+[2025-05-16 03:34:09] (step=0303200) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:34:50] (step=0303300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 03:35:30] (step=0303400) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-16 03:36:11] (step=0303500) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 03:36:51] (step=0303600) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 03:37:31] (step=0303700) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 03:38:12] (step=0303800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:38:52] (step=0303900) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 03:39:33] (step=0304000) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 03:39:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 03:40:25] (step=0304100) Train Loss: 0.0695, Train Steps/Sec: 1.91, Samples/Sec: 68.78
+[2025-05-16 03:41:05] (step=0304200) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 03:41:46] (step=0304300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 03:42:26] (step=0304400) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 03:43:06] (step=0304500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:43:47] (step=0304600) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 03:44:27] (step=0304700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 03:45:08] (step=0304800) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-16 03:45:48] (step=0304900) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 03:46:28] (step=0305000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 03:47:09] (step=0305100) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 03:47:49] (step=0305200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:48:29] (step=0305300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:49:10] (step=0305400) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 03:49:50] (step=0305500) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 03:50:31] (step=0305600) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 03:51:11] (step=0305700) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 03:51:51] (step=0305800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:52:32] (step=0305900) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 03:53:12] (step=0306000) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 03:53:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 03:54:06] (step=0306100) Train Loss: 0.0644, Train Steps/Sec: 1.87, Samples/Sec: 67.48
+[2025-05-16 03:54:46] (step=0306200) Train Loss: 0.0609, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 03:55:26] (step=0306300) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 03:56:07] (step=0306400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:56:47] (step=0306500) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 03:57:27] (step=0306600) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 03:58:08] (step=0306700) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 03:58:48] (step=0306800) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-16 03:59:29] (step=0306900) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 04:00:09] (step=0307000) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 04:00:25] Beginning epoch 76...
+[2025-05-16 04:00:50] (step=0307100) Train Loss: 0.0679, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-16 04:01:30] (step=0307200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 04:02:10] (step=0307300) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 04:02:51] (step=0307400) Train Loss: 0.0732, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 04:03:31] (step=0307500) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 04:04:12] (step=0307600) Train Loss: 0.0598, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 04:04:52] (step=0307700) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:05:32] (step=0307800) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 04:06:13] (step=0307900) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:06:53] (step=0308000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 04:07:06] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 04:07:46] (step=0308100) Train Loss: 0.0634, Train Steps/Sec: 1.89, Samples/Sec: 68.01
+[2025-05-16 04:08:27] (step=0308200) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 04:09:07] (step=0308300) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 04:09:47] (step=0308400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 04:10:28] (step=0308500) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 04:11:08] (step=0308600) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 04:11:48] (step=0308700) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:12:29] (step=0308800) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-16 04:13:09] (step=0308900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:13:50] (step=0309000) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 04:14:30] (step=0309100) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 04:15:11] (step=0309200) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 04:15:51] (step=0309300) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 04:16:31] (step=0309400) Train Loss: 0.0740, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 04:17:12] (step=0309500) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 04:17:52] (step=0309600) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 04:18:33] (step=0309700) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:19:13] (step=0309800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:19:53] (step=0309900) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:20:34] (step=0310000) Train Loss: 0.0609, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 04:20:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 04:20:50] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 04:22:49] (step=0310000) Perceptual Loss: 0.3250, Eval Time: 122.75
+[2025-05-16 04:23:30] (step=0310100) Train Loss: 0.0703, Train Steps/Sec: 0.57, Samples/Sec: 20.44
+[2025-05-16 04:24:10] (step=0310200) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 04:24:51] (step=0310300) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 04:25:31] (step=0310400) Train Loss: 0.0725, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 04:26:12] (step=0310500) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 04:26:52] (step=0310600) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 04:27:33] (step=0310700) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-16 04:28:13] (step=0310800) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 04:28:53] (step=0310900) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 04:29:34] (step=0311000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 04:30:06] Beginning epoch 77...
+[2025-05-16 04:30:14] (step=0311100) Train Loss: 0.0619, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-16 04:30:55] (step=0311200) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 04:31:35] (step=0311300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 04:32:16] (step=0311400) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-16 04:32:56] (step=0311500) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 04:33:36] (step=0311600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 04:34:17] (step=0311700) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 04:34:57] (step=0311800) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 04:35:38] (step=0311900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 04:36:18] (step=0312000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 04:36:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 04:37:10] (step=0312100) Train Loss: 0.0619, Train Steps/Sec: 1.90, Samples/Sec: 68.57
+[2025-05-16 04:37:51] (step=0312200) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 04:38:31] (step=0312300) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:39:12] (step=0312400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 04:39:52] (step=0312500) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 04:40:32] (step=0312600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 04:41:13] (step=0312700) Train Loss: 0.0611, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 04:41:53] (step=0312800) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 04:42:34] (step=0312900) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 04:43:14] (step=0313000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 04:43:54] (step=0313100) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 04:44:35] (step=0313200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 04:45:15] (step=0313300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:45:56] (step=0313400) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-16 04:46:36] (step=0313500) Train Loss: 0.0749, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 04:47:17] (step=0313600) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 04:47:57] (step=0313700) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:48:37] (step=0313800) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:49:18] (step=0313900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:49:58] (step=0314000) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 04:50:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 04:50:51] (step=0314100) Train Loss: 0.0695, Train Steps/Sec: 1.90, Samples/Sec: 68.48
+[2025-05-16 04:51:31] (step=0314200) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 04:52:11] (step=0314300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 04:52:52] (step=0314400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 04:53:32] (step=0314500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 04:54:12] (step=0314600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 04:54:53] (step=0314700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 04:55:33] (step=0314800) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-16 04:56:14] (step=0314900) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 04:56:54] (step=0315000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 04:57:34] (step=0315100) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 04:57:43] Beginning epoch 78...
+[2025-05-16 04:58:15] (step=0315200) Train Loss: 0.0660, Train Steps/Sec: 2.46, Samples/Sec: 88.51
+[2025-05-16 04:58:56] (step=0315300) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 04:59:36] (step=0315400) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 05:00:16] (step=0315500) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 05:00:57] (step=0315600) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 05:01:37] (step=0315700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 05:02:18] (step=0315800) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:02:58] (step=0315900) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 05:03:38] (step=0316000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:03:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 05:04:31] (step=0316100) Train Loss: 0.0666, Train Steps/Sec: 1.90, Samples/Sec: 68.55
+[2025-05-16 05:05:11] (step=0316200) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 05:05:52] (step=0316300) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 05:06:32] (step=0316400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:07:12] (step=0316500) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:07:53] (step=0316600) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:08:33] (step=0316700) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 05:09:14] (step=0316800) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-16 05:09:54] (step=0316900) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 05:10:35] (step=0317000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 05:11:15] (step=0317100) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:11:55] (step=0317200) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:12:36] (step=0317300) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 05:13:16] (step=0317400) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 05:13:57] (step=0317500) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 05:14:37] (step=0317600) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 05:15:17] (step=0317700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:15:58] (step=0317800) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 05:16:38] (step=0317900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 05:17:19] (step=0318000) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:17:31] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 05:18:11] (step=0318100) Train Loss: 0.0678, Train Steps/Sec: 1.90, Samples/Sec: 68.30
+[2025-05-16 05:18:52] (step=0318200) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 05:19:32] (step=0318300) Train Loss: 0.0732, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 05:20:12] (step=0318400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:20:53] (step=0318500) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:21:33] (step=0318600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 05:22:14] (step=0318700) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 05:22:54] (step=0318800) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-16 05:23:34] (step=0318900) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 05:24:15] (step=0319000) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 05:24:55] (step=0319100) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 05:25:19] Beginning epoch 79...
+[2025-05-16 05:25:36] (step=0319200) Train Loss: 0.0685, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-16 05:26:16] (step=0319300) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:26:57] (step=0319400) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 05:27:37] (step=0319500) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 05:28:18] (step=0319600) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 05:28:58] (step=0319700) Train Loss: 0.0604, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 05:29:38] (step=0319800) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 05:30:19] (step=0319900) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:30:59] (step=0320000) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 05:31:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0320000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 05:31:26] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 05:33:26] (step=0320000) Perceptual Loss: 0.3122, Eval Time: 122.41
+[2025-05-16 05:34:06] (step=0320100) Train Loss: 0.0716, Train Steps/Sec: 0.53, Samples/Sec: 19.21
+[2025-05-16 05:34:47] (step=0320200) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 05:35:27] (step=0320300) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 05:36:08] (step=0320400) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:36:48] (step=0320500) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:37:28] (step=0320600) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 05:38:09] (step=0320700) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-16 05:38:49] (step=0320800) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 05:39:30] (step=0320900) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 05:40:10] (step=0321000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 05:40:51] (step=0321100) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 05:41:31] (step=0321200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 05:42:11] (step=0321300) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:42:52] (step=0321400) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-16 05:43:32] (step=0321500) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 05:44:13] (step=0321600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 05:44:53] (step=0321700) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 05:45:33] (step=0321800) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 05:46:14] (step=0321900) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 05:46:54] (step=0322000) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 05:47:07] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 05:47:47] (step=0322100) Train Loss: 0.0685, Train Steps/Sec: 1.89, Samples/Sec: 68.04
+[2025-05-16 05:48:28] (step=0322200) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 05:49:08] (step=0322300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 05:49:48] (step=0322400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:50:29] (step=0322500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 05:51:09] (step=0322600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 05:51:49] (step=0322700) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 05:52:30] (step=0322800) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 05:53:10] (step=0322900) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 05:53:51] (step=0323000) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 05:54:31] (step=0323100) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 05:55:12] (step=0323200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 05:55:12] Beginning epoch 80...
+[2025-05-16 05:55:52] (step=0323300) Train Loss: 0.0660, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-16 05:56:33] (step=0323400) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-16 05:57:13] (step=0323500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:57:54] (step=0323600) Train Loss: 0.0726, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 05:58:34] (step=0323700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 05:59:14] (step=0323800) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 05:59:55] (step=0323900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 06:00:35] (step=0324000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 06:00:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 06:01:28] (step=0324100) Train Loss: 0.0639, Train Steps/Sec: 1.90, Samples/Sec: 68.39
+[2025-05-16 06:02:08] (step=0324200) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 06:02:48] (step=0324300) Train Loss: 0.0593, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 06:03:29] (step=0324400) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 06:04:09] (step=0324500) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 06:04:50] (step=0324600) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 06:05:30] (step=0324700) Train Loss: 0.0619, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 06:06:10] (step=0324800) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 06:06:51] (step=0324900) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 06:07:31] (step=0325000) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 06:08:12] (step=0325100) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 06:08:52] (step=0325200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 06:09:32] (step=0325300) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 06:10:13] (step=0325400) Train Loss: 0.0730, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 06:10:53] (step=0325500) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 06:11:34] (step=0325600) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 06:12:14] (step=0325700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 06:12:54] (step=0325800) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 06:13:35] (step=0325900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 06:14:15] (step=0326000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 06:14:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 06:15:09] (step=0326100) Train Loss: 0.0651, Train Steps/Sec: 1.86, Samples/Sec: 67.09
+[2025-05-16 06:15:49] (step=0326200) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 06:16:30] (step=0326300) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 06:17:10] (step=0326400) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 06:17:50] (step=0326500) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 06:18:31] (step=0326600) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 06:19:11] (step=0326700) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 06:19:52] (step=0326800) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 06:20:32] (step=0326900) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 06:21:12] (step=0327000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 06:21:53] (step=0327100) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 06:22:33] (step=0327200) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 06:22:49] Beginning epoch 81...
+[2025-05-16 06:23:14] (step=0327300) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-16 06:23:54] (step=0327400) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 06:24:35] (step=0327500) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 06:25:15] (step=0327600) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 06:25:55] (step=0327700) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 06:26:36] (step=0327800) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 06:27:16] (step=0327900) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 06:27:57] (step=0328000) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 06:28:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 06:28:49] (step=0328100) Train Loss: 0.0639, Train Steps/Sec: 1.89, Samples/Sec: 68.02
+[2025-05-16 06:29:30] (step=0328200) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 06:30:10] (step=0328300) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 06:30:51] (step=0328400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 06:31:31] (step=0328500) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 06:32:11] (step=0328600) Train Loss: 0.0604, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 06:32:52] (step=0328700) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 06:33:32] (step=0328800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 06:34:13] (step=0328900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 06:34:53] (step=0329000) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 06:35:34] (step=0329100) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 06:36:14] (step=0329200) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 06:36:54] (step=0329300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 06:37:35] (step=0329400) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 06:38:15] (step=0329500) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 06:38:55] (step=0329600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 06:39:36] (step=0329700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 06:40:16] (step=0329800) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 06:40:56] (step=0329900) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 06:41:37] (step=0330000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 06:41:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 06:41:52] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 06:43:52] (step=0330000) Perceptual Loss: 0.2983, Eval Time: 122.52
+[2025-05-16 06:44:33] (step=0330100) Train Loss: 0.0726, Train Steps/Sec: 0.57, Samples/Sec: 20.47
+[2025-05-16 06:45:13] (step=0330200) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 06:45:53] (step=0330300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 06:46:34] (step=0330400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 06:47:14] (step=0330500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 06:47:54] (step=0330600) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 06:48:35] (step=0330700) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 06:49:15] (step=0330800) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 06:49:56] (step=0330900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 06:50:36] (step=0331000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 06:51:16] (step=0331100) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 06:51:57] (step=0331200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 06:52:29] Beginning epoch 82...
+[2025-05-16 06:52:37] (step=0331300) Train Loss: 0.0706, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-16 06:53:18] (step=0331400) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 06:53:58] (step=0331500) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 06:54:38] (step=0331600) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 06:55:19] (step=0331700) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 06:55:59] (step=0331800) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 06:56:39] (step=0331900) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 06:57:20] (step=0332000) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 06:57:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 06:58:12] (step=0332100) Train Loss: 0.0659, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-16 06:58:53] (step=0332200) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 06:59:33] (step=0332300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 07:00:13] (step=0332400) Train Loss: 0.0741, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 07:00:54] (step=0332500) Train Loss: 0.0608, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:01:34] (step=0332600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:02:14] (step=0332700) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 07:02:55] (step=0332800) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 07:03:35] (step=0332900) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 07:04:15] (step=0333000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 07:04:56] (step=0333100) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:05:36] (step=0333200) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 07:06:16] (step=0333300) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 07:06:57] (step=0333400) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 07:07:37] (step=0333500) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 07:08:18] (step=0333600) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 07:08:58] (step=0333700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 07:09:38] (step=0333800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 07:10:18] (step=0333900) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:10:59] (step=0334000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:11:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 07:11:51] (step=0334100) Train Loss: 0.0632, Train Steps/Sec: 1.90, Samples/Sec: 68.49
+[2025-05-16 07:12:32] (step=0334200) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:13:12] (step=0334300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 07:13:52] (step=0334400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:14:33] (step=0334500) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:15:13] (step=0334600) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:15:53] (step=0334700) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 07:16:34] (step=0334800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 07:17:14] (step=0334900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 07:17:54] (step=0335000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:18:35] (step=0335100) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 07:19:15] (step=0335200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 07:19:55] (step=0335300) Train Loss: 0.0610, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 07:20:03] Beginning epoch 83...
+[2025-05-16 07:20:36] (step=0335400) Train Loss: 0.0638, Train Steps/Sec: 2.46, Samples/Sec: 88.58
+[2025-05-16 07:21:16] (step=0335500) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 07:21:57] (step=0335600) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 07:22:37] (step=0335700) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 07:23:18] (step=0335800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 07:23:58] (step=0335900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 07:24:38] (step=0336000) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 07:24:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 07:25:31] (step=0336100) Train Loss: 0.0696, Train Steps/Sec: 1.91, Samples/Sec: 68.62
+[2025-05-16 07:26:11] (step=0336200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 07:26:51] (step=0336300) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 07:27:32] (step=0336400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:28:12] (step=0336500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:28:52] (step=0336600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:29:33] (step=0336700) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 07:30:13] (step=0336800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 07:30:53] (step=0336900) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 07:31:34] (step=0337000) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 07:32:14] (step=0337100) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 07:32:54] (step=0337200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:33:35] (step=0337300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:34:15] (step=0337400) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 07:34:55] (step=0337500) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 07:35:36] (step=0337600) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 07:36:16] (step=0337700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:36:57] (step=0337800) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 07:37:37] (step=0337900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 07:38:17] (step=0338000) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 07:38:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 07:39:10] (step=0338100) Train Loss: 0.0671, Train Steps/Sec: 1.90, Samples/Sec: 68.43
+[2025-05-16 07:39:50] (step=0338200) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 07:40:30] (step=0338300) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 07:41:11] (step=0338400) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 07:41:51] (step=0338500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 07:42:31] (step=0338600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:43:12] (step=0338700) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 07:43:52] (step=0338800) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 07:44:32] (step=0338900) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 07:45:13] (step=0339000) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 07:45:53] (step=0339100) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 07:46:33] (step=0339200) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 07:47:14] (step=0339300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 07:47:38] Beginning epoch 84...
+[2025-05-16 07:47:54] (step=0339400) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.63
+[2025-05-16 07:48:35] (step=0339500) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 07:49:15] (step=0339600) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 07:49:55] (step=0339700) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 07:50:36] (step=0339800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:51:16] (step=0339900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 07:51:56] (step=0340000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 07:52:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0340000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 07:52:23] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 07:54:22] (step=0340000) Perceptual Loss: 0.3187, Eval Time: 122.59
+[2025-05-16 07:55:03] (step=0340100) Train Loss: 0.0716, Train Steps/Sec: 0.54, Samples/Sec: 19.33
+[2025-05-16 07:55:43] (step=0340200) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 07:56:23] (step=0340300) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 07:57:04] (step=0340400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 07:57:44] (step=0340500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:58:24] (step=0340600) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 07:59:05] (step=0340700) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 07:59:45] (step=0340800) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:00:25] (step=0340900) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 08:01:06] (step=0341000) Train Loss: 0.0604, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:01:46] (step=0341100) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 08:02:26] (step=0341200) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:03:07] (step=0341300) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 08:03:47] (step=0341400) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 08:04:28] (step=0341500) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:05:08] (step=0341600) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 08:05:48] (step=0341700) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 08:06:29] (step=0341800) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:07:09] (step=0341900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:07:49] (step=0342000) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:08:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 08:08:42] (step=0342100) Train Loss: 0.0662, Train Steps/Sec: 1.91, Samples/Sec: 68.82
+[2025-05-16 08:09:22] (step=0342200) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 08:10:02] (step=0342300) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 08:10:42] (step=0342400) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 08:11:23] (step=0342500) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 08:12:03] (step=0342600) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 08:12:43] (step=0342700) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 08:13:24] (step=0342800) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 08:14:04] (step=0342900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 08:14:45] (step=0343000) Train Loss: 0.0617, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 08:15:25] (step=0343100) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 08:16:05] (step=0343200) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 08:16:46] (step=0343300) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 08:17:26] (step=0343400) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 08:17:26] Beginning epoch 85...
+[2025-05-16 08:18:07] (step=0343500) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-16 08:18:47] (step=0343600) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 08:19:27] (step=0343700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 08:20:08] (step=0343800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 08:20:48] (step=0343900) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 08:21:28] (step=0344000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 08:21:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 08:22:21] (step=0344100) Train Loss: 0.0655, Train Steps/Sec: 1.91, Samples/Sec: 68.64
+[2025-05-16 08:23:01] (step=0344200) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 08:23:42] (step=0344300) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 08:24:22] (step=0344400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 08:25:02] (step=0344500) Train Loss: 0.0752, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:25:43] (step=0344600) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 08:26:23] (step=0344700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:27:03] (step=0344800) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 08:27:44] (step=0344900) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:28:24] (step=0345000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 08:29:04] (step=0345100) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:29:45] (step=0345200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:30:25] (step=0345300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:31:05] (step=0345400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 08:31:46] (step=0345500) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 08:32:26] (step=0345600) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 08:33:06] (step=0345700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:33:47] (step=0345800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:34:27] (step=0345900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:35:07] (step=0346000) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:35:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 08:36:00] (step=0346100) Train Loss: 0.0680, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-16 08:36:40] (step=0346200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 08:37:21] (step=0346300) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 08:38:01] (step=0346400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:38:41] (step=0346500) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 08:39:21] (step=0346600) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 08:40:02] (step=0346700) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 08:40:42] (step=0346800) Train Loss: 0.0623, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 08:41:23] (step=0346900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:42:03] (step=0347000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 08:42:43] (step=0347100) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 08:43:23] (step=0347200) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 08:44:04] (step=0347300) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:44:44] (step=0347400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 08:45:00] Beginning epoch 86...
+[2025-05-16 08:45:25] (step=0347500) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.49
+[2025-05-16 08:46:05] (step=0347600) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 08:46:46] (step=0347700) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:47:26] (step=0347800) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:48:06] (step=0347900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 08:48:46] (step=0348000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:48:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 08:49:39] (step=0348100) Train Loss: 0.0707, Train Steps/Sec: 1.89, Samples/Sec: 68.08
+[2025-05-16 08:50:20] (step=0348200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 08:51:00] (step=0348300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 08:51:40] (step=0348400) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 08:52:21] (step=0348500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 08:53:01] (step=0348600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 08:53:41] (step=0348700) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:54:22] (step=0348800) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 08:55:02] (step=0348900) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 08:55:43] (step=0349000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 08:56:23] (step=0349100) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 08:57:03] (step=0349200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 08:57:43] (step=0349300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 08:58:24] (step=0349400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 08:59:04] (step=0349500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 08:59:45] (step=0349600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 09:00:25] (step=0349700) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 09:01:05] (step=0349800) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 09:01:46] (step=0349900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 09:02:26] (step=0350000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 09:02:39] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 09:02:42] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 09:04:41] (step=0350000) Perceptual Loss: 0.3006, Eval Time: 122.57
+[2025-05-16 09:05:22] (step=0350100) Train Loss: 0.0731, Train Steps/Sec: 0.57, Samples/Sec: 20.46
+[2025-05-16 09:06:02] (step=0350200) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 09:06:43] (step=0350300) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 09:07:23] (step=0350400) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 09:08:03] (step=0350500) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 09:08:44] (step=0350600) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 09:09:24] (step=0350700) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 09:10:04] (step=0350800) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 09:10:45] (step=0350900) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 09:11:25] (step=0351000) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 09:12:05] (step=0351100) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 09:12:46] (step=0351200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 09:13:26] (step=0351300) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 09:14:07] (step=0351400) Train Loss: 0.0664, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-16 09:14:39] Beginning epoch 87...
+[2025-05-16 09:14:47] (step=0351500) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-16 09:15:28] (step=0351600) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 09:16:08] (step=0351700) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 09:16:48] (step=0351800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 09:17:29] (step=0351900) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 09:18:09] (step=0352000) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 09:18:21] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 09:19:02] (step=0352100) Train Loss: 0.0657, Train Steps/Sec: 1.90, Samples/Sec: 68.30
+[2025-05-16 09:19:42] (step=0352200) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-16 09:20:23] (step=0352300) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 09:21:03] (step=0352400) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 09:21:44] (step=0352500) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 09:22:24] (step=0352600) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 09:23:04] (step=0352700) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 09:23:45] (step=0352800) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-16 09:24:25] (step=0352900) Train Loss: 0.0741, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-16 09:25:06] (step=0353000) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 09:25:46] (step=0353100) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 09:26:27] (step=0353200) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 09:27:07] (step=0353300) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 09:27:48] (step=0353400) Train Loss: 0.0704, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-16 09:28:28] (step=0353500) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 09:29:09] (step=0353600) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-16 09:29:50] (step=0353700) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-16 09:30:30] (step=0353800) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 09:31:11] (step=0353900) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 09:31:51] (step=0354000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 09:32:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 09:32:44] (step=0354100) Train Loss: 0.0657, Train Steps/Sec: 1.88, Samples/Sec: 67.82
+[2025-05-16 09:33:25] (step=0354200) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 09:34:05] (step=0354300) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-16 09:34:46] (step=0354400) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-16 09:35:26] (step=0354500) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 09:36:07] (step=0354600) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 09:36:47] (step=0354700) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 09:37:28] (step=0354800) Train Loss: 0.0703, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-16 09:38:08] (step=0354900) Train Loss: 0.0670, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-16 09:38:49] (step=0355000) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-16 09:39:29] (step=0355100) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 09:40:10] (step=0355200) Train Loss: 0.0613, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 09:40:50] (step=0355300) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-16 09:41:31] (step=0355400) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 09:42:11] (step=0355500) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 09:42:19] Beginning epoch 88...
+[2025-05-16 09:42:52] (step=0355600) Train Loss: 0.0679, Train Steps/Sec: 2.46, Samples/Sec: 88.46
+[2025-05-16 09:43:32] (step=0355700) Train Loss: 0.0606, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 09:44:13] (step=0355800) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 09:44:53] (step=0355900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 09:45:33] (step=0356000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 09:45:46] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 09:46:26] (step=0356100) Train Loss: 0.0718, Train Steps/Sec: 1.89, Samples/Sec: 67.94
+[2025-05-16 09:47:06] (step=0356200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 09:47:47] (step=0356300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 09:48:27] (step=0356400) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 09:49:07] (step=0356500) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 09:49:48] (step=0356600) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 09:50:28] (step=0356700) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 09:51:09] (step=0356800) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 09:51:49] (step=0356900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 09:52:29] (step=0357000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 09:53:09] (step=0357100) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 09:53:50] (step=0357200) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 09:54:30] (step=0357300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 09:55:10] (step=0357400) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 09:55:51] (step=0357500) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 09:56:31] (step=0357600) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 09:57:12] (step=0357700) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 09:57:52] (step=0357800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 09:58:32] (step=0357900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 09:59:12] (step=0358000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 09:59:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 10:00:05] (step=0358100) Train Loss: 0.0618, Train Steps/Sec: 1.92, Samples/Sec: 69.02
+[2025-05-16 10:00:45] (step=0358200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 10:01:25] (step=0358300) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 10:02:06] (step=0358400) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 10:02:46] (step=0358500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:03:26] (step=0358600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:04:07] (step=0358700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 10:04:47] (step=0358800) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 10:05:27] (step=0358900) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 10:06:08] (step=0359000) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 10:06:48] (step=0359100) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 10:07:28] (step=0359200) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 10:08:09] (step=0359300) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 10:08:49] (step=0359400) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 10:09:30] (step=0359500) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 10:09:54] Beginning epoch 89...
+[2025-05-16 10:10:10] (step=0359600) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-16 10:10:50] (step=0359700) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 10:11:31] (step=0359800) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 10:12:11] (step=0359900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:12:51] (step=0360000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 10:13:15] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0360000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 10:13:18] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 10:15:17] (step=0360000) Perceptual Loss: 0.3067, Eval Time: 122.36
+[2025-05-16 10:15:58] (step=0360100) Train Loss: 0.0688, Train Steps/Sec: 0.54, Samples/Sec: 19.29
+[2025-05-16 10:16:38] (step=0360200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 10:17:19] (step=0360300) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 10:17:59] (step=0360400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 10:18:39] (step=0360500) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 10:19:20] (step=0360600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 10:20:00] (step=0360700) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 10:20:40] (step=0360800) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 10:21:21] (step=0360900) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 10:22:01] (step=0361000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 10:22:42] (step=0361100) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 10:23:22] (step=0361200) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 10:24:02] (step=0361300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 10:24:43] (step=0361400) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 10:25:23] (step=0361500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 10:26:03] (step=0361600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 10:26:44] (step=0361700) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 10:27:24] (step=0361800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 10:28:04] (step=0361900) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 10:28:45] (step=0362000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 10:28:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 10:29:37] (step=0362100) Train Loss: 0.0666, Train Steps/Sec: 1.90, Samples/Sec: 68.28
+[2025-05-16 10:30:18] (step=0362200) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:30:58] (step=0362300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 10:31:38] (step=0362400) Train Loss: 0.0738, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:32:19] (step=0362500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 10:32:59] (step=0362600) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 10:33:39] (step=0362700) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 10:34:20] (step=0362800) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 10:35:00] (step=0362900) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 10:35:41] (step=0363000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:36:21] (step=0363100) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 10:37:01] (step=0363200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 10:37:42] (step=0363300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 10:38:22] (step=0363400) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 10:39:02] (step=0363500) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 10:39:43] (step=0363600) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 10:39:43] Beginning epoch 90...
+[2025-05-16 10:40:23] (step=0363700) Train Loss: 0.0638, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-16 10:41:04] (step=0363800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 10:41:44] (step=0363900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:42:24] (step=0364000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:42:37] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 10:43:17] (step=0364100) Train Loss: 0.0731, Train Steps/Sec: 1.89, Samples/Sec: 68.10
+[2025-05-16 10:43:57] (step=0364200) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 10:44:38] (step=0364300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 10:45:18] (step=0364400) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:45:58] (step=0364500) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 10:46:39] (step=0364600) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 10:47:19] (step=0364700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:47:59] (step=0364800) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 10:48:40] (step=0364900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 10:49:20] (step=0365000) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 10:50:00] (step=0365100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:50:41] (step=0365200) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 10:51:21] (step=0365300) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 10:52:02] (step=0365400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 10:52:42] (step=0365500) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 10:53:22] (step=0365600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 10:54:03] (step=0365700) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:54:43] (step=0365800) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:55:23] (step=0365900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 10:56:04] (step=0366000) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 10:56:16] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 10:56:56] (step=0366100) Train Loss: 0.0698, Train Steps/Sec: 1.90, Samples/Sec: 68.34
+[2025-05-16 10:57:37] (step=0366200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 10:58:17] (step=0366300) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 10:58:57] (step=0366400) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 10:59:38] (step=0366500) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 11:00:18] (step=0366600) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 11:00:58] (step=0366700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 11:01:39] (step=0366800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 11:02:19] (step=0366900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 11:02:59] (step=0367000) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 11:03:40] (step=0367100) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 11:04:20] (step=0367200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 11:05:00] (step=0367300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 11:05:41] (step=0367400) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 11:06:21] (step=0367500) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 11:07:02] (step=0367600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 11:07:18] Beginning epoch 91...
+[2025-05-16 11:07:42] (step=0367700) Train Loss: 0.0621, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-16 11:08:22] (step=0367800) Train Loss: 0.0595, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 11:09:03] (step=0367900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:09:43] (step=0368000) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 11:09:55] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 11:10:35] (step=0368100) Train Loss: 0.0688, Train Steps/Sec: 1.91, Samples/Sec: 68.93
+[2025-05-16 11:11:16] (step=0368200) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 11:11:56] (step=0368300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 11:12:37] (step=0368400) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 11:13:17] (step=0368500) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 11:13:57] (step=0368600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 11:14:38] (step=0368700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 11:15:18] (step=0368800) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-16 11:15:59] (step=0368900) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 11:16:39] (step=0369000) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 11:17:19] (step=0369100) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 11:18:00] (step=0369200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 11:18:40] (step=0369300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 11:19:20] (step=0369400) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 11:20:01] (step=0369500) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 11:20:41] (step=0369600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 11:21:21] (step=0369700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 11:22:02] (step=0369800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 11:22:42] (step=0369900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 11:23:22] (step=0370000) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 11:23:35] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 11:23:38] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 11:25:38] (step=0370000) Perceptual Loss: 0.3162, Eval Time: 122.64
+[2025-05-16 11:26:18] (step=0370100) Train Loss: 0.0661, Train Steps/Sec: 0.57, Samples/Sec: 20.48
+[2025-05-16 11:26:59] (step=0370200) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 11:27:39] (step=0370300) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 11:28:19] (step=0370400) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 11:29:00] (step=0370500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 11:29:40] (step=0370600) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 11:30:20] (step=0370700) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 11:31:01] (step=0370800) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 11:31:41] (step=0370900) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 11:32:22] (step=0371000) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 11:33:02] (step=0371100) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 11:33:42] (step=0371200) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:34:23] (step=0371300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 11:35:03] (step=0371400) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 11:35:43] (step=0371500) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 11:36:24] (step=0371600) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 11:36:56] Beginning epoch 92...
+[2025-05-16 11:37:04] (step=0371700) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-16 11:37:45] (step=0371800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 11:38:25] (step=0371900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 11:39:05] (step=0372000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 11:39:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 11:39:58] (step=0372100) Train Loss: 0.0658, Train Steps/Sec: 1.90, Samples/Sec: 68.39
+[2025-05-16 11:40:38] (step=0372200) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 11:41:19] (step=0372300) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 11:41:59] (step=0372400) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 11:42:39] (step=0372500) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 11:43:20] (step=0372600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:44:00] (step=0372700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 11:44:41] (step=0372800) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 11:45:21] (step=0372900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 11:46:01] (step=0373000) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:46:42] (step=0373100) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 11:47:22] (step=0373200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 11:48:02] (step=0373300) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 11:48:43] (step=0373400) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 11:49:23] (step=0373500) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 11:50:03] (step=0373600) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 11:50:44] (step=0373700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 11:51:24] (step=0373800) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 11:52:04] (step=0373900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:52:45] (step=0374000) Train Loss: 0.0771, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 11:52:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 11:53:37] (step=0374100) Train Loss: 0.0649, Train Steps/Sec: 1.91, Samples/Sec: 68.77
+[2025-05-16 11:54:17] (step=0374200) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 11:54:58] (step=0374300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 11:55:38] (step=0374400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:56:18] (step=0374500) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 11:56:59] (step=0374600) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:57:39] (step=0374700) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:58:20] (step=0374800) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 11:59:00] (step=0374900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 11:59:40] (step=0375000) Train Loss: 0.0595, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 12:00:21] (step=0375100) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 12:01:01] (step=0375200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:01:41] (step=0375300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 12:02:22] (step=0375400) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:03:02] (step=0375500) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 12:03:42] (step=0375600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 12:04:23] (step=0375700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:04:31] Beginning epoch 93...
+[2025-05-16 12:05:03] (step=0375800) Train Loss: 0.0717, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-16 12:05:44] (step=0375900) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:06:24] (step=0376000) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:06:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 12:07:17] (step=0376100) Train Loss: 0.0664, Train Steps/Sec: 1.90, Samples/Sec: 68.24
+[2025-05-16 12:07:57] (step=0376200) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 12:08:37] (step=0376300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 12:09:18] (step=0376400) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 12:09:58] (step=0376500) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 12:10:38] (step=0376600) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 12:11:19] (step=0376700) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 12:11:59] (step=0376800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 12:12:39] (step=0376900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:13:20] (step=0377000) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 12:14:00] (step=0377100) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 12:14:40] (step=0377200) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:15:21] (step=0377300) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 12:16:01] (step=0377400) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 12:16:41] (step=0377500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 12:17:22] (step=0377600) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 12:18:02] (step=0377700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:18:43] (step=0377800) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:19:23] (step=0377900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 12:20:03] (step=0378000) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:20:15] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 12:20:56] (step=0378100) Train Loss: 0.0671, Train Steps/Sec: 1.91, Samples/Sec: 68.60
+[2025-05-16 12:21:36] (step=0378200) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 12:22:16] (step=0378300) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 12:22:57] (step=0378400) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:23:37] (step=0378500) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:24:17] (step=0378600) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:24:58] (step=0378700) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 12:25:38] (step=0378800) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-16 12:26:19] (step=0378900) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:26:59] (step=0379000) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 12:27:39] (step=0379100) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:28:20] (step=0379200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 12:29:00] (step=0379300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 12:29:40] (step=0379400) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-16 12:30:21] (step=0379500) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.58
+[2025-05-16 12:31:01] (step=0379600) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 12:31:42] (step=0379700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 12:32:06] Beginning epoch 94...
+[2025-05-16 12:32:22] (step=0379800) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-16 12:33:03] (step=0379900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 12:33:43] (step=0380000) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 12:34:07] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0380000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 12:34:10] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 12:36:10] (step=0380000) Perceptual Loss: 0.3298, Eval Time: 122.46
+[2025-05-16 12:36:50] (step=0380100) Train Loss: 0.0645, Train Steps/Sec: 0.53, Samples/Sec: 19.20
+[2025-05-16 12:37:31] (step=0380200) Train Loss: 0.0780, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 12:38:11] (step=0380300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 12:38:52] (step=0380400) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 12:39:32] (step=0380500) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 12:40:12] (step=0380600) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 12:40:53] (step=0380700) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-16 12:41:33] (step=0380800) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 12:42:13] (step=0380900) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 12:42:54] (step=0381000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 12:43:34] (step=0381100) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 12:44:14] (step=0381200) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 12:44:55] (step=0381300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:45:35] (step=0381400) Train Loss: 0.0595, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 12:46:16] (step=0381500) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 12:46:56] (step=0381600) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 12:47:36] (step=0381700) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 12:48:17] (step=0381800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 12:48:57] (step=0381900) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 12:49:37] (step=0382000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 12:49:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 12:50:31] (step=0382100) Train Loss: 0.0698, Train Steps/Sec: 1.87, Samples/Sec: 67.41
+[2025-05-16 12:51:11] (step=0382200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 12:51:52] (step=0382300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 12:52:32] (step=0382400) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 12:53:12] (step=0382500) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:53:52] (step=0382600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:54:33] (step=0382700) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 12:55:13] (step=0382800) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 12:55:54] (step=0382900) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 12:56:34] (step=0383000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 12:57:14] (step=0383100) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 12:57:55] (step=0383200) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 12:58:35] (step=0383300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 12:59:15] (step=0383400) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 12:59:56] (step=0383500) Train Loss: 0.0741, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 13:00:36] (step=0383600) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 13:01:17] (step=0383700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 13:01:57] (step=0383800) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 13:01:57] Beginning epoch 95...
+[2025-05-16 13:02:37] (step=0383900) Train Loss: 0.0651, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-16 13:03:18] (step=0384000) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 13:03:31] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 13:04:12] (step=0384100) Train Loss: 0.0663, Train Steps/Sec: 1.85, Samples/Sec: 66.78
+[2025-05-16 13:04:52] (step=0384200) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 13:05:32] (step=0384300) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 13:06:13] (step=0384400) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 13:06:53] (step=0384500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 13:07:34] (step=0384600) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 13:08:14] (step=0384700) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 13:08:54] (step=0384800) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-16 13:09:35] (step=0384900) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 13:10:15] (step=0385000) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 13:10:56] (step=0385100) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 13:11:36] (step=0385200) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 13:12:16] (step=0385300) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 13:12:57] (step=0385400) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 13:13:37] (step=0385500) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 13:14:18] (step=0385600) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 13:14:58] (step=0385700) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 13:15:38] (step=0385800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 13:16:19] (step=0385900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 13:16:59] (step=0386000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 13:17:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 13:17:52] (step=0386100) Train Loss: 0.0682, Train Steps/Sec: 1.90, Samples/Sec: 68.44
+[2025-05-16 13:18:32] (step=0386200) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 13:19:13] (step=0386300) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 13:19:53] (step=0386400) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 13:20:33] (step=0386500) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 13:21:14] (step=0386600) Train Loss: 0.0733, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 13:21:54] (step=0386700) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 13:22:35] (step=0386800) Train Loss: 0.0650, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-16 13:23:15] (step=0386900) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 13:23:56] (step=0387000) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 13:24:36] (step=0387100) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 13:25:17] (step=0387200) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 13:25:57] (step=0387300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 13:26:37] (step=0387400) Train Loss: 0.0733, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-16 13:27:18] (step=0387500) Train Loss: 0.0608, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 13:27:58] (step=0387600) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 13:28:39] (step=0387700) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 13:29:19] (step=0387800) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 13:29:35] Beginning epoch 96...
+[2025-05-16 13:30:00] (step=0387900) Train Loss: 0.0711, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-16 13:30:40] (step=0388000) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 13:30:53] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 13:31:33] (step=0388100) Train Loss: 0.0675, Train Steps/Sec: 1.88, Samples/Sec: 67.85
+[2025-05-16 13:32:14] (step=0388200) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 13:32:54] (step=0388300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 13:33:34] (step=0388400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 13:34:15] (step=0388500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 13:34:55] (step=0388600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 13:35:35] (step=0388700) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 13:36:16] (step=0388800) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 13:36:56] (step=0388900) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 13:37:36] (step=0389000) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 13:38:17] (step=0389100) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 13:38:57] (step=0389200) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 13:39:37] (step=0389300) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 13:40:18] (step=0389400) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 13:40:58] (step=0389500) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 13:41:38] (step=0389600) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 13:42:19] (step=0389700) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 13:42:59] (step=0389800) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 13:43:39] (step=0389900) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 13:44:20] (step=0390000) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 13:44:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 13:44:35] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 13:46:35] (step=0390000) Perceptual Loss: 0.3054, Eval Time: 122.65
+[2025-05-16 13:47:16] (step=0390100) Train Loss: 0.0718, Train Steps/Sec: 0.57, Samples/Sec: 20.48
+[2025-05-16 13:47:56] (step=0390200) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-16 13:48:37] (step=0390300) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 13:49:17] (step=0390400) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 13:49:57] (step=0390500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 13:50:38] (step=0390600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 13:51:18] (step=0390700) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 13:51:59] (step=0390800) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 13:52:39] (step=0390900) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 13:53:19] (step=0391000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 13:54:00] (step=0391100) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 13:54:40] (step=0391200) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 13:55:20] (step=0391300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 13:56:01] (step=0391400) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 13:56:41] (step=0391500) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 13:57:22] (step=0391600) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 13:58:02] (step=0391700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 13:58:42] (step=0391800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 13:59:14] Beginning epoch 97...
+[2025-05-16 13:59:23] (step=0391900) Train Loss: 0.0641, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-16 14:00:03] (step=0392000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 14:00:16] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 14:00:57] (step=0392100) Train Loss: 0.0683, Train Steps/Sec: 1.87, Samples/Sec: 67.18
+[2025-05-16 14:01:37] (step=0392200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 14:02:17] (step=0392300) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 14:02:58] (step=0392400) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 14:03:38] (step=0392500) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 14:04:18] (step=0392600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 14:04:59] (step=0392700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 14:05:39] (step=0392800) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 14:06:20] (step=0392900) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 14:07:00] (step=0393000) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 14:07:40] (step=0393100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 14:08:21] (step=0393200) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 14:09:01] (step=0393300) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 14:09:41] (step=0393400) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 14:10:22] (step=0393500) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 14:11:02] (step=0393600) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 14:11:43] (step=0393700) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 14:12:23] (step=0393800) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 14:13:04] (step=0393900) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 14:13:44] (step=0394000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 14:13:56] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 14:14:37] (step=0394100) Train Loss: 0.0690, Train Steps/Sec: 1.89, Samples/Sec: 68.12
+[2025-05-16 14:15:17] (step=0394200) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 14:15:58] (step=0394300) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-16 14:16:38] (step=0394400) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 14:17:19] (step=0394500) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 14:17:59] (step=0394600) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 14:18:39] (step=0394700) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 14:19:20] (step=0394800) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-16 14:20:00] (step=0394900) Train Loss: 0.0750, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 14:20:41] (step=0395000) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-16 14:21:21] (step=0395100) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 14:22:02] (step=0395200) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 14:22:42] (step=0395300) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 14:23:23] (step=0395400) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 14:24:03] (step=0395500) Train Loss: 0.0698, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-16 14:24:44] (step=0395600) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 14:25:24] (step=0395700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 14:26:04] (step=0395800) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 14:26:45] (step=0395900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 14:26:53] Beginning epoch 98...
+[2025-05-16 14:27:25] (step=0396000) Train Loss: 0.0668, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-16 14:27:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 14:28:18] (step=0396100) Train Loss: 0.0706, Train Steps/Sec: 1.88, Samples/Sec: 67.72
+[2025-05-16 14:28:59] (step=0396200) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 14:29:39] (step=0396300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 14:30:19] (step=0396400) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 14:31:00] (step=0396500) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 14:31:40] (step=0396600) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 14:32:20] (step=0396700) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 14:33:01] (step=0396800) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 14:33:41] (step=0396900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 14:34:21] (step=0397000) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 14:35:02] (step=0397100) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 14:35:42] (step=0397200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 14:36:22] (step=0397300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 14:37:03] (step=0397400) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 14:37:43] (step=0397500) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 14:38:24] (step=0397600) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 14:39:04] (step=0397700) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 14:39:44] (step=0397800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 14:40:25] (step=0397900) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 14:41:05] (step=0398000) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 14:41:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 14:41:58] (step=0398100) Train Loss: 0.0709, Train Steps/Sec: 1.90, Samples/Sec: 68.29
+[2025-05-16 14:42:38] (step=0398200) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 14:43:18] (step=0398300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 14:43:59] (step=0398400) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 14:44:39] (step=0398500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 14:45:19] (step=0398600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 14:46:00] (step=0398700) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 14:46:40] (step=0398800) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 14:47:20] (step=0398900) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 14:48:01] (step=0399000) Train Loss: 0.0739, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 14:48:41] (step=0399100) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 14:49:21] (step=0399200) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 14:50:02] (step=0399300) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 14:50:42] (step=0399400) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 14:51:23] (step=0399500) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 14:52:03] (step=0399600) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 14:52:43] (step=0399700) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 14:53:24] (step=0399800) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 14:54:04] (step=0399900) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 14:54:28] Beginning epoch 99...
+[2025-05-16 14:54:44] (step=0400000) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 14:55:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0400000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 14:55:12] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 14:57:12] (step=0400000) Perceptual Loss: 0.3133, Eval Time: 122.80
+[2025-05-16 14:57:52] (step=0400100) Train Loss: 0.0638, Train Steps/Sec: 0.53, Samples/Sec: 19.15
+[2025-05-16 14:58:33] (step=0400200) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 14:59:13] (step=0400300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 14:59:53] (step=0400400) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 15:00:34] (step=0400500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 15:01:14] (step=0400600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 15:01:55] (step=0400700) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 15:02:35] (step=0400800) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 15:03:15] (step=0400900) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 15:03:56] (step=0401000) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 15:04:36] (step=0401100) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 15:05:16] (step=0401200) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 15:05:57] (step=0401300) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 15:06:37] (step=0401400) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 15:07:18] (step=0401500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 15:07:58] (step=0401600) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 15:08:38] (step=0401700) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 15:09:19] (step=0401800) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 15:09:59] (step=0401900) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 15:10:39] (step=0402000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 15:10:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 15:11:32] (step=0402100) Train Loss: 0.0621, Train Steps/Sec: 1.91, Samples/Sec: 68.75
+[2025-05-16 15:12:12] (step=0402200) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 15:12:52] (step=0402300) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 15:13:33] (step=0402400) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 15:14:13] (step=0402500) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 15:14:53] (step=0402600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 15:15:34] (step=0402700) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 15:16:14] (step=0402800) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 15:16:55] (step=0402900) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 15:17:35] (step=0403000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 15:18:15] (step=0403100) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 15:18:56] (step=0403200) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 15:19:36] (step=0403300) Train Loss: 0.0750, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 15:20:16] (step=0403400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 15:20:57] (step=0403500) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 15:21:37] (step=0403600) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 15:22:17] (step=0403700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 15:22:58] (step=0403800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:23:38] (step=0403900) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 15:24:18] (step=0404000) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.40
+[2025-05-16 15:24:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 15:24:30] Beginning epoch 100...
+[2025-05-16 15:25:11] (step=0404100) Train Loss: 0.0659, Train Steps/Sec: 1.89, Samples/Sec: 68.09
+[2025-05-16 15:25:52] (step=0404200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:26:32] (step=0404300) Train Loss: 0.0614, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 15:27:12] (step=0404400) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 15:27:52] (step=0404500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 15:28:33] (step=0404600) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 15:29:13] (step=0404700) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:29:54] (step=0404800) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 15:30:34] (step=0404900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 15:31:14] (step=0405000) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 15:31:55] (step=0405100) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 15:32:35] (step=0405200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 15:33:15] (step=0405300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 15:33:56] (step=0405400) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 15:34:36] (step=0405500) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 15:35:16] (step=0405600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 15:35:57] (step=0405700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:36:37] (step=0405800) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 15:37:17] (step=0405900) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 15:37:58] (step=0406000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:38:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 15:38:50] (step=0406100) Train Loss: 0.0663, Train Steps/Sec: 1.90, Samples/Sec: 68.47
+[2025-05-16 15:39:31] (step=0406200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:40:11] (step=0406300) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 15:40:51] (step=0406400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 15:41:32] (step=0406500) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 15:42:12] (step=0406600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 15:42:52] (step=0406700) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 15:43:33] (step=0406800) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 15:44:13] (step=0406900) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 15:44:53] (step=0407000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 15:45:34] (step=0407100) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:46:14] (step=0407200) Train Loss: 0.0612, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:46:54] (step=0407300) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 15:47:35] (step=0407400) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:48:15] (step=0407500) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 15:48:55] (step=0407600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:49:36] (step=0407700) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 15:50:16] (step=0407800) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 15:50:56] (step=0407900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 15:51:37] (step=0408000) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 15:51:49] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 15:52:05] Beginning epoch 101...
+[2025-05-16 15:52:29] (step=0408100) Train Loss: 0.0686, Train Steps/Sec: 1.90, Samples/Sec: 68.33
+[2025-05-16 15:53:10] (step=0408200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 15:53:50] (step=0408300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 15:54:30] (step=0408400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 15:55:11] (step=0408500) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 15:55:51] (step=0408600) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 15:56:31] (step=0408700) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 15:57:12] (step=0408800) Train Loss: 0.0608, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 15:57:52] (step=0408900) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 15:58:33] (step=0409000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 15:59:13] (step=0409100) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 15:59:53] (step=0409200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:00:33] (step=0409300) Train Loss: 0.0593, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 16:01:14] (step=0409400) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:01:54] (step=0409500) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 16:02:35] (step=0409600) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 16:03:15] (step=0409700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 16:03:55] (step=0409800) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 16:04:36] (step=0409900) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:05:16] (step=0410000) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 16:05:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 16:05:30] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 16:07:30] (step=0410000) Perceptual Loss: 0.3141, Eval Time: 122.67
+[2025-05-16 16:08:11] (step=0410100) Train Loss: 0.0675, Train Steps/Sec: 0.57, Samples/Sec: 20.58
+[2025-05-16 16:08:51] (step=0410200) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 16:09:31] (step=0410300) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 16:10:12] (step=0410400) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 16:10:52] (step=0410500) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 16:11:33] (step=0410600) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 16:12:13] (step=0410700) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 16:12:53] (step=0410800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 16:13:34] (step=0410900) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 16:14:14] (step=0411000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 16:14:54] (step=0411100) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 16:15:35] (step=0411200) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 16:16:15] (step=0411300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 16:16:56] (step=0411400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 16:17:36] (step=0411500) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 16:18:16] (step=0411600) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 16:18:57] (step=0411700) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 16:19:37] (step=0411800) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 16:20:17] (step=0411900) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 16:20:58] (step=0412000) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 16:21:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 16:21:42] Beginning epoch 102...
+[2025-05-16 16:21:50] (step=0412100) Train Loss: 0.0644, Train Steps/Sec: 1.91, Samples/Sec: 68.62
+[2025-05-16 16:22:31] (step=0412200) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 16:23:11] (step=0412300) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 16:23:51] (step=0412400) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 16:24:32] (step=0412500) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 16:25:12] (step=0412600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 16:25:52] (step=0412700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 16:26:33] (step=0412800) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 16:27:13] (step=0412900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 16:27:53] (step=0413000) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 16:28:34] (step=0413100) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 16:29:14] (step=0413200) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 16:29:54] (step=0413300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:30:35] (step=0413400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 16:31:15] (step=0413500) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 16:31:56] (step=0413600) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 16:32:36] (step=0413700) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:33:16] (step=0413800) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 16:33:57] (step=0413900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 16:34:37] (step=0414000) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 16:34:49] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 16:35:29] (step=0414100) Train Loss: 0.0642, Train Steps/Sec: 1.90, Samples/Sec: 68.36
+[2025-05-16 16:36:10] (step=0414200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 16:36:50] (step=0414300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 16:37:30] (step=0414400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:38:11] (step=0414500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 16:38:51] (step=0414600) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 16:39:31] (step=0414700) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 16:40:12] (step=0414800) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 16:40:52] (step=0414900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 16:41:33] (step=0415000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 16:42:13] (step=0415100) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 16:42:53] (step=0415200) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 16:43:34] (step=0415300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 16:44:14] (step=0415400) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 16:44:54] (step=0415500) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 16:45:35] (step=0415600) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 16:46:15] (step=0415700) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 16:46:55] (step=0415800) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:47:36] (step=0415900) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 16:48:16] (step=0416000) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 16:48:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 16:49:08] (step=0416100) Train Loss: 0.0671, Train Steps/Sec: 1.91, Samples/Sec: 68.85
+[2025-05-16 16:49:16] Beginning epoch 103...
+[2025-05-16 16:49:49] (step=0416200) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-16 16:50:29] (step=0416300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 16:51:10] (step=0416400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 16:51:50] (step=0416500) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 16:52:30] (step=0416600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 16:53:10] (step=0416700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 16:53:51] (step=0416800) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 16:54:31] (step=0416900) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 16:55:12] (step=0417000) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 16:55:52] (step=0417100) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:56:32] (step=0417200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 16:57:12] (step=0417300) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 16:57:53] (step=0417400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 16:58:33] (step=0417500) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-16 16:59:14] (step=0417600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 16:59:54] (step=0417700) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 17:00:34] (step=0417800) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 17:01:15] (step=0417900) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 17:01:55] (step=0418000) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 17:02:07] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 17:02:48] (step=0418100) Train Loss: 0.0696, Train Steps/Sec: 1.89, Samples/Sec: 68.10
+[2025-05-16 17:03:28] (step=0418200) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 17:04:09] (step=0418300) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 17:04:49] (step=0418400) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 17:05:30] (step=0418500) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 17:06:10] (step=0418600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 17:06:50] (step=0418700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 17:07:31] (step=0418800) Train Loss: 0.0738, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-16 17:08:11] (step=0418900) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 17:08:52] (step=0419000) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 17:09:32] (step=0419100) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 17:10:12] (step=0419200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 17:10:53] (step=0419300) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 17:11:33] (step=0419400) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 17:12:14] (step=0419500) Train Loss: 0.0741, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-16 17:12:54] (step=0419600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 17:13:34] (step=0419700) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 17:14:15] (step=0419800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 17:14:55] (step=0419900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 17:15:36] (step=0420000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 17:16:00] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0420000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 17:16:03] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 17:18:02] (step=0420000) Perceptual Loss: 0.3144, Eval Time: 122.38
+[2025-05-16 17:18:43] (step=0420100) Train Loss: 0.0690, Train Steps/Sec: 0.53, Samples/Sec: 19.22
+[2025-05-16 17:19:07] Beginning epoch 104...
+[2025-05-16 17:19:24] (step=0420200) Train Loss: 0.0655, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-16 17:20:04] (step=0420300) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 17:20:44] (step=0420400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 17:21:25] (step=0420500) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 17:22:05] (step=0420600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 17:22:45] (step=0420700) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 17:23:26] (step=0420800) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 17:24:06] (step=0420900) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 17:24:47] (step=0421000) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 17:25:27] (step=0421100) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 17:26:08] (step=0421200) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 17:26:48] (step=0421300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 17:27:28] (step=0421400) Train Loss: 0.0614, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 17:28:09] (step=0421500) Train Loss: 0.0741, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 17:28:49] (step=0421600) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 17:29:30] (step=0421700) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 17:30:10] (step=0421800) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 17:30:50] (step=0421900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 17:31:31] (step=0422000) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 17:31:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 17:32:23] (step=0422100) Train Loss: 0.0654, Train Steps/Sec: 1.91, Samples/Sec: 68.59
+[2025-05-16 17:33:04] (step=0422200) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 17:33:44] (step=0422300) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 17:34:24] (step=0422400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 17:35:05] (step=0422500) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 17:35:45] (step=0422600) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 17:36:25] (step=0422700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 17:37:06] (step=0422800) Train Loss: 0.0613, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-16 17:37:46] (step=0422900) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 17:38:27] (step=0423000) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 17:39:07] (step=0423100) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 17:39:47] (step=0423200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 17:40:28] (step=0423300) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 17:41:08] (step=0423400) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 17:41:49] (step=0423500) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 17:42:29] (step=0423600) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 17:43:09] (step=0423700) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 17:43:50] (step=0423800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 17:44:30] (step=0423900) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 17:45:10] (step=0424000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 17:45:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 17:46:03] (step=0424100) Train Loss: 0.0664, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-16 17:46:43] (step=0424200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 17:46:43] Beginning epoch 105...
+[2025-05-16 17:47:24] (step=0424300) Train Loss: 0.0689, Train Steps/Sec: 2.46, Samples/Sec: 88.51
+[2025-05-16 17:48:04] (step=0424400) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 17:48:45] (step=0424500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 17:49:25] (step=0424600) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 17:50:05] (step=0424700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 17:50:46] (step=0424800) Train Loss: 0.0755, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-16 17:51:26] (step=0424900) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 17:52:07] (step=0425000) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 17:52:47] (step=0425100) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 17:53:27] (step=0425200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 17:54:08] (step=0425300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 17:54:48] (step=0425400) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 17:55:29] (step=0425500) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-16 17:56:09] (step=0425600) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 17:56:50] (step=0425700) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 17:57:30] (step=0425800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 17:58:10] (step=0425900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 17:58:51] (step=0426000) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-16 17:59:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 17:59:44] (step=0426100) Train Loss: 0.0649, Train Steps/Sec: 1.89, Samples/Sec: 68.02
+[2025-05-16 18:00:24] (step=0426200) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 18:01:04] (step=0426300) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 18:01:45] (step=0426400) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 18:02:25] (step=0426500) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 18:03:06] (step=0426600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 18:03:46] (step=0426700) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 18:04:26] (step=0426800) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-16 18:05:07] (step=0426900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 18:05:47] (step=0427000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 18:06:28] (step=0427100) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 18:07:08] (step=0427200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 18:07:48] (step=0427300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 18:08:29] (step=0427400) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 18:09:09] (step=0427500) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 18:09:49] (step=0427600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 18:10:30] (step=0427700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 18:11:10] (step=0427800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 18:11:50] (step=0427900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 18:12:31] (step=0428000) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 18:12:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 18:13:23] (step=0428100) Train Loss: 0.0726, Train Steps/Sec: 1.89, Samples/Sec: 68.19
+[2025-05-16 18:14:04] (step=0428200) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 18:14:20] Beginning epoch 106...
+[2025-05-16 18:14:45] (step=0428300) Train Loss: 0.0686, Train Steps/Sec: 2.46, Samples/Sec: 88.48
+[2025-05-16 18:15:25] (step=0428400) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 18:16:05] (step=0428500) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 18:16:46] (step=0428600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 18:17:26] (step=0428700) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 18:18:06] (step=0428800) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 18:18:47] (step=0428900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 18:19:27] (step=0429000) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 18:20:07] (step=0429100) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 18:20:48] (step=0429200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 18:21:28] (step=0429300) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 18:22:08] (step=0429400) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 18:22:49] (step=0429500) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-16 18:23:29] (step=0429600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 18:24:10] (step=0429700) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 18:24:50] (step=0429800) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 18:25:30] (step=0429900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 18:26:11] (step=0430000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 18:26:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 18:26:26] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 18:28:26] (step=0430000) Perceptual Loss: 0.2893, Eval Time: 122.29
+[2025-05-16 18:29:06] (step=0430100) Train Loss: 0.0686, Train Steps/Sec: 0.57, Samples/Sec: 20.54
+[2025-05-16 18:29:46] (step=0430200) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 18:30:27] (step=0430300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 18:31:07] (step=0430400) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 18:31:47] (step=0430500) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 18:32:28] (step=0430600) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 18:33:08] (step=0430700) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 18:33:48] (step=0430800) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 18:34:29] (step=0430900) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 18:35:09] (step=0431000) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 18:35:49] (step=0431100) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 18:36:30] (step=0431200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 18:37:10] (step=0431300) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 18:37:50] (step=0431400) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 18:38:31] (step=0431500) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 18:39:11] (step=0431600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 18:39:52] (step=0431700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 18:40:32] (step=0431800) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 18:41:12] (step=0431900) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 18:41:53] (step=0432000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 18:42:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 18:42:45] (step=0432100) Train Loss: 0.0714, Train Steps/Sec: 1.91, Samples/Sec: 68.88
+[2025-05-16 18:43:25] (step=0432200) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 18:43:57] Beginning epoch 107...
+[2025-05-16 18:44:06] (step=0432300) Train Loss: 0.0638, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-16 18:44:46] (step=0432400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 18:45:26] (step=0432500) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 18:46:07] (step=0432600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 18:46:47] (step=0432700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 18:47:27] (step=0432800) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 18:48:08] (step=0432900) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 18:48:48] (step=0433000) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 18:49:28] (step=0433100) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 18:50:09] (step=0433200) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 18:50:49] (step=0433300) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 18:51:29] (step=0433400) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-16 18:52:10] (step=0433500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 18:52:50] (step=0433600) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 18:53:30] (step=0433700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 18:54:11] (step=0433800) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 18:54:51] (step=0433900) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 18:55:31] (step=0434000) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 18:55:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 18:56:24] (step=0434100) Train Loss: 0.0677, Train Steps/Sec: 1.89, Samples/Sec: 68.10
+[2025-05-16 18:57:05] (step=0434200) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 18:57:45] (step=0434300) Train Loss: 0.0711, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 18:58:25] (step=0434400) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 18:59:06] (step=0434500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 18:59:46] (step=0434600) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 19:00:26] (step=0434700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 19:01:07] (step=0434800) Train Loss: 0.0726, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 19:01:47] (step=0434900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 19:02:28] (step=0435000) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 19:03:08] (step=0435100) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 19:03:48] (step=0435200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 19:04:29] (step=0435300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 19:05:09] (step=0435400) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 19:05:49] (step=0435500) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 19:06:30] (step=0435600) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 19:07:10] (step=0435700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 19:07:51] (step=0435800) Train Loss: 0.0780, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 19:08:31] (step=0435900) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 19:09:11] (step=0436000) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 19:09:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 19:10:04] (step=0436100) Train Loss: 0.0663, Train Steps/Sec: 1.88, Samples/Sec: 67.62
+[2025-05-16 19:10:45] (step=0436200) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 19:11:25] (step=0436300) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 19:11:33] Beginning epoch 108...
+[2025-05-16 19:12:06] (step=0436400) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-16 19:12:46] (step=0436500) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 19:13:26] (step=0436600) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 19:14:07] (step=0436700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 19:14:47] (step=0436800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 19:15:27] (step=0436900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 19:16:08] (step=0437000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 19:16:48] (step=0437100) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 19:17:28] (step=0437200) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 19:18:09] (step=0437300) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 19:18:49] (step=0437400) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 19:19:30] (step=0437500) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 19:20:10] (step=0437600) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 19:20:50] (step=0437700) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-16 19:21:31] (step=0437800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 19:22:11] (step=0437900) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 19:22:51] (step=0438000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 19:23:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 19:23:44] (step=0438100) Train Loss: 0.0702, Train Steps/Sec: 1.90, Samples/Sec: 68.30
+[2025-05-16 19:24:24] (step=0438200) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 19:25:05] (step=0438300) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 19:25:45] (step=0438400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 19:26:25] (step=0438500) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 19:27:06] (step=0438600) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 19:27:46] (step=0438700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 19:28:27] (step=0438800) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 19:29:07] (step=0438900) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 19:29:47] (step=0439000) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-16 19:30:28] (step=0439100) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-16 19:31:08] (step=0439200) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 19:31:49] (step=0439300) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 19:32:29] (step=0439400) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-16 19:33:10] (step=0439500) Train Loss: 0.0720, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-16 19:33:51] (step=0439600) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 19:34:31] (step=0439700) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-16 19:35:12] (step=0439800) Train Loss: 0.0723, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 19:35:52] (step=0439900) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 19:36:32] (step=0440000) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 19:36:56] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0440000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 19:36:59] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 19:38:59] (step=0440000) Perceptual Loss: 0.3021, Eval Time: 122.48
+[2025-05-16 19:39:40] (step=0440100) Train Loss: 0.0675, Train Steps/Sec: 0.53, Samples/Sec: 19.24
+[2025-05-16 19:40:20] (step=0440200) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-16 19:41:01] (step=0440300) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 19:41:25] Beginning epoch 109...
+[2025-05-16 19:41:41] (step=0440400) Train Loss: 0.0709, Train Steps/Sec: 2.45, Samples/Sec: 88.34
+[2025-05-16 19:42:22] (step=0440500) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-16 19:43:02] (step=0440600) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-16 19:43:43] (step=0440700) Train Loss: 0.0608, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-16 19:44:24] (step=0440800) Train Loss: 0.0748, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-16 19:45:04] (step=0440900) Train Loss: 0.0678, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-16 19:45:45] (step=0441000) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 19:46:25] (step=0441100) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 19:47:06] (step=0441200) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 19:47:46] (step=0441300) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-16 19:48:27] (step=0441400) Train Loss: 0.0707, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-16 19:49:07] (step=0441500) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 19:49:48] (step=0441600) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 19:50:28] (step=0441700) Train Loss: 0.0755, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 19:51:08] (step=0441800) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 19:51:49] (step=0441900) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 19:52:29] (step=0442000) Train Loss: 0.0734, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 19:52:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 19:53:22] (step=0442100) Train Loss: 0.0675, Train Steps/Sec: 1.91, Samples/Sec: 68.70
+[2025-05-16 19:54:02] (step=0442200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 19:54:42] (step=0442300) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 19:55:23] (step=0442400) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 19:56:03] (step=0442500) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 19:56:43] (step=0442600) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 19:57:24] (step=0442700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 19:58:04] (step=0442800) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 19:58:44] (step=0442900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 19:59:25] (step=0443000) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:00:05] (step=0443100) Train Loss: 0.0756, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:00:45] (step=0443200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 20:01:26] (step=0443300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:02:06] (step=0443400) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 20:02:46] (step=0443500) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:03:27] (step=0443600) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 20:04:07] (step=0443700) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:04:47] (step=0443800) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:05:28] (step=0443900) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:06:08] (step=0444000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:06:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 20:07:01] (step=0444100) Train Loss: 0.0708, Train Steps/Sec: 1.90, Samples/Sec: 68.25
+[2025-05-16 20:07:41] (step=0444200) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:08:22] (step=0444300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 20:09:02] (step=0444400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-16 20:09:02] Beginning epoch 110...
+[2025-05-16 20:09:42] (step=0444500) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-16 20:10:23] (step=0444600) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 20:11:03] (step=0444700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:11:44] (step=0444800) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 20:12:24] (step=0444900) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:13:04] (step=0445000) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 20:13:45] (step=0445100) Train Loss: 0.0612, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:14:25] (step=0445200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 20:15:05] (step=0445300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:15:46] (step=0445400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 20:16:26] (step=0445500) Train Loss: 0.0595, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 20:17:06] (step=0445600) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 20:17:47] (step=0445700) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:18:27] (step=0445800) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 20:19:07] (step=0445900) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 20:19:48] (step=0446000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:19:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 20:20:40] (step=0446100) Train Loss: 0.0693, Train Steps/Sec: 1.91, Samples/Sec: 68.74
+[2025-05-16 20:21:20] (step=0446200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 20:22:01] (step=0446300) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 20:22:41] (step=0446400) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:23:21] (step=0446500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:24:02] (step=0446600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:24:42] (step=0446700) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 20:25:22] (step=0446800) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 20:26:03] (step=0446900) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 20:26:43] (step=0447000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 20:27:23] (step=0447100) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:28:04] (step=0447200) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:28:44] (step=0447300) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:29:24] (step=0447400) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 20:30:05] (step=0447500) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 20:30:45] (step=0447600) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 20:31:25] (step=0447700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 20:32:06] (step=0447800) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 20:32:46] (step=0447900) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 20:33:26] (step=0448000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:33:39] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 20:34:19] (step=0448100) Train Loss: 0.0646, Train Steps/Sec: 1.90, Samples/Sec: 68.36
+[2025-05-16 20:34:59] (step=0448200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 20:35:40] (step=0448300) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 20:36:20] (step=0448400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:36:36] Beginning epoch 111...
+[2025-05-16 20:37:01] (step=0448500) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-16 20:37:41] (step=0448600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 20:38:21] (step=0448700) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:39:02] (step=0448800) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 20:39:42] (step=0448900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 20:40:22] (step=0449000) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 20:41:03] (step=0449100) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:41:43] (step=0449200) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 20:42:23] (step=0449300) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:43:04] (step=0449400) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:43:44] (step=0449500) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 20:44:24] (step=0449600) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:45:05] (step=0449700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 20:45:45] (step=0449800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 20:46:25] (step=0449900) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:47:06] (step=0450000) Train Loss: 0.0603, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 20:47:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 20:47:21] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 20:49:20] (step=0450000) Perceptual Loss: 0.3151, Eval Time: 122.58
+[2025-05-16 20:50:01] (step=0450100) Train Loss: 0.0676, Train Steps/Sec: 0.57, Samples/Sec: 20.55
+[2025-05-16 20:50:41] (step=0450200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 20:51:22] (step=0450300) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:52:02] (step=0450400) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 20:52:42] (step=0450500) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 20:53:23] (step=0450600) Train Loss: 0.0739, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 20:54:03] (step=0450700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 20:54:43] (step=0450800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 20:55:24] (step=0450900) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 20:56:04] (step=0451000) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 20:56:44] (step=0451100) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 20:57:25] (step=0451200) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 20:58:05] (step=0451300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 20:58:46] (step=0451400) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-16 20:59:26] (step=0451500) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:00:06] (step=0451600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 21:00:47] (step=0451700) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 21:01:27] (step=0451800) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:02:07] (step=0451900) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 21:02:48] (step=0452000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 21:03:00] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 21:03:40] (step=0452100) Train Loss: 0.0653, Train Steps/Sec: 1.91, Samples/Sec: 68.72
+[2025-05-16 21:04:20] (step=0452200) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:05:01] (step=0452300) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 21:05:41] (step=0452400) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:06:13] Beginning epoch 112...
+[2025-05-16 21:06:22] (step=0452500) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-16 21:07:02] (step=0452600) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 21:07:42] (step=0452700) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 21:08:23] (step=0452800) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-16 21:09:03] (step=0452900) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-16 21:09:44] (step=0453000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 21:10:24] (step=0453100) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:11:04] (step=0453200) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:11:45] (step=0453300) Train Loss: 0.0581, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:12:25] (step=0453400) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 21:13:05] (step=0453500) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 21:13:46] (step=0453600) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 21:14:26] (step=0453700) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:15:06] (step=0453800) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:15:47] (step=0453900) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:16:27] (step=0454000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 21:16:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 21:17:21] (step=0454100) Train Loss: 0.0648, Train Steps/Sec: 1.87, Samples/Sec: 67.19
+[2025-05-16 21:18:01] (step=0454200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:18:41] (step=0454300) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 21:19:22] (step=0454400) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:20:02] (step=0454500) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:20:42] (step=0454600) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 21:21:23] (step=0454700) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:22:03] (step=0454800) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 21:22:43] (step=0454900) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 21:23:24] (step=0455000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 21:24:04] (step=0455100) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 21:24:44] (step=0455200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 21:25:25] (step=0455300) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 21:26:05] (step=0455400) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:26:46] (step=0455500) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 21:27:26] (step=0455600) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 21:28:06] (step=0455700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 21:28:47] (step=0455800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 21:29:27] (step=0455900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 21:30:07] (step=0456000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:30:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 21:30:59] (step=0456100) Train Loss: 0.0650, Train Steps/Sec: 1.92, Samples/Sec: 69.02
+[2025-05-16 21:31:40] (step=0456200) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 21:32:20] (step=0456300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 21:33:00] (step=0456400) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:33:41] (step=0456500) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 21:33:49] Beginning epoch 113...
+[2025-05-16 21:34:21] (step=0456600) Train Loss: 0.0629, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-16 21:35:02] (step=0456700) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:35:42] (step=0456800) Train Loss: 0.0731, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 21:36:22] (step=0456900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 21:37:03] (step=0457000) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 21:37:43] (step=0457100) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:38:23] (step=0457200) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 21:39:04] (step=0457300) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:39:44] (step=0457400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:40:25] (step=0457500) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-16 21:41:05] (step=0457600) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:41:45] (step=0457700) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 21:42:26] (step=0457800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:43:06] (step=0457900) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:43:46] (step=0458000) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:43:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 21:44:39] (step=0458100) Train Loss: 0.0677, Train Steps/Sec: 1.90, Samples/Sec: 68.33
+[2025-05-16 21:45:19] (step=0458200) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 21:46:00] (step=0458300) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-16 21:46:40] (step=0458400) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 21:47:20] (step=0458500) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 21:48:01] (step=0458600) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:48:41] (step=0458700) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:49:21] (step=0458800) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-16 21:50:02] (step=0458900) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:50:42] (step=0459000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 21:51:22] (step=0459100) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:52:03] (step=0459200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 21:52:43] (step=0459300) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 21:53:23] (step=0459400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 21:54:04] (step=0459500) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 21:54:44] (step=0459600) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 21:55:24] (step=0459700) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 21:56:05] (step=0459800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 21:56:45] (step=0459900) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 21:57:25] (step=0460000) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 21:57:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0460000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 21:57:53] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 21:59:53] (step=0460000) Perceptual Loss: 0.3119, Eval Time: 122.97
+[2025-05-16 22:00:33] (step=0460100) Train Loss: 0.0684, Train Steps/Sec: 0.53, Samples/Sec: 19.16
+[2025-05-16 22:01:14] (step=0460200) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 22:01:54] (step=0460300) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:02:34] (step=0460400) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-16 22:03:15] (step=0460500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:03:39] Beginning epoch 114...
+[2025-05-16 22:03:55] (step=0460600) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-16 22:04:36] (step=0460700) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 22:05:16] (step=0460800) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 22:05:56] (step=0460900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 22:06:37] (step=0461000) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 22:07:17] (step=0461100) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 22:07:57] (step=0461200) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 22:08:38] (step=0461300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 22:09:18] (step=0461400) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 22:09:59] (step=0461500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:10:39] (step=0461600) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 22:11:19] (step=0461700) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 22:12:00] (step=0461800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 22:12:40] (step=0461900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 22:13:20] (step=0462000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 22:13:33] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 22:14:13] (step=0462100) Train Loss: 0.0660, Train Steps/Sec: 1.90, Samples/Sec: 68.25
+[2025-05-16 22:14:53] (step=0462200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:15:34] (step=0462300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 22:16:14] (step=0462400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:16:54] (step=0462500) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 22:17:35] (step=0462600) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:18:15] (step=0462700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 22:18:56] (step=0462800) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 22:19:36] (step=0462900) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 22:20:16] (step=0463000) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:20:57] (step=0463100) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:21:37] (step=0463200) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:22:17] (step=0463300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 22:22:58] (step=0463400) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 22:23:38] (step=0463500) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 22:24:18] (step=0463600) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-16 22:24:59] (step=0463700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 22:25:39] (step=0463800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 22:26:19] (step=0463900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 22:27:00] (step=0464000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 22:27:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 22:27:52] (step=0464100) Train Loss: 0.0633, Train Steps/Sec: 1.91, Samples/Sec: 68.60
+[2025-05-16 22:28:33] (step=0464200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:29:13] (step=0464300) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 22:29:53] (step=0464400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 22:30:34] (step=0464500) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:31:14] (step=0464600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-16 22:31:14] Beginning epoch 115...
+[2025-05-16 22:31:54] (step=0464700) Train Loss: 0.0624, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-16 22:32:35] (step=0464800) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-16 22:33:15] (step=0464900) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 22:33:56] (step=0465000) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-16 22:34:36] (step=0465100) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:35:16] (step=0465200) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:35:57] (step=0465300) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 22:36:37] (step=0465400) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:37:17] (step=0465500) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-16 22:37:58] (step=0465600) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 22:38:38] (step=0465700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:39:18] (step=0465800) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:39:59] (step=0465900) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:40:39] (step=0466000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:40:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 22:41:32] (step=0466100) Train Loss: 0.0626, Train Steps/Sec: 1.89, Samples/Sec: 67.95
+[2025-05-16 22:42:12] (step=0466200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 22:42:53] (step=0466300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 22:43:33] (step=0466400) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:44:13] (step=0466500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:44:54] (step=0466600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 22:45:34] (step=0466700) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 22:46:15] (step=0466800) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-16 22:46:55] (step=0466900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 22:47:35] (step=0467000) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-16 22:48:16] (step=0467100) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 22:48:56] (step=0467200) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 22:49:36] (step=0467300) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-16 22:50:17] (step=0467400) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 22:50:57] (step=0467500) Train Loss: 0.0615, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 22:51:37] (step=0467600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 22:52:18] (step=0467700) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-16 22:52:58] (step=0467800) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 22:53:38] (step=0467900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 22:54:19] (step=0468000) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 22:54:31] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 22:55:12] (step=0468100) Train Loss: 0.0655, Train Steps/Sec: 1.89, Samples/Sec: 67.93
+[2025-05-16 22:55:52] (step=0468200) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-16 22:56:33] (step=0468300) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-16 22:57:13] (step=0468400) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 22:57:53] (step=0468500) Train Loss: 0.0597, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 22:58:34] (step=0468600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 22:58:50] Beginning epoch 116...
+[2025-05-16 22:59:14] (step=0468700) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-16 22:59:55] (step=0468800) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-16 23:00:35] (step=0468900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 23:01:15] (step=0469000) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-16 23:01:56] (step=0469100) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-16 23:02:36] (step=0469200) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 23:03:16] (step=0469300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 23:03:57] (step=0469400) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 23:04:37] (step=0469500) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-16 23:05:18] (step=0469600) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 23:05:58] (step=0469700) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-16 23:06:38] (step=0469800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-16 23:07:19] (step=0469900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 23:07:59] (step=0470000) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 23:08:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-16 23:08:14] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-16 23:10:14] (step=0470000) Perceptual Loss: 0.3030, Eval Time: 122.59
+[2025-05-16 23:10:54] (step=0470100) Train Loss: 0.0634, Train Steps/Sec: 0.57, Samples/Sec: 20.58
+[2025-05-16 23:11:34] (step=0470200) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 23:12:15] (step=0470300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 23:12:55] (step=0470400) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 23:13:35] (step=0470500) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 23:14:16] (step=0470600) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-16 23:14:56] (step=0470700) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-16 23:15:36] (step=0470800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-16 23:16:17] (step=0470900) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-16 23:16:57] (step=0471000) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-16 23:17:37] (step=0471100) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 23:18:18] (step=0471200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-16 23:18:58] (step=0471300) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-16 23:19:38] (step=0471400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 23:20:19] (step=0471500) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-16 23:20:59] (step=0471600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 23:21:40] (step=0471700) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 23:22:20] (step=0471800) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 23:23:00] (step=0471900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-16 23:23:40] (step=0472000) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-16 23:23:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 23:24:33] (step=0472100) Train Loss: 0.0639, Train Steps/Sec: 1.91, Samples/Sec: 68.86
+[2025-05-16 23:25:13] (step=0472200) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 23:25:53] (step=0472300) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 23:26:34] (step=0472400) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-16 23:27:14] (step=0472500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 23:27:54] (step=0472600) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-16 23:28:26] Beginning epoch 117...
+[2025-05-16 23:28:35] (step=0472700) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-16 23:29:15] (step=0472800) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-16 23:29:56] (step=0472900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 23:30:36] (step=0473000) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-16 23:31:16] (step=0473100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 23:31:56] (step=0473200) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 23:32:37] (step=0473300) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 23:33:17] (step=0473400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 23:33:57] (step=0473500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-16 23:34:38] (step=0473600) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-16 23:35:18] (step=0473700) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-16 23:35:58] (step=0473800) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-16 23:36:39] (step=0473900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 23:37:19] (step=0474000) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 23:37:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 23:38:12] (step=0474100) Train Loss: 0.0703, Train Steps/Sec: 1.89, Samples/Sec: 67.90
+[2025-05-16 23:38:52] (step=0474200) Train Loss: 0.0600, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-16 23:39:33] (step=0474300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 23:40:13] (step=0474400) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-16 23:40:53] (step=0474500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-16 23:41:33] (step=0474600) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-16 23:42:14] (step=0474700) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 23:42:54] (step=0474800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-16 23:43:34] (step=0474900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-16 23:44:15] (step=0475000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 23:44:55] (step=0475100) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 23:45:35] (step=0475200) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-16 23:46:16] (step=0475300) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 23:46:56] (step=0475400) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-16 23:47:36] (step=0475500) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 23:48:17] (step=0475600) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-16 23:48:57] (step=0475700) Train Loss: 0.0742, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-16 23:49:37] (step=0475800) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-16 23:50:18] (step=0475900) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-16 23:50:58] (step=0476000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-16 23:51:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-16 23:51:50] (step=0476100) Train Loss: 0.0634, Train Steps/Sec: 1.91, Samples/Sec: 68.85
+[2025-05-16 23:52:30] (step=0476200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-16 23:53:11] (step=0476300) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-16 23:53:51] (step=0476400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 23:54:31] (step=0476500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-16 23:55:12] (step=0476600) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 23:55:52] (step=0476700) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-16 23:56:00] Beginning epoch 118...
+[2025-05-16 23:56:33] (step=0476800) Train Loss: 0.0620, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-16 23:57:13] (step=0476900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.40
+[2025-05-16 23:57:53] (step=0477000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-16 23:58:33] (step=0477100) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-16 23:59:14] (step=0477200) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-16 23:59:54] (step=0477300) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 00:00:34] (step=0477400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 00:01:15] (step=0477500) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 00:01:55] (step=0477600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:02:35] (step=0477700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 00:03:16] (step=0477800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 00:03:56] (step=0477900) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.40
+[2025-05-17 00:04:36] (step=0478000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.40
+[2025-05-17 00:04:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 00:05:28] (step=0478100) Train Loss: 0.0637, Train Steps/Sec: 1.92, Samples/Sec: 69.04
+[2025-05-17 00:06:09] (step=0478200) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 00:06:49] (step=0478300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-17 00:07:29] (step=0478400) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 00:08:10] (step=0478500) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:08:50] (step=0478600) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:09:30] (step=0478700) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:10:11] (step=0478800) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-17 00:10:51] (step=0478900) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:11:31] (step=0479000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 00:12:12] (step=0479100) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:12:52] (step=0479200) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:13:32] (step=0479300) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 00:14:12] (step=0479400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 00:14:53] (step=0479500) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-17 00:15:33] (step=0479600) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 00:16:14] (step=0479700) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-17 00:16:54] (step=0479800) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 00:17:34] (step=0479900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 00:18:15] (step=0480000) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 00:18:39] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0480000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 00:18:42] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 00:20:42] (step=0480000) Perceptual Loss: 0.3311, Eval Time: 122.68
+[2025-05-17 00:21:22] (step=0480100) Train Loss: 0.0641, Train Steps/Sec: 0.53, Samples/Sec: 19.21
+[2025-05-17 00:22:02] (step=0480200) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 00:22:43] (step=0480300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 00:23:23] (step=0480400) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 00:24:04] (step=0480500) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 00:24:44] (step=0480600) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-17 00:25:25] (step=0480700) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-17 00:25:49] Beginning epoch 119...
+[2025-05-17 00:26:06] (step=0480800) Train Loss: 0.0644, Train Steps/Sec: 2.44, Samples/Sec: 87.94
+[2025-05-17 00:26:46] (step=0480900) Train Loss: 0.0730, Train Steps/Sec: 2.46, Samples/Sec: 88.58
+[2025-05-17 00:27:27] (step=0481000) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-17 00:28:07] (step=0481100) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-17 00:28:48] (step=0481200) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 00:29:28] (step=0481300) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-17 00:30:09] (step=0481400) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-17 00:30:49] (step=0481500) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-17 00:31:30] (step=0481600) Train Loss: 0.0623, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-17 00:32:10] (step=0481700) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-17 00:32:51] (step=0481800) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 00:33:31] (step=0481900) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-17 00:34:11] (step=0482000) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 00:34:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 00:35:04] (step=0482100) Train Loss: 0.0717, Train Steps/Sec: 1.92, Samples/Sec: 68.94
+[2025-05-17 00:35:44] (step=0482200) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 00:36:24] (step=0482300) Train Loss: 0.0610, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 00:37:04] (step=0482400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:37:45] (step=0482500) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:38:25] (step=0482600) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:39:05] (step=0482700) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:39:46] (step=0482800) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-17 00:40:26] (step=0482900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 00:41:06] (step=0483000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 00:41:47] (step=0483100) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 00:42:27] (step=0483200) Train Loss: 0.0612, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 00:43:07] (step=0483300) Train Loss: 0.0739, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:43:48] (step=0483400) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 00:44:28] (step=0483500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 00:45:08] (step=0483600) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 00:45:49] (step=0483700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:46:29] (step=0483800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:47:09] (step=0483900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 00:47:50] (step=0484000) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:48:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 00:48:42] (step=0484100) Train Loss: 0.0706, Train Steps/Sec: 1.90, Samples/Sec: 68.23
+[2025-05-17 00:49:23] (step=0484200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:50:03] (step=0484300) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 00:50:43] (step=0484400) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:51:24] (step=0484500) Train Loss: 0.0738, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 00:52:04] (step=0484600) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:52:44] (step=0484700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 00:53:25] (step=0484800) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 00:53:25] Beginning epoch 120...
+[2025-05-17 00:54:05] (step=0484900) Train Loss: 0.0644, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-17 00:54:45] (step=0485000) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 00:55:26] (step=0485100) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 00:56:06] (step=0485200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 00:56:46] (step=0485300) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 00:57:27] (step=0485400) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 00:58:07] (step=0485500) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-17 00:58:48] (step=0485600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 00:59:28] (step=0485700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 01:00:08] (step=0485800) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 01:00:49] (step=0485900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:01:29] (step=0486000) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 01:01:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 01:02:21] (step=0486100) Train Loss: 0.0662, Train Steps/Sec: 1.91, Samples/Sec: 68.81
+[2025-05-17 01:03:01] (step=0486200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:03:42] (step=0486300) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 01:04:22] (step=0486400) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 01:05:02] (step=0486500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 01:05:43] (step=0486600) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 01:06:23] (step=0486700) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:07:03] (step=0486800) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 01:07:44] (step=0486900) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:08:24] (step=0487000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 01:09:04] (step=0487100) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:09:45] (step=0487200) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 01:10:25] (step=0487300) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:11:05] (step=0487400) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:11:46] (step=0487500) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 01:12:26] (step=0487600) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 01:13:07] (step=0487700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 01:13:47] (step=0487800) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 01:14:27] (step=0487900) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 01:15:07] (step=0488000) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 01:15:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 01:16:00] (step=0488100) Train Loss: 0.0687, Train Steps/Sec: 1.91, Samples/Sec: 68.89
+[2025-05-17 01:16:40] (step=0488200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 01:17:20] (step=0488300) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 01:18:01] (step=0488400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 01:18:41] (step=0488500) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 01:19:21] (step=0488600) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 01:20:02] (step=0488700) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 01:20:42] (step=0488800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 01:20:58] Beginning epoch 121...
+[2025-05-17 01:21:23] (step=0488900) Train Loss: 0.0723, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-17 01:22:03] (step=0489000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 01:22:43] (step=0489100) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 01:23:24] (step=0489200) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 01:24:04] (step=0489300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 01:24:44] (step=0489400) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 01:25:25] (step=0489500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 01:26:05] (step=0489600) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:26:46] (step=0489700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 01:27:26] (step=0489800) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 01:28:06] (step=0489900) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 01:28:46] (step=0490000) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 01:28:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 01:29:01] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 01:31:01] (step=0490000) Perceptual Loss: 0.3015, Eval Time: 122.33
+[2025-05-17 01:31:41] (step=0490100) Train Loss: 0.0680, Train Steps/Sec: 0.57, Samples/Sec: 20.59
+[2025-05-17 01:32:22] (step=0490200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 01:33:02] (step=0490300) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 01:33:42] (step=0490400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 01:34:23] (step=0490500) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 01:35:03] (step=0490600) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 01:35:43] (step=0490700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 01:36:24] (step=0490800) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-17 01:37:04] (step=0490900) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 01:37:44] (step=0491000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 01:38:25] (step=0491100) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 01:39:05] (step=0491200) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 01:39:45] (step=0491300) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 01:40:26] (step=0491400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 01:41:06] (step=0491500) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 01:41:47] (step=0491600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 01:42:27] (step=0491700) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 01:43:07] (step=0491800) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 01:43:48] (step=0491900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:44:28] (step=0492000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:44:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 01:45:20] (step=0492100) Train Loss: 0.0676, Train Steps/Sec: 1.91, Samples/Sec: 68.83
+[2025-05-17 01:46:01] (step=0492200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:46:41] (step=0492300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 01:47:21] (step=0492400) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 01:48:02] (step=0492500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 01:48:42] (step=0492600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:49:22] (step=0492700) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 01:50:03] (step=0492800) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 01:50:35] Beginning epoch 122...
+[2025-05-17 01:50:43] (step=0492900) Train Loss: 0.0706, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-17 01:51:24] (step=0493000) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 01:52:04] (step=0493100) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:52:44] (step=0493200) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 01:53:24] (step=0493300) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:54:05] (step=0493400) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 01:54:45] (step=0493500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 01:55:26] (step=0493600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 01:56:06] (step=0493700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 01:56:46] (step=0493800) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 01:57:26] (step=0493900) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 01:58:07] (step=0494000) Train Loss: 0.0608, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 01:58:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 01:58:59] (step=0494100) Train Loss: 0.0670, Train Steps/Sec: 1.91, Samples/Sec: 68.76
+[2025-05-17 01:59:39] (step=0494200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 02:00:20] (step=0494300) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 02:01:00] (step=0494400) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:01:40] (step=0494500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 02:02:21] (step=0494600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:03:01] (step=0494700) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:03:41] (step=0494800) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 02:04:22] (step=0494900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:05:02] (step=0495000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 02:05:42] (step=0495100) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:06:23] (step=0495200) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:07:03] (step=0495300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 02:07:43] (step=0495400) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:08:24] (step=0495500) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 02:09:04] (step=0495600) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 02:09:44] (step=0495700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:10:25] (step=0495800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 02:11:05] (step=0495900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:11:45] (step=0496000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 02:11:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 02:12:38] (step=0496100) Train Loss: 0.0681, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-17 02:13:18] (step=0496200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:13:59] (step=0496300) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 02:14:39] (step=0496400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:15:19] (step=0496500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:15:59] (step=0496600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:16:40] (step=0496700) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:17:20] (step=0496800) Train Loss: 0.0711, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-17 02:18:01] (step=0496900) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:18:09] Beginning epoch 123...
+[2025-05-17 02:18:41] (step=0497000) Train Loss: 0.0684, Train Steps/Sec: 2.46, Samples/Sec: 88.58
+[2025-05-17 02:19:21] (step=0497100) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 02:20:02] (step=0497200) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 02:20:42] (step=0497300) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 02:21:23] (step=0497400) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 02:22:03] (step=0497500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 02:22:43] (step=0497600) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:23:24] (step=0497700) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 02:24:04] (step=0497800) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 02:24:44] (step=0497900) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:25:25] (step=0498000) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:25:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 02:26:17] (step=0498100) Train Loss: 0.0608, Train Steps/Sec: 1.91, Samples/Sec: 68.79
+[2025-05-17 02:26:57] (step=0498200) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 02:27:38] (step=0498300) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 02:28:18] (step=0498400) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:28:58] (step=0498500) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 02:29:39] (step=0498600) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:30:19] (step=0498700) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:30:59] (step=0498800) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 02:31:40] (step=0498900) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:32:20] (step=0499000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 02:33:00] (step=0499100) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:33:41] (step=0499200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:34:21] (step=0499300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 02:35:01] (step=0499400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 02:35:42] (step=0499500) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 02:36:22] (step=0499600) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:37:02] (step=0499700) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 02:37:43] (step=0499800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:38:23] (step=0499900) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:39:03] (step=0500000) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 02:39:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0500000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 02:39:29] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 02:41:29] (step=0500000) Perceptual Loss: 0.3119, Eval Time: 122.47
+[2025-05-17 02:42:09] (step=0500100) Train Loss: 0.0710, Train Steps/Sec: 0.54, Samples/Sec: 19.33
+[2025-05-17 02:42:50] (step=0500200) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 02:43:30] (step=0500300) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:44:10] (step=0500400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 02:44:51] (step=0500500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 02:45:31] (step=0500600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:46:12] (step=0500700) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-17 02:46:52] (step=0500800) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:47:32] (step=0500900) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 02:47:56] Beginning epoch 124...
+[2025-05-17 02:48:13] (step=0501000) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-17 02:48:53] (step=0501100) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:49:33] (step=0501200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 02:50:14] (step=0501300) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 02:50:54] (step=0501400) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 02:51:35] (step=0501500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:52:15] (step=0501600) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 02:52:55] (step=0501700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-17 02:53:36] (step=0501800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 02:54:16] (step=0501900) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 02:54:56] (step=0502000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 02:55:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 02:55:49] (step=0502100) Train Loss: 0.0725, Train Steps/Sec: 1.90, Samples/Sec: 68.30
+[2025-05-17 02:56:29] (step=0502200) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:57:10] (step=0502300) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 02:57:50] (step=0502400) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 02:58:30] (step=0502500) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:59:11] (step=0502600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 02:59:51] (step=0502700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 03:00:31] (step=0502800) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-17 03:01:12] (step=0502900) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 03:01:52] (step=0503000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 03:02:32] (step=0503100) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 03:03:13] (step=0503200) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 03:03:53] (step=0503300) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-17 03:04:33] (step=0503400) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-17 03:05:14] (step=0503500) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-17 03:05:54] (step=0503600) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 03:06:35] (step=0503700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-17 03:07:15] (step=0503800) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 03:07:55] (step=0503900) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 03:08:36] (step=0504000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 03:08:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 03:09:28] (step=0504100) Train Loss: 0.0654, Train Steps/Sec: 1.89, Samples/Sec: 68.06
+[2025-05-17 03:10:09] (step=0504200) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 03:10:49] (step=0504300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 03:11:29] (step=0504400) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 03:12:10] (step=0504500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 03:12:50] (step=0504600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:13:30] (step=0504700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:14:11] (step=0504800) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 03:14:51] (step=0504900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 03:15:31] (step=0505000) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 03:15:31] Beginning epoch 125...
+[2025-05-17 03:16:12] (step=0505100) Train Loss: 0.0685, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-17 03:16:52] (step=0505200) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 03:17:33] (step=0505300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 03:18:13] (step=0505400) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 03:18:53] (step=0505500) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 03:19:34] (step=0505600) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 03:20:14] (step=0505700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 03:20:54] (step=0505800) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 03:21:35] (step=0505900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 03:22:15] (step=0506000) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 03:22:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 03:23:07] (step=0506100) Train Loss: 0.0633, Train Steps/Sec: 1.91, Samples/Sec: 68.70
+[2025-05-17 03:23:48] (step=0506200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 03:24:28] (step=0506300) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 03:25:08] (step=0506400) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 03:25:49] (step=0506500) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 03:26:29] (step=0506600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 03:27:09] (step=0506700) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 03:27:50] (step=0506800) Train Loss: 0.0717, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 03:28:30] (step=0506900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 03:29:11] (step=0507000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 03:29:51] (step=0507100) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:30:31] (step=0507200) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 03:31:11] (step=0507300) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 03:31:52] (step=0507400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 03:32:32] (step=0507500) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 03:33:13] (step=0507600) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 03:33:53] (step=0507700) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 03:34:33] (step=0507800) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 03:35:14] (step=0507900) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 03:35:54] (step=0508000) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 03:36:06] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 03:36:46] (step=0508100) Train Loss: 0.0692, Train Steps/Sec: 1.91, Samples/Sec: 68.88
+[2025-05-17 03:37:26] (step=0508200) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 03:38:07] (step=0508300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 03:38:47] (step=0508400) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:39:27] (step=0508500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 03:40:08] (step=0508600) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:40:48] (step=0508700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 03:41:29] (step=0508800) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 03:42:09] (step=0508900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:42:49] (step=0509000) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 03:43:05] Beginning epoch 126...
+[2025-05-17 03:43:30] (step=0509100) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-17 03:44:10] (step=0509200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 03:44:50] (step=0509300) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:45:31] (step=0509400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:46:11] (step=0509500) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 03:46:51] (step=0509600) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 03:47:32] (step=0509700) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 03:48:12] (step=0509800) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 03:48:52] (step=0509900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 03:49:33] (step=0510000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 03:49:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 03:49:47] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 03:51:47] (step=0510000) Perceptual Loss: 0.3152, Eval Time: 122.20
+[2025-05-17 03:52:27] (step=0510100) Train Loss: 0.0672, Train Steps/Sec: 0.57, Samples/Sec: 20.60
+[2025-05-17 03:53:08] (step=0510200) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:53:48] (step=0510300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 03:54:28] (step=0510400) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:55:09] (step=0510500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 03:55:49] (step=0510600) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 03:56:29] (step=0510700) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 03:57:10] (step=0510800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 03:57:50] (step=0510900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 03:58:30] (step=0511000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 03:59:11] (step=0511100) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 03:59:51] (step=0511200) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:00:31] (step=0511300) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:01:12] (step=0511400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 04:01:52] (step=0511500) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 04:02:32] (step=0511600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 04:03:13] (step=0511700) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 04:03:53] (step=0511800) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:04:33] (step=0511900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 04:05:14] (step=0512000) Train Loss: 0.0597, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:05:26] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 04:06:06] (step=0512100) Train Loss: 0.0642, Train Steps/Sec: 1.91, Samples/Sec: 68.67
+[2025-05-17 04:06:46] (step=0512200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 04:07:27] (step=0512300) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 04:08:07] (step=0512400) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 04:08:47] (step=0512500) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 04:09:28] (step=0512600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:10:08] (step=0512700) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:10:48] (step=0512800) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 04:11:29] (step=0512900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 04:12:09] (step=0513000) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 04:12:41] Beginning epoch 127...
+[2025-05-17 04:12:50] (step=0513100) Train Loss: 0.0686, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-17 04:13:30] (step=0513200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 04:14:10] (step=0513300) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:14:51] (step=0513400) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 04:15:31] (step=0513500) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 04:16:11] (step=0513600) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 04:16:52] (step=0513700) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:17:32] (step=0513800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 04:18:12] (step=0513900) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 04:18:52] (step=0514000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 04:19:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 04:19:45] (step=0514100) Train Loss: 0.0718, Train Steps/Sec: 1.91, Samples/Sec: 68.70
+[2025-05-17 04:20:25] (step=0514200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 04:21:06] (step=0514300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 04:21:46] (step=0514400) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:22:26] (step=0514500) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 04:23:06] (step=0514600) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:23:47] (step=0514700) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:24:27] (step=0514800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 04:25:07] (step=0514900) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:25:48] (step=0515000) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 04:26:28] (step=0515100) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 04:27:08] (step=0515200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:27:49] (step=0515300) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 04:28:29] (step=0515400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:29:09] (step=0515500) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 04:29:50] (step=0515600) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:30:30] (step=0515700) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 04:31:10] (step=0515800) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 04:31:51] (step=0515900) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 04:32:31] (step=0516000) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:32:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 04:33:24] (step=0516100) Train Loss: 0.0634, Train Steps/Sec: 1.89, Samples/Sec: 68.21
+[2025-05-17 04:34:04] (step=0516200) Train Loss: 0.0741, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 04:34:44] (step=0516300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 04:35:25] (step=0516400) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 04:36:05] (step=0516500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 04:36:45] (step=0516600) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 04:37:26] (step=0516700) Train Loss: 0.0608, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 04:38:06] (step=0516800) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-17 04:38:47] (step=0516900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 04:39:27] (step=0517000) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 04:40:07] (step=0517100) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 04:40:15] Beginning epoch 128...
+[2025-05-17 04:40:48] (step=0517200) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-17 04:41:28] (step=0517300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 04:42:08] (step=0517400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 04:42:49] (step=0517500) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 04:43:29] (step=0517600) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 04:44:10] (step=0517700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 04:44:50] (step=0517800) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:45:30] (step=0517900) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 04:46:10] (step=0518000) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 04:46:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 04:47:03] (step=0518100) Train Loss: 0.0707, Train Steps/Sec: 1.91, Samples/Sec: 68.89
+[2025-05-17 04:47:43] (step=0518200) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 04:48:23] (step=0518300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 04:49:04] (step=0518400) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 04:49:44] (step=0518500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:50:24] (step=0518600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 04:51:05] (step=0518700) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 04:51:45] (step=0518800) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 04:52:25] (step=0518900) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 04:53:06] (step=0519000) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 04:53:46] (step=0519100) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:54:26] (step=0519200) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 04:55:07] (step=0519300) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 04:55:47] (step=0519400) Train Loss: 0.0740, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 04:56:27] (step=0519500) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-17 04:57:08] (step=0519600) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 04:57:48] (step=0519700) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 04:58:28] (step=0519800) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 04:59:09] (step=0519900) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 04:59:49] (step=0520000) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 05:00:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0520000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 05:00:15] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 05:02:15] (step=0520000) Perceptual Loss: 0.3147, Eval Time: 122.41
+[2025-05-17 05:02:55] (step=0520100) Train Loss: 0.0689, Train Steps/Sec: 0.54, Samples/Sec: 19.32
+[2025-05-17 05:03:36] (step=0520200) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 05:04:16] (step=0520300) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 05:04:56] (step=0520400) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:05:36] (step=0520500) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 05:06:17] (step=0520600) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:06:57] (step=0520700) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 05:07:38] (step=0520800) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 05:08:18] (step=0520900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 05:08:58] (step=0521000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 05:09:39] (step=0521100) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:10:03] Beginning epoch 129...
+[2025-05-17 05:10:19] (step=0521200) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-17 05:10:59] (step=0521300) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 05:11:40] (step=0521400) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 05:12:20] (step=0521500) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 05:13:01] (step=0521600) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 05:13:41] (step=0521700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 05:14:21] (step=0521800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 05:15:02] (step=0521900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 05:15:42] (step=0522000) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:15:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 05:16:35] (step=0522100) Train Loss: 0.0653, Train Steps/Sec: 1.90, Samples/Sec: 68.33
+[2025-05-17 05:17:15] (step=0522200) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:17:55] (step=0522300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 05:18:36] (step=0522400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:19:16] (step=0522500) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:19:56] (step=0522600) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 05:20:37] (step=0522700) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 05:21:17] (step=0522800) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-17 05:21:57] (step=0522900) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 05:22:38] (step=0523000) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-17 05:23:18] (step=0523100) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-17 05:23:58] (step=0523200) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-17 05:24:39] (step=0523300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 05:25:19] (step=0523400) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-17 05:26:00] (step=0523500) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 05:26:40] (step=0523600) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 05:27:20] (step=0523700) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 05:28:01] (step=0523800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:28:41] (step=0523900) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:29:21] (step=0524000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 05:29:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 05:30:14] (step=0524100) Train Loss: 0.0683, Train Steps/Sec: 1.90, Samples/Sec: 68.44
+[2025-05-17 05:30:54] (step=0524200) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:31:35] (step=0524300) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 05:32:15] (step=0524400) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 05:32:55] (step=0524500) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:33:36] (step=0524600) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 05:34:16] (step=0524700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:34:57] (step=0524800) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-17 05:35:37] (step=0524900) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:36:17] (step=0525000) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-17 05:36:58] (step=0525100) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:37:38] (step=0525200) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 05:37:38] Beginning epoch 130...
+[2025-05-17 05:38:19] (step=0525300) Train Loss: 0.0638, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-17 05:38:59] (step=0525400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 05:39:39] (step=0525500) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 05:40:20] (step=0525600) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 05:41:00] (step=0525700) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 05:41:40] (step=0525800) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:42:21] (step=0525900) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 05:43:01] (step=0526000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 05:43:14] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 05:43:54] (step=0526100) Train Loss: 0.0650, Train Steps/Sec: 1.87, Samples/Sec: 67.48
+[2025-05-17 05:44:35] (step=0526200) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:45:15] (step=0526300) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 05:45:55] (step=0526400) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:46:36] (step=0526500) Train Loss: 0.0597, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:47:16] (step=0526600) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 05:47:56] (step=0526700) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 05:48:37] (step=0526800) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-17 05:49:17] (step=0526900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:49:57] (step=0527000) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 05:50:38] (step=0527100) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 05:51:18] (step=0527200) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 05:51:58] (step=0527300) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 05:52:39] (step=0527400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:53:19] (step=0527500) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 05:53:59] (step=0527600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:54:40] (step=0527700) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 05:55:20] (step=0527800) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 05:56:00] (step=0527900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 05:56:41] (step=0528000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 05:56:53] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 05:57:33] (step=0528100) Train Loss: 0.0655, Train Steps/Sec: 1.91, Samples/Sec: 68.64
+[2025-05-17 05:58:14] (step=0528200) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 05:58:54] (step=0528300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 05:59:34] (step=0528400) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 06:00:15] (step=0528500) Train Loss: 0.0746, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 06:00:55] (step=0528600) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 06:01:35] (step=0528700) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 06:02:16] (step=0528800) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 06:02:56] (step=0528900) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 06:03:36] (step=0529000) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 06:04:17] (step=0529100) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 06:04:57] (step=0529200) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 06:05:13] Beginning epoch 131...
+[2025-05-17 06:05:37] (step=0529300) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-17 06:06:18] (step=0529400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 06:06:58] (step=0529500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 06:07:38] (step=0529600) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 06:08:19] (step=0529700) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 06:08:59] (step=0529800) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 06:09:39] (step=0529900) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 06:10:20] (step=0530000) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 06:10:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 06:10:35] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 06:12:34] (step=0530000) Perceptual Loss: 0.3228, Eval Time: 122.17
+[2025-05-17 06:13:15] (step=0530100) Train Loss: 0.0655, Train Steps/Sec: 0.57, Samples/Sec: 20.57
+[2025-05-17 06:13:55] (step=0530200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 06:14:35] (step=0530300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 06:15:16] (step=0530400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 06:15:56] (step=0530500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 06:16:36] (step=0530600) Train Loss: 0.0612, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 06:17:17] (step=0530700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 06:17:57] (step=0530800) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 06:18:37] (step=0530900) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 06:19:18] (step=0531000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:19:58] (step=0531100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 06:20:38] (step=0531200) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:21:19] (step=0531300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 06:21:59] (step=0531400) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 06:22:40] (step=0531500) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 06:23:20] (step=0531600) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 06:24:00] (step=0531700) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 06:24:41] (step=0531800) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 06:25:21] (step=0531900) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 06:26:01] (step=0532000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 06:26:13] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 06:26:54] (step=0532100) Train Loss: 0.0668, Train Steps/Sec: 1.90, Samples/Sec: 68.27
+[2025-05-17 06:27:34] (step=0532200) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 06:28:15] (step=0532300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 06:28:55] (step=0532400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:29:35] (step=0532500) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 06:30:16] (step=0532600) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:30:56] (step=0532700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 06:31:36] (step=0532800) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-17 06:32:17] (step=0532900) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:32:57] (step=0533000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 06:33:37] (step=0533100) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 06:34:18] (step=0533200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 06:34:50] Beginning epoch 132...
+[2025-05-17 06:34:58] (step=0533300) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-17 06:35:39] (step=0533400) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-17 06:36:19] (step=0533500) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 06:37:00] (step=0533600) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 06:37:40] (step=0533700) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 06:38:20] (step=0533800) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-17 06:39:01] (step=0533900) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 06:39:41] (step=0534000) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-17 06:39:53] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 06:40:34] (step=0534100) Train Loss: 0.0715, Train Steps/Sec: 1.90, Samples/Sec: 68.31
+[2025-05-17 06:41:14] (step=0534200) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 06:41:54] (step=0534300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 06:42:35] (step=0534400) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-17 06:43:15] (step=0534500) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:43:55] (step=0534600) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 06:44:36] (step=0534700) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:45:16] (step=0534800) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-17 06:45:56] (step=0534900) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 06:46:37] (step=0535000) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-17 06:47:17] (step=0535100) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 06:47:57] (step=0535200) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:48:38] (step=0535300) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 06:49:18] (step=0535400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:49:58] (step=0535500) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-17 06:50:39] (step=0535600) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 06:51:19] (step=0535700) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 06:51:59] (step=0535800) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 06:52:40] (step=0535900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 06:53:20] (step=0536000) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 06:53:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 06:54:13] (step=0536100) Train Loss: 0.0623, Train Steps/Sec: 1.90, Samples/Sec: 68.41
+[2025-05-17 06:54:53] (step=0536200) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 06:55:33] (step=0536300) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 06:56:14] (step=0536400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 06:56:54] (step=0536500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 06:57:34] (step=0536600) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 06:58:15] (step=0536700) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 06:58:55] (step=0536800) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 06:59:35] (step=0536900) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 07:00:16] (step=0537000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 07:00:56] (step=0537100) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:01:36] (step=0537200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:02:17] (step=0537300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 07:02:25] Beginning epoch 133...
+[2025-05-17 07:02:57] (step=0537400) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-17 07:03:38] (step=0537500) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 07:04:18] (step=0537600) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:04:58] (step=0537700) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:05:39] (step=0537800) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:06:19] (step=0537900) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:06:59] (step=0538000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 07:07:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 07:07:52] (step=0538100) Train Loss: 0.0654, Train Steps/Sec: 1.90, Samples/Sec: 68.40
+[2025-05-17 07:08:32] (step=0538200) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:09:13] (step=0538300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:09:53] (step=0538400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:10:33] (step=0538500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:11:14] (step=0538600) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:11:54] (step=0538700) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:12:34] (step=0538800) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 07:13:15] (step=0538900) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:13:55] (step=0539000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 07:14:35] (step=0539100) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 07:15:16] (step=0539200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:15:56] (step=0539300) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:16:36] (step=0539400) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:17:17] (step=0539500) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 07:17:57] (step=0539600) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 07:18:37] (step=0539700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 07:19:18] (step=0539800) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:19:58] (step=0539900) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:20:38] (step=0540000) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 07:21:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0540000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 07:21:06] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 07:23:06] (step=0540000) Perceptual Loss: 0.3147, Eval Time: 122.54
+[2025-05-17 07:23:46] (step=0540100) Train Loss: 0.0670, Train Steps/Sec: 0.53, Samples/Sec: 19.14
+[2025-05-17 07:24:27] (step=0540200) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 07:25:07] (step=0540300) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:25:47] (step=0540400) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:26:28] (step=0540500) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:27:08] (step=0540600) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:27:48] (step=0540700) Train Loss: 0.0757, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 07:28:29] (step=0540800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 07:29:09] (step=0540900) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:29:49] (step=0541000) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:30:30] (step=0541100) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 07:31:10] (step=0541200) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 07:31:50] (step=0541300) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 07:32:15] Beginning epoch 134...
+[2025-05-17 07:32:31] (step=0541400) Train Loss: 0.0687, Train Steps/Sec: 2.46, Samples/Sec: 88.43
+[2025-05-17 07:33:11] (step=0541500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:33:52] (step=0541600) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 07:34:32] (step=0541700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 07:35:12] (step=0541800) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 07:35:53] (step=0541900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 07:36:33] (step=0542000) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:36:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 07:37:26] (step=0542100) Train Loss: 0.0677, Train Steps/Sec: 1.90, Samples/Sec: 68.38
+[2025-05-17 07:38:06] (step=0542200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:38:46] (step=0542300) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:39:27] (step=0542400) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 07:40:07] (step=0542500) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 07:40:47] (step=0542600) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 07:41:27] (step=0542700) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 07:42:08] (step=0542800) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-17 07:42:48] (step=0542900) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:43:29] (step=0543000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:44:09] (step=0543100) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:44:49] (step=0543200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:45:30] (step=0543300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 07:46:10] (step=0543400) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 07:46:50] (step=0543500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:47:31] (step=0543600) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:48:11] (step=0543700) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:48:51] (step=0543800) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 07:49:32] (step=0543900) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 07:50:12] (step=0544000) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 07:50:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 07:51:05] (step=0544100) Train Loss: 0.0646, Train Steps/Sec: 1.90, Samples/Sec: 68.25
+[2025-05-17 07:51:45] (step=0544200) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:52:25] (step=0544300) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 07:53:06] (step=0544400) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 07:53:46] (step=0544500) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 07:54:26] (step=0544600) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:55:07] (step=0544700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 07:55:47] (step=0544800) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 07:56:27] (step=0544900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 07:57:08] (step=0545000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 07:57:48] (step=0545100) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 07:58:28] (step=0545200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 07:59:09] (step=0545300) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 07:59:49] (step=0545400) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.41
+[2025-05-17 07:59:49] Beginning epoch 135...
+[2025-05-17 08:00:30] (step=0545500) Train Loss: 0.0625, Train Steps/Sec: 2.45, Samples/Sec: 88.20
+[2025-05-17 08:01:10] (step=0545600) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 08:01:50] (step=0545700) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 08:02:31] (step=0545800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:03:11] (step=0545900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 08:03:51] (step=0546000) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:04:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 08:04:44] (step=0546100) Train Loss: 0.0675, Train Steps/Sec: 1.90, Samples/Sec: 68.32
+[2025-05-17 08:05:24] (step=0546200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-17 08:06:05] (step=0546300) Train Loss: 0.0720, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 08:06:45] (step=0546400) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 08:07:25] (step=0546500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:08:06] (step=0546600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 08:08:46] (step=0546700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 08:09:27] (step=0546800) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-17 08:10:07] (step=0546900) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 08:10:47] (step=0547000) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 08:11:28] (step=0547100) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:12:08] (step=0547200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 08:12:48] (step=0547300) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 08:13:28] (step=0547400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 08:14:09] (step=0547500) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 08:14:49] (step=0547600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:15:30] (step=0547700) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 08:16:10] (step=0547800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 08:16:50] (step=0547900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 08:17:31] (step=0548000) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 08:17:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 08:18:23] (step=0548100) Train Loss: 0.0627, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-17 08:19:03] (step=0548200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 08:19:44] (step=0548300) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 08:20:24] (step=0548400) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-17 08:21:04] (step=0548500) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:21:45] (step=0548600) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:22:25] (step=0548700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 08:23:05] (step=0548800) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 08:23:46] (step=0548900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:24:26] (step=0549000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 08:25:06] (step=0549100) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 08:25:47] (step=0549200) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:26:27] (step=0549300) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:27:07] (step=0549400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:27:23] Beginning epoch 136...
+[2025-05-17 08:27:48] (step=0549500) Train Loss: 0.0674, Train Steps/Sec: 2.46, Samples/Sec: 88.47
+[2025-05-17 08:28:28] (step=0549600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:29:09] (step=0549700) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 08:29:49] (step=0549800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:30:29] (step=0549900) Train Loss: 0.0748, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:31:10] (step=0550000) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 08:31:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 08:31:24] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 08:33:24] (step=0550000) Perceptual Loss: 0.3117, Eval Time: 122.45
+[2025-05-17 08:34:04] (step=0550100) Train Loss: 0.0674, Train Steps/Sec: 0.57, Samples/Sec: 20.58
+[2025-05-17 08:34:45] (step=0550200) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-17 08:35:25] (step=0550300) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 08:36:06] (step=0550400) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 08:36:46] (step=0550500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:37:26] (step=0550600) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 08:38:06] (step=0550700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 08:38:47] (step=0550800) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:39:27] (step=0550900) Train Loss: 0.0732, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 08:40:08] (step=0551000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 08:40:48] (step=0551100) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:41:28] (step=0551200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:42:08] (step=0551300) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 08:42:49] (step=0551400) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 08:43:29] (step=0551500) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 08:44:10] (step=0551600) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 08:44:50] (step=0551700) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 08:45:30] (step=0551800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 08:46:10] (step=0551900) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:46:51] (step=0552000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:47:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 08:47:43] (step=0552100) Train Loss: 0.0649, Train Steps/Sec: 1.90, Samples/Sec: 68.50
+[2025-05-17 08:48:24] (step=0552200) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 08:49:04] (step=0552300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 08:49:44] (step=0552400) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:50:25] (step=0552500) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:51:05] (step=0552600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:51:45] (step=0552700) Train Loss: 0.0745, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 08:52:26] (step=0552800) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 08:53:06] (step=0552900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 08:53:46] (step=0553000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 08:54:27] (step=0553100) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:55:07] (step=0553200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 08:55:47] (step=0553300) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 08:56:28] (step=0553400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 08:57:00] Beginning epoch 137...
+[2025-05-17 08:57:08] (step=0553500) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-17 08:57:49] (step=0553600) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 08:58:29] (step=0553700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 08:59:09] (step=0553800) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 08:59:50] (step=0553900) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 09:00:30] (step=0554000) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:00:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 09:01:22] (step=0554100) Train Loss: 0.0673, Train Steps/Sec: 1.91, Samples/Sec: 68.73
+[2025-05-17 09:02:03] (step=0554200) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 09:02:43] (step=0554300) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 09:03:23] (step=0554400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 09:04:04] (step=0554500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 09:04:44] (step=0554600) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 09:05:24] (step=0554700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:06:04] (step=0554800) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 09:06:45] (step=0554900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 09:07:25] (step=0555000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 09:08:06] (step=0555100) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:08:46] (step=0555200) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:09:26] (step=0555300) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:10:06] (step=0555400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:10:47] (step=0555500) Train Loss: 0.0609, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 09:11:27] (step=0555600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 09:12:08] (step=0555700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 09:12:48] (step=0555800) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:13:28] (step=0555900) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:14:08] (step=0556000) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:14:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 09:15:01] (step=0556100) Train Loss: 0.0676, Train Steps/Sec: 1.91, Samples/Sec: 68.66
+[2025-05-17 09:15:41] (step=0556200) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 09:16:22] (step=0556300) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 09:17:02] (step=0556400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 09:17:42] (step=0556500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:18:23] (step=0556600) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 09:19:03] (step=0556700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 09:19:43] (step=0556800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 09:20:24] (step=0556900) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 09:21:04] (step=0557000) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 09:21:44] (step=0557100) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 09:22:25] (step=0557200) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:23:05] (step=0557300) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:23:45] (step=0557400) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 09:24:26] (step=0557500) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 09:24:34] Beginning epoch 138...
+[2025-05-17 09:25:06] (step=0557600) Train Loss: 0.0684, Train Steps/Sec: 2.46, Samples/Sec: 88.47
+[2025-05-17 09:25:47] (step=0557700) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 09:26:27] (step=0557800) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:27:07] (step=0557900) Train Loss: 0.0739, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 09:27:48] (step=0558000) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 09:28:00] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 09:28:40] (step=0558100) Train Loss: 0.0661, Train Steps/Sec: 1.91, Samples/Sec: 68.68
+[2025-05-17 09:29:20] (step=0558200) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 09:30:01] (step=0558300) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 09:30:41] (step=0558400) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 09:31:21] (step=0558500) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 09:32:02] (step=0558600) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 09:32:42] (step=0558700) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 09:33:22] (step=0558800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 09:34:03] (step=0558900) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-17 09:34:43] (step=0559000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 09:35:24] (step=0559100) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:36:04] (step=0559200) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 09:36:44] (step=0559300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 09:37:24] (step=0559400) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:38:05] (step=0559500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 09:38:45] (step=0559600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 09:39:26] (step=0559700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 09:40:06] (step=0559800) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:40:46] (step=0559900) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:41:27] (step=0560000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 09:41:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0560000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 09:41:53] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 09:43:53] (step=0560000) Perceptual Loss: 0.3184, Eval Time: 122.46
+[2025-05-17 09:44:33] (step=0560100) Train Loss: 0.0643, Train Steps/Sec: 0.54, Samples/Sec: 19.27
+[2025-05-17 09:45:14] (step=0560200) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 09:45:54] (step=0560300) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-17 09:46:34] (step=0560400) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 09:47:15] (step=0560500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:47:55] (step=0560600) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:48:35] (step=0560700) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 09:49:16] (step=0560800) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 09:49:56] (step=0560900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 09:50:36] (step=0561000) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 09:51:17] (step=0561100) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:51:57] (step=0561200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 09:52:37] (step=0561300) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:53:18] (step=0561400) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 09:53:58] (step=0561500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 09:54:22] Beginning epoch 139...
+[2025-05-17 09:54:39] (step=0561600) Train Loss: 0.0703, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-17 09:55:19] (step=0561700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 09:55:59] (step=0561800) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:56:40] (step=0561900) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 09:57:20] (step=0562000) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 09:57:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 09:58:12] (step=0562100) Train Loss: 0.0669, Train Steps/Sec: 1.90, Samples/Sec: 68.52
+[2025-05-17 09:58:53] (step=0562200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 09:59:33] (step=0562300) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 10:00:13] (step=0562400) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 10:00:54] (step=0562500) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 10:01:34] (step=0562600) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:02:14] (step=0562700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:02:55] (step=0562800) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 10:03:35] (step=0562900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:04:15] (step=0563000) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 10:04:56] (step=0563100) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:05:36] (step=0563200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:06:16] (step=0563300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:06:57] (step=0563400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 10:07:37] (step=0563500) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-17 10:08:17] (step=0563600) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 10:08:58] (step=0563700) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:09:38] (step=0563800) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 10:10:18] (step=0563900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 10:10:58] (step=0564000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:11:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 10:11:51] (step=0564100) Train Loss: 0.0642, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-17 10:12:31] (step=0564200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 10:13:12] (step=0564300) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 10:13:52] (step=0564400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 10:14:32] (step=0564500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:15:12] (step=0564600) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 10:15:53] (step=0564700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:16:33] (step=0564800) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 10:17:14] (step=0564900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 10:17:54] (step=0565000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 10:18:34] (step=0565100) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 10:19:15] (step=0565200) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:19:55] (step=0565300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:20:35] (step=0565400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:21:16] (step=0565500) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 10:21:56] (step=0565600) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.43
+[2025-05-17 10:21:56] Beginning epoch 140...
+[2025-05-17 10:22:36] (step=0565700) Train Loss: 0.0694, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-17 10:23:17] (step=0565800) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:23:57] (step=0565900) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:24:37] (step=0566000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:24:49] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 10:25:30] (step=0566100) Train Loss: 0.0645, Train Steps/Sec: 1.91, Samples/Sec: 68.72
+[2025-05-17 10:26:10] (step=0566200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 10:26:50] (step=0566300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 10:27:31] (step=0566400) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:28:11] (step=0566500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:28:51] (step=0566600) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:29:32] (step=0566700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:30:12] (step=0566800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 10:30:52] (step=0566900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 10:31:33] (step=0567000) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 10:32:13] (step=0567100) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:32:53] (step=0567200) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 10:33:34] (step=0567300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 10:34:14] (step=0567400) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 10:34:54] (step=0567500) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 10:35:35] (step=0567600) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:36:15] (step=0567700) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-17 10:36:55] (step=0567800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:37:36] (step=0567900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 10:38:16] (step=0568000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:38:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 10:39:08] (step=0568100) Train Loss: 0.0650, Train Steps/Sec: 1.91, Samples/Sec: 68.79
+[2025-05-17 10:39:49] (step=0568200) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 10:40:29] (step=0568300) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 10:41:09] (step=0568400) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 10:41:50] (step=0568500) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:42:30] (step=0568600) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:43:10] (step=0568700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 10:43:51] (step=0568800) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 10:44:31] (step=0568900) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 10:45:12] (step=0569000) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-17 10:45:52] (step=0569100) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:46:32] (step=0569200) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 10:47:12] (step=0569300) Train Loss: 0.0591, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:47:53] (step=0569400) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 10:48:33] (step=0569500) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 10:49:14] (step=0569600) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 10:49:30] Beginning epoch 141...
+[2025-05-17 10:49:54] (step=0569700) Train Loss: 0.0692, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-17 10:50:34] (step=0569800) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 10:51:15] (step=0569900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 10:51:55] (step=0570000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 10:52:07] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 10:52:10] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 10:54:10] (step=0570000) Perceptual Loss: 0.2872, Eval Time: 122.53
+[2025-05-17 10:54:50] (step=0570100) Train Loss: 0.0660, Train Steps/Sec: 0.57, Samples/Sec: 20.52
+[2025-05-17 10:55:31] (step=0570200) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 10:56:11] (step=0570300) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 10:56:52] (step=0570400) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 10:57:32] (step=0570500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 10:58:12] (step=0570600) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 10:58:53] (step=0570700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 10:59:33] (step=0570800) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 11:00:13] (step=0570900) Train Loss: 0.0606, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 11:00:54] (step=0571000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-17 11:01:34] (step=0571100) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 11:02:14] (step=0571200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 11:02:55] (step=0571300) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 11:03:35] (step=0571400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 11:04:15] (step=0571500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:04:56] (step=0571600) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-17 11:05:36] (step=0571700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 11:06:17] (step=0571800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 11:06:57] (step=0571900) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 11:07:37] (step=0572000) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 11:07:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 11:08:30] (step=0572100) Train Loss: 0.0625, Train Steps/Sec: 1.89, Samples/Sec: 67.91
+[2025-05-17 11:09:11] (step=0572200) Train Loss: 0.0607, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-17 11:09:51] (step=0572300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 11:10:31] (step=0572400) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:11:12] (step=0572500) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:11:52] (step=0572600) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:12:32] (step=0572700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-17 11:13:13] (step=0572800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 11:13:53] (step=0572900) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-17 11:14:34] (step=0573000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 11:15:14] (step=0573100) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:15:54] (step=0573200) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:16:34] (step=0573300) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:17:15] (step=0573400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-17 11:17:55] (step=0573500) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:18:36] (step=0573600) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-17 11:19:08] Beginning epoch 142...
+[2025-05-17 11:19:16] (step=0573700) Train Loss: 0.0690, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-17 11:19:56] (step=0573800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 11:20:37] (step=0573900) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 11:21:17] (step=0574000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 11:21:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 11:22:10] (step=0574100) Train Loss: 0.0668, Train Steps/Sec: 1.90, Samples/Sec: 68.42
+[2025-05-17 11:22:50] (step=0574200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 11:23:30] (step=0574300) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 11:24:11] (step=0574400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 11:24:51] (step=0574500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 11:25:31] (step=0574600) Train Loss: 0.0605, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 11:26:12] (step=0574700) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 11:26:52] (step=0574800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 11:27:32] (step=0574900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 11:28:13] (step=0575000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 11:28:53] (step=0575100) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 11:29:33] (step=0575200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 11:30:14] (step=0575300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 11:30:54] (step=0575400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 11:31:34] (step=0575500) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 11:32:15] (step=0575600) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 11:32:55] (step=0575700) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-17 11:33:36] (step=0575800) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-17 11:34:16] (step=0575900) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 11:34:57] (step=0576000) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 11:35:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 11:35:50] (step=0576100) Train Loss: 0.0605, Train Steps/Sec: 1.86, Samples/Sec: 66.92
+[2025-05-17 11:36:31] (step=0576200) Train Loss: 0.0698, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-17 11:37:11] (step=0576300) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-17 11:37:52] (step=0576400) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-17 11:38:32] (step=0576500) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 11:39:13] (step=0576600) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 11:39:53] (step=0576700) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 11:40:34] (step=0576800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 11:41:14] (step=0576900) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 11:41:54] (step=0577000) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 11:42:35] (step=0577100) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 11:43:15] (step=0577200) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 11:43:55] (step=0577300) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 11:44:36] (step=0577400) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 11:45:16] (step=0577500) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 11:45:56] (step=0577600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 11:46:37] (step=0577700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 11:46:45] Beginning epoch 143...
+[2025-05-17 11:47:17] (step=0577800) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-17 11:47:58] (step=0577900) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 11:48:38] (step=0578000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 11:48:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 11:49:30] (step=0578100) Train Loss: 0.0679, Train Steps/Sec: 1.91, Samples/Sec: 68.59
+[2025-05-17 11:50:11] (step=0578200) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 11:50:51] (step=0578300) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 11:51:31] (step=0578400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 11:52:12] (step=0578500) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 11:52:52] (step=0578600) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 11:53:32] (step=0578700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 11:54:13] (step=0578800) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 11:54:53] (step=0578900) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 11:55:33] (step=0579000) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 11:56:14] (step=0579100) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 11:56:54] (step=0579200) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 11:57:34] (step=0579300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 11:58:15] (step=0579400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 11:58:55] (step=0579500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 11:59:35] (step=0579600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 12:00:16] (step=0579700) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 12:00:56] (step=0579800) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 12:01:36] (step=0579900) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 12:02:17] (step=0580000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 12:02:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0580000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 12:02:44] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 12:04:44] (step=0580000) Perceptual Loss: 0.3182, Eval Time: 122.48
+[2025-05-17 12:05:24] (step=0580100) Train Loss: 0.0730, Train Steps/Sec: 0.53, Samples/Sec: 19.18
+[2025-05-17 12:06:05] (step=0580200) Train Loss: 0.0603, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:06:45] (step=0580300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 12:07:25] (step=0580400) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:08:06] (step=0580500) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:08:46] (step=0580600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 12:09:26] (step=0580700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 12:10:07] (step=0580800) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-17 12:10:47] (step=0580900) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:11:27] (step=0581000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 12:12:08] (step=0581100) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:12:48] (step=0581200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:13:28] (step=0581300) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:14:09] (step=0581400) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 12:14:49] (step=0581500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 12:15:29] (step=0581600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 12:16:10] (step=0581700) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:16:34] Beginning epoch 144...
+[2025-05-17 12:16:50] (step=0581800) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-17 12:17:31] (step=0581900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 12:18:11] (step=0582000) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 12:18:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 12:19:04] (step=0582100) Train Loss: 0.0663, Train Steps/Sec: 1.90, Samples/Sec: 68.47
+[2025-05-17 12:19:44] (step=0582200) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 12:20:24] (step=0582300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 12:21:04] (step=0582400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:21:45] (step=0582500) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 12:22:25] (step=0582600) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:23:05] (step=0582700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 12:23:46] (step=0582800) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 12:24:26] (step=0582900) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:25:06] (step=0583000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 12:25:47] (step=0583100) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:26:27] (step=0583200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:27:07] (step=0583300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:27:48] (step=0583400) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 12:28:28] (step=0583500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 12:29:08] (step=0583600) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:29:49] (step=0583700) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 12:30:29] (step=0583800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:31:09] (step=0583900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:31:50] (step=0584000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:32:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 12:32:43] (step=0584100) Train Loss: 0.0679, Train Steps/Sec: 1.88, Samples/Sec: 67.68
+[2025-05-17 12:33:23] (step=0584200) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:34:04] (step=0584300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 12:34:44] (step=0584400) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 12:35:24] (step=0584500) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:36:04] (step=0584600) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 12:36:45] (step=0584700) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 12:37:25] (step=0584800) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 12:38:05] (step=0584900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:38:46] (step=0585000) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 12:39:26] (step=0585100) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 12:40:06] (step=0585200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:40:47] (step=0585300) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:41:27] (step=0585400) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:42:07] (step=0585500) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-17 12:42:48] (step=0585600) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 12:43:28] (step=0585700) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 12:44:08] (step=0585800) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.42
+[2025-05-17 12:44:08] Beginning epoch 145...
+[2025-05-17 12:44:49] (step=0585900) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-17 12:45:29] (step=0586000) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 12:45:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 12:46:22] (step=0586100) Train Loss: 0.0632, Train Steps/Sec: 1.90, Samples/Sec: 68.38
+[2025-05-17 12:47:02] (step=0586200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:47:43] (step=0586300) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 12:48:23] (step=0586400) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:49:03] (step=0586500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:49:44] (step=0586600) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:50:24] (step=0586700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 12:51:04] (step=0586800) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 12:51:45] (step=0586900) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:52:25] (step=0587000) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 12:53:05] (step=0587100) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:53:46] (step=0587200) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:54:26] (step=0587300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:55:06] (step=0587400) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 12:55:47] (step=0587500) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 12:56:27] (step=0587600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 12:57:07] (step=0587700) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 12:57:48] (step=0587800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:58:28] (step=0587900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:59:08] (step=0588000) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 12:59:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 13:00:01] (step=0588100) Train Loss: 0.0638, Train Steps/Sec: 1.90, Samples/Sec: 68.54
+[2025-05-17 13:00:41] (step=0588200) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 13:01:21] (step=0588300) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 13:02:02] (step=0588400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 13:02:42] (step=0588500) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 13:03:22] (step=0588600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 13:04:03] (step=0588700) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 13:04:43] (step=0588800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 13:05:23] (step=0588900) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 13:06:04] (step=0589000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 13:06:44] (step=0589100) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 13:07:24] (step=0589200) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 13:08:05] (step=0589300) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 13:08:45] (step=0589400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 13:09:25] (step=0589500) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 13:10:06] (step=0589600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 13:10:46] (step=0589700) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 13:11:26] (step=0589800) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 13:11:43] Beginning epoch 146...
+[2025-05-17 13:12:07] (step=0589900) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-17 13:12:47] (step=0590000) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 13:12:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 13:13:02] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 13:15:02] (step=0590000) Perceptual Loss: 0.3092, Eval Time: 122.24
+[2025-05-17 13:15:42] (step=0590100) Train Loss: 0.0689, Train Steps/Sec: 0.57, Samples/Sec: 20.60
+[2025-05-17 13:16:22] (step=0590200) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 13:17:03] (step=0590300) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 13:17:43] (step=0590400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:18:23] (step=0590500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:19:04] (step=0590600) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:19:44] (step=0590700) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-17 13:20:24] (step=0590800) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:21:05] (step=0590900) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 13:21:45] (step=0591000) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:22:25] (step=0591100) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:23:06] (step=0591200) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-17 13:23:46] (step=0591300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-17 13:24:26] (step=0591400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 13:25:07] (step=0591500) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 13:25:47] (step=0591600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-17 13:26:27] (step=0591700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 13:27:07] (step=0591800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 13:27:48] (step=0591900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 13:28:28] (step=0592000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 13:28:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 13:29:21] (step=0592100) Train Loss: 0.0649, Train Steps/Sec: 1.89, Samples/Sec: 67.92
+[2025-05-17 13:30:01] (step=0592200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 13:30:42] (step=0592300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 13:31:22] (step=0592400) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 13:32:02] (step=0592500) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:32:43] (step=0592600) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:33:23] (step=0592700) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:34:03] (step=0592800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 13:34:44] (step=0592900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 13:35:24] (step=0593000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 13:36:04] (step=0593100) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 13:36:45] (step=0593200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 13:37:25] (step=0593300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 13:38:05] (step=0593400) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 13:38:46] (step=0593500) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:39:26] (step=0593600) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 13:40:06] (step=0593700) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 13:40:47] (step=0593800) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:41:19] Beginning epoch 147...
+[2025-05-17 13:41:27] (step=0593900) Train Loss: 0.0720, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-17 13:42:07] (step=0594000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:42:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 13:43:00] (step=0594100) Train Loss: 0.0676, Train Steps/Sec: 1.91, Samples/Sec: 68.76
+[2025-05-17 13:43:40] (step=0594200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 13:44:21] (step=0594300) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 13:45:01] (step=0594400) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:45:41] (step=0594500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 13:46:21] (step=0594600) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:47:02] (step=0594700) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:47:42] (step=0594800) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 13:48:22] (step=0594900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 13:49:03] (step=0595000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 13:49:43] (step=0595100) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:50:23] (step=0595200) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:51:04] (step=0595300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:51:44] (step=0595400) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:52:24] (step=0595500) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 13:53:05] (step=0595600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 13:53:45] (step=0595700) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 13:54:25] (step=0595800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:55:06] (step=0595900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 13:55:46] (step=0596000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:55:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 13:56:38] (step=0596100) Train Loss: 0.0693, Train Steps/Sec: 1.91, Samples/Sec: 68.75
+[2025-05-17 13:57:19] (step=0596200) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 13:57:59] (step=0596300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 13:58:39] (step=0596400) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 13:59:20] (step=0596500) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 14:00:00] (step=0596600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 14:00:40] (step=0596700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 14:01:20] (step=0596800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 14:02:01] (step=0596900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 14:02:41] (step=0597000) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 14:03:21] (step=0597100) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 14:04:02] (step=0597200) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 14:04:42] (step=0597300) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 14:05:22] (step=0597400) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 14:06:03] (step=0597500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 14:06:43] (step=0597600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 14:07:23] (step=0597700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 14:08:04] (step=0597800) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 14:08:44] (step=0597900) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 14:08:52] Beginning epoch 148...
+[2025-05-17 14:09:24] (step=0598000) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-17 14:09:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 14:10:17] (step=0598100) Train Loss: 0.0647, Train Steps/Sec: 1.92, Samples/Sec: 68.98
+[2025-05-17 14:10:57] (step=0598200) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 14:11:37] (step=0598300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 14:12:18] (step=0598400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 14:12:58] (step=0598500) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 14:13:38] (step=0598600) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 14:14:19] (step=0598700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 14:14:59] (step=0598800) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 14:15:39] (step=0598900) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 14:16:20] (step=0599000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 14:17:00] (step=0599100) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:17:40] (step=0599200) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:18:21] (step=0599300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:19:01] (step=0599400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:19:41] (step=0599500) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 14:20:22] (step=0599600) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 14:21:02] (step=0599700) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 14:21:42] (step=0599800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.40
+[2025-05-17 14:22:23] (step=0599900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 14:23:03] (step=0600000) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 14:23:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0600000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 14:23:30] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 14:25:29] (step=0600000) Perceptual Loss: 0.3205, Eval Time: 122.37
+[2025-05-17 14:26:10] (step=0600100) Train Loss: 0.0635, Train Steps/Sec: 0.54, Samples/Sec: 19.27
+[2025-05-17 14:26:50] (step=0600200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:27:30] (step=0600300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 14:28:11] (step=0600400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 14:28:51] (step=0600500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:29:31] (step=0600600) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:30:12] (step=0600700) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 14:30:52] (step=0600800) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 14:31:32] (step=0600900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 14:32:13] (step=0601000) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 14:32:53] (step=0601100) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 14:33:33] (step=0601200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:34:14] (step=0601300) Train Loss: 0.0617, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:34:54] (step=0601400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 14:35:34] (step=0601500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 14:36:15] (step=0601600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 14:36:55] (step=0601700) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 14:37:35] (step=0601800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:38:16] (step=0601900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:38:40] Beginning epoch 149...
+[2025-05-17 14:38:56] (step=0602000) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-17 14:39:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 14:39:49] (step=0602100) Train Loss: 0.0681, Train Steps/Sec: 1.90, Samples/Sec: 68.44
+[2025-05-17 14:40:29] (step=0602200) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:41:09] (step=0602300) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 14:41:50] (step=0602400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:42:30] (step=0602500) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:43:10] (step=0602600) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 14:43:51] (step=0602700) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:44:31] (step=0602800) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-17 14:45:11] (step=0602900) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:45:52] (step=0603000) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 14:46:32] (step=0603100) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:47:12] (step=0603200) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:47:53] (step=0603300) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:48:33] (step=0603400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 14:49:13] (step=0603500) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 14:49:54] (step=0603600) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 14:50:34] (step=0603700) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:51:14] (step=0603800) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:51:55] (step=0603900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:52:35] (step=0604000) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:52:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 14:53:27] (step=0604100) Train Loss: 0.0656, Train Steps/Sec: 1.91, Samples/Sec: 68.76
+[2025-05-17 14:54:08] (step=0604200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:54:48] (step=0604300) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 14:55:28] (step=0604400) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:56:09] (step=0604500) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 14:56:49] (step=0604600) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 14:57:29] (step=0604700) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:58:10] (step=0604800) Train Loss: 0.0615, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-17 14:58:50] (step=0604900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 14:59:30] (step=0605000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 15:00:11] (step=0605100) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 15:00:51] (step=0605200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:01:31] (step=0605300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:02:12] (step=0605400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:02:52] (step=0605500) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 15:03:32] (step=0605600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:04:13] (step=0605700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 15:04:53] (step=0605800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:05:33] (step=0605900) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:06:13] (step=0606000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.42
+[2025-05-17 15:06:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 15:06:25] Beginning epoch 150...
+[2025-05-17 15:07:06] (step=0606100) Train Loss: 0.0686, Train Steps/Sec: 1.91, Samples/Sec: 68.64
+[2025-05-17 15:07:46] (step=0606200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:08:27] (step=0606300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 15:09:07] (step=0606400) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-17 15:09:47] (step=0606500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:10:27] (step=0606600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:11:08] (step=0606700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:11:48] (step=0606800) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-17 15:12:28] (step=0606900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:13:09] (step=0607000) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 15:13:49] (step=0607100) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:14:29] (step=0607200) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:15:10] (step=0607300) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:15:50] (step=0607400) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-17 15:16:30] (step=0607500) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 15:17:11] (step=0607600) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-17 15:17:51] (step=0607700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 15:18:31] (step=0607800) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:19:11] (step=0607900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:19:52] (step=0608000) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:20:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 15:20:44] (step=0608100) Train Loss: 0.0720, Train Steps/Sec: 1.92, Samples/Sec: 69.18
+[2025-05-17 15:21:24] (step=0608200) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-17 15:22:04] (step=0608300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:22:45] (step=0608400) Train Loss: 0.0749, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-17 15:23:25] (step=0608500) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:24:05] (step=0608600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.40
+[2025-05-17 15:24:46] (step=0608700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-17 15:25:26] (step=0608800) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-17 15:26:06] (step=0608900) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-17 15:26:47] (step=0609000) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 15:27:27] (step=0609100) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:28:07] (step=0609200) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:28:48] (step=0609300) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-17 15:29:28] (step=0609400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:30:08] (step=0609500) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 15:30:49] (step=0609600) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:31:29] (step=0609700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-17 15:32:09] (step=0609800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:32:49] (step=0609900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:33:30] (step=0610000) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 15:33:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 15:33:44] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 15:35:44] (step=0610000) Perceptual Loss: 0.2970, Eval Time: 122.14
+[2025-05-17 15:36:00] Beginning epoch 151...
+[2025-05-17 15:36:24] (step=0610100) Train Loss: 0.0665, Train Steps/Sec: 0.57, Samples/Sec: 20.61
+[2025-05-17 15:37:05] (step=0610200) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 15:37:45] (step=0610300) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 15:38:26] (step=0610400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 15:39:06] (step=0610500) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:39:46] (step=0610600) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:40:26] (step=0610700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 15:41:07] (step=0610800) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:41:47] (step=0610900) Train Loss: 0.0728, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 15:42:27] (step=0611000) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 15:43:08] (step=0611100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:43:48] (step=0611200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:44:28] (step=0611300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:45:09] (step=0611400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 15:45:49] (step=0611500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:46:29] (step=0611600) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-17 15:47:10] (step=0611700) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 15:47:50] (step=0611800) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:48:30] (step=0611900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 15:49:11] (step=0612000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:49:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 15:50:03] (step=0612100) Train Loss: 0.0735, Train Steps/Sec: 1.91, Samples/Sec: 68.83
+[2025-05-17 15:50:43] (step=0612200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 15:51:24] (step=0612300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 15:52:04] (step=0612400) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:52:44] (step=0612500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:53:24] (step=0612600) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:54:05] (step=0612700) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:54:45] (step=0612800) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-17 15:55:25] (step=0612900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 15:56:06] (step=0613000) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 15:56:46] (step=0613100) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 15:57:26] (step=0613200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:58:07] (step=0613300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 15:58:47] (step=0613400) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 15:59:27] (step=0613500) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:00:08] (step=0613600) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-17 16:00:48] (step=0613700) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 16:01:28] (step=0613800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 16:02:09] (step=0613900) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 16:02:49] (step=0614000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:03:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 16:03:34] Beginning epoch 152...
+[2025-05-17 16:03:42] (step=0614100) Train Loss: 0.0659, Train Steps/Sec: 1.88, Samples/Sec: 67.67
+[2025-05-17 16:04:22] (step=0614200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 16:05:03] (step=0614300) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 16:05:43] (step=0614400) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 16:06:23] (step=0614500) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:07:04] (step=0614600) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 16:07:44] (step=0614700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 16:08:24] (step=0614800) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 16:09:05] (step=0614900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:09:45] (step=0615000) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 16:10:25] (step=0615100) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:11:06] (step=0615200) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:11:46] (step=0615300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:12:26] (step=0615400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 16:13:07] (step=0615500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 16:13:47] (step=0615600) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 16:14:27] (step=0615700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 16:15:08] (step=0615800) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:15:48] (step=0615900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:16:28] (step=0616000) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 16:16:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 16:17:21] (step=0616100) Train Loss: 0.0696, Train Steps/Sec: 1.91, Samples/Sec: 68.85
+[2025-05-17 16:18:01] (step=0616200) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 16:18:41] (step=0616300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 16:19:22] (step=0616400) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:20:02] (step=0616500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 16:20:42] (step=0616600) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 16:21:22] (step=0616700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 16:22:03] (step=0616800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 16:22:43] (step=0616900) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 16:23:24] (step=0617000) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 16:24:04] (step=0617100) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 16:24:44] (step=0617200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:25:25] (step=0617300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:26:05] (step=0617400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:26:45] (step=0617500) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 16:27:26] (step=0617600) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 16:28:06] (step=0617700) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 16:28:46] (step=0617800) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:29:26] (step=0617900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:30:07] (step=0618000) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:30:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 16:30:59] (step=0618100) Train Loss: 0.0718, Train Steps/Sec: 1.92, Samples/Sec: 69.03
+[2025-05-17 16:31:07] Beginning epoch 153...
+[2025-05-17 16:31:40] (step=0618200) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-17 16:32:20] (step=0618300) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 16:33:00] (step=0618400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 16:33:41] (step=0618500) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:34:21] (step=0618600) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:35:01] (step=0618700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:35:41] (step=0618800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 16:36:22] (step=0618900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:37:02] (step=0619000) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 16:37:43] (step=0619100) Train Loss: 0.0755, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 16:38:23] (step=0619200) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:39:03] (step=0619300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:39:43] (step=0619400) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:40:24] (step=0619500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 16:41:04] (step=0619600) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:41:45] (step=0619700) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-17 16:42:25] (step=0619800) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:43:05] (step=0619900) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 16:43:45] (step=0620000) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 16:44:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0620000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 16:44:12] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 16:46:11] (step=0620000) Perceptual Loss: 0.3190, Eval Time: 122.28
+[2025-05-17 16:46:52] (step=0620100) Train Loss: 0.0667, Train Steps/Sec: 0.54, Samples/Sec: 19.31
+[2025-05-17 16:47:32] (step=0620200) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 16:48:13] (step=0620300) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 16:48:53] (step=0620400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 16:49:33] (step=0620500) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:50:14] (step=0620600) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 16:50:54] (step=0620700) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 16:51:34] (step=0620800) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:52:15] (step=0620900) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 16:52:55] (step=0621000) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:53:35] (step=0621100) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 16:54:16] (step=0621200) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:54:56] (step=0621300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:55:36] (step=0621400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 16:56:17] (step=0621500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 16:56:57] (step=0621600) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 16:57:37] (step=0621700) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 16:58:18] (step=0621800) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 16:58:58] (step=0621900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 16:59:38] (step=0622000) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 16:59:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 17:00:31] (step=0622100) Train Loss: 0.0660, Train Steps/Sec: 1.91, Samples/Sec: 68.82
+[2025-05-17 17:00:55] Beginning epoch 154...
+[2025-05-17 17:01:11] (step=0622200) Train Loss: 0.0698, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-17 17:01:52] (step=0622300) Train Loss: 0.0717, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 17:02:32] (step=0622400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 17:03:12] (step=0622500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:03:52] (step=0622600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:04:33] (step=0622700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:05:13] (step=0622800) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 17:05:53] (step=0622900) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 17:06:34] (step=0623000) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 17:07:14] (step=0623100) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 17:07:54] (step=0623200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:08:35] (step=0623300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:09:15] (step=0623400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 17:09:55] (step=0623500) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 17:10:36] (step=0623600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 17:11:16] (step=0623700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 17:11:56] (step=0623800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:12:37] (step=0623900) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:13:17] (step=0624000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:13:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 17:14:10] (step=0624100) Train Loss: 0.0681, Train Steps/Sec: 1.89, Samples/Sec: 68.03
+[2025-05-17 17:14:50] (step=0624200) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 17:15:31] (step=0624300) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 17:16:11] (step=0624400) Train Loss: 0.0605, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:16:51] (step=0624500) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:17:32] (step=0624600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 17:18:12] (step=0624700) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 17:18:52] (step=0624800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 17:19:33] (step=0624900) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 17:20:13] (step=0625000) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-17 17:20:53] (step=0625100) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 17:21:34] (step=0625200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 17:22:14] (step=0625300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:22:54] (step=0625400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 17:23:35] (step=0625500) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 17:24:15] (step=0625600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 17:24:56] (step=0625700) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-17 17:25:36] (step=0625800) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:26:16] (step=0625900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:26:57] (step=0626000) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 17:27:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 17:27:49] (step=0626100) Train Loss: 0.0665, Train Steps/Sec: 1.92, Samples/Sec: 68.95
+[2025-05-17 17:28:29] (step=0626200) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.44
+[2025-05-17 17:28:29] Beginning epoch 155...
+[2025-05-17 17:29:10] (step=0626300) Train Loss: 0.0695, Train Steps/Sec: 2.45, Samples/Sec: 88.31
+[2025-05-17 17:29:50] (step=0626400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 17:30:30] (step=0626500) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:31:11] (step=0626600) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 17:31:51] (step=0626700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:32:31] (step=0626800) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 17:33:12] (step=0626900) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:33:52] (step=0627000) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 17:34:33] (step=0627100) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:35:13] (step=0627200) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 17:35:53] (step=0627300) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:36:33] (step=0627400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:37:14] (step=0627500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 17:37:54] (step=0627600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:38:35] (step=0627700) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-17 17:39:15] (step=0627800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 17:39:55] (step=0627900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:40:35] (step=0628000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 17:40:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 17:41:28] (step=0628100) Train Loss: 0.0646, Train Steps/Sec: 1.92, Samples/Sec: 69.18
+[2025-05-17 17:42:08] (step=0628200) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 17:42:48] (step=0628300) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 17:43:29] (step=0628400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 17:44:09] (step=0628500) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:44:49] (step=0628600) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:45:30] (step=0628700) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:46:10] (step=0628800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 17:46:50] (step=0628900) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:47:31] (step=0629000) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 17:48:11] (step=0629100) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:48:51] (step=0629200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 17:49:32] (step=0629300) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 17:50:12] (step=0629400) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 17:50:52] (step=0629500) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 17:51:33] (step=0629600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 17:52:13] (step=0629700) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-17 17:52:53] (step=0629800) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 17:53:34] (step=0629900) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 17:54:14] (step=0630000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 17:54:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 17:54:29] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 17:56:29] (step=0630000) Perceptual Loss: 0.3208, Eval Time: 122.30
+[2025-05-17 17:57:09] (step=0630100) Train Loss: 0.0669, Train Steps/Sec: 0.57, Samples/Sec: 20.54
+[2025-05-17 17:57:50] (step=0630200) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 17:58:06] Beginning epoch 156...
+[2025-05-17 17:58:30] (step=0630300) Train Loss: 0.0654, Train Steps/Sec: 2.45, Samples/Sec: 88.33
+[2025-05-17 17:59:11] (step=0630400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 17:59:51] (step=0630500) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:00:31] (step=0630600) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:01:12] (step=0630700) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 18:01:52] (step=0630800) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:02:32] (step=0630900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:03:13] (step=0631000) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-17 18:03:53] (step=0631100) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:04:33] (step=0631200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 18:05:14] (step=0631300) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 18:05:54] (step=0631400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 18:06:34] (step=0631500) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:07:15] (step=0631600) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 18:07:55] (step=0631700) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 18:08:36] (step=0631800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 18:09:16] (step=0631900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:09:56] (step=0632000) Train Loss: 0.0758, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 18:10:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 18:10:48] (step=0632100) Train Loss: 0.0666, Train Steps/Sec: 1.92, Samples/Sec: 69.01
+[2025-05-17 18:11:29] (step=0632200) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 18:12:09] (step=0632300) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-17 18:12:49] (step=0632400) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 18:13:30] (step=0632500) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:14:10] (step=0632600) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:14:50] (step=0632700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 18:15:31] (step=0632800) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 18:16:11] (step=0632900) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 18:16:52] (step=0633000) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 18:17:32] (step=0633100) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:18:12] (step=0633200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:18:52] (step=0633300) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:19:33] (step=0633400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 18:20:13] (step=0633500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 18:20:53] (step=0633600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:21:34] (step=0633700) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 18:22:14] (step=0633800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:22:54] (step=0633900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:23:35] (step=0634000) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:23:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 18:24:27] (step=0634100) Train Loss: 0.0678, Train Steps/Sec: 1.91, Samples/Sec: 68.89
+[2025-05-17 18:25:07] (step=0634200) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:25:40] Beginning epoch 157...
+[2025-05-17 18:25:48] (step=0634300) Train Loss: 0.0738, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-17 18:26:28] (step=0634400) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 18:27:08] (step=0634500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:27:49] (step=0634600) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:28:29] (step=0634700) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:29:09] (step=0634800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 18:29:50] (step=0634900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:30:30] (step=0635000) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 18:31:11] (step=0635100) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 18:31:51] (step=0635200) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:32:31] (step=0635300) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:33:11] (step=0635400) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:33:52] (step=0635500) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 18:34:32] (step=0635600) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:35:12] (step=0635700) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 18:35:53] (step=0635800) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 18:36:33] (step=0635900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:37:13] (step=0636000) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:37:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 18:38:06] (step=0636100) Train Loss: 0.0624, Train Steps/Sec: 1.92, Samples/Sec: 69.00
+[2025-05-17 18:38:46] (step=0636200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:39:26] (step=0636300) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 18:40:07] (step=0636400) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 18:40:47] (step=0636500) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:41:27] (step=0636600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:42:07] (step=0636700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:42:48] (step=0636800) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 18:43:28] (step=0636900) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:44:09] (step=0637000) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 18:44:49] (step=0637100) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 18:45:29] (step=0637200) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 18:46:10] (step=0637300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:46:50] (step=0637400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 18:47:30] (step=0637500) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 18:48:11] (step=0637600) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 18:48:51] (step=0637700) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 18:49:31] (step=0637800) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 18:50:12] (step=0637900) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 18:50:52] (step=0638000) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:51:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 18:51:44] (step=0638100) Train Loss: 0.0718, Train Steps/Sec: 1.92, Samples/Sec: 69.18
+[2025-05-17 18:52:24] (step=0638200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 18:53:05] (step=0638300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:53:13] Beginning epoch 158...
+[2025-05-17 18:53:45] (step=0638400) Train Loss: 0.0668, Train Steps/Sec: 2.46, Samples/Sec: 88.48
+[2025-05-17 18:54:26] (step=0638500) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:55:06] (step=0638600) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 18:55:46] (step=0638700) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 18:56:27] (step=0638800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 18:57:07] (step=0638900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 18:57:47] (step=0639000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 18:58:28] (step=0639100) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 18:59:08] (step=0639200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 18:59:48] (step=0639300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:00:28] (step=0639400) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 19:01:09] (step=0639500) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 19:01:49] (step=0639600) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:02:29] (step=0639700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 19:03:10] (step=0639800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 19:03:50] (step=0639900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 19:04:30] (step=0640000) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 19:04:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0640000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 19:04:56] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 19:06:56] (step=0640000) Perceptual Loss: 0.3129, Eval Time: 122.16
+[2025-05-17 19:07:36] (step=0640100) Train Loss: 0.0675, Train Steps/Sec: 0.54, Samples/Sec: 19.37
+[2025-05-17 19:08:17] (step=0640200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 19:08:57] (step=0640300) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-17 19:09:37] (step=0640400) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 19:10:18] (step=0640500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 19:10:58] (step=0640600) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 19:11:38] (step=0640700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 19:12:19] (step=0640800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 19:12:59] (step=0640900) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 19:13:39] (step=0641000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 19:14:20] (step=0641100) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:15:00] (step=0641200) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 19:15:40] (step=0641300) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 19:16:21] (step=0641400) Train Loss: 0.0766, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 19:17:01] (step=0641500) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 19:17:41] (step=0641600) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 19:18:22] (step=0641700) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 19:19:02] (step=0641800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:19:42] (step=0641900) Train Loss: 0.0607, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 19:20:23] (step=0642000) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:20:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 19:21:15] (step=0642100) Train Loss: 0.0655, Train Steps/Sec: 1.92, Samples/Sec: 68.97
+[2025-05-17 19:21:55] (step=0642200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 19:22:36] (step=0642300) Train Loss: 0.0738, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 19:23:00] Beginning epoch 159...
+[2025-05-17 19:23:16] (step=0642400) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-17 19:23:56] (step=0642500) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:24:37] (step=0642600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:25:17] (step=0642700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:25:57] (step=0642800) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 19:26:38] (step=0642900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 19:27:18] (step=0643000) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-17 19:27:58] (step=0643100) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 19:28:39] (step=0643200) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:29:19] (step=0643300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:29:59] (step=0643400) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 19:30:39] (step=0643500) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 19:31:20] (step=0643600) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 19:32:00] (step=0643700) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 19:32:41] (step=0643800) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:33:21] (step=0643900) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:34:01] (step=0644000) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 19:34:13] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 19:34:53] (step=0644100) Train Loss: 0.0680, Train Steps/Sec: 1.91, Samples/Sec: 68.90
+[2025-05-17 19:35:34] (step=0644200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 19:36:14] (step=0644300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 19:36:54] (step=0644400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 19:37:35] (step=0644500) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 19:38:15] (step=0644600) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 19:38:55] (step=0644700) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 19:39:36] (step=0644800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 19:40:16] (step=0644900) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 19:40:56] (step=0645000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 19:41:37] (step=0645100) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 19:42:17] (step=0645200) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 19:42:57] (step=0645300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 19:43:38] (step=0645400) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 19:44:18] (step=0645500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 19:44:58] (step=0645600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 19:45:39] (step=0645700) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-17 19:46:19] (step=0645800) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 19:46:59] (step=0645900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 19:47:40] (step=0646000) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 19:47:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 19:48:32] (step=0646100) Train Loss: 0.0657, Train Steps/Sec: 1.91, Samples/Sec: 68.85
+[2025-05-17 19:49:12] (step=0646200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 19:49:53] (step=0646300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 19:50:33] (step=0646400) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 19:50:33] Beginning epoch 160...
+[2025-05-17 19:51:14] (step=0646500) Train Loss: 0.0630, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-17 19:51:54] (step=0646600) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 19:52:34] (step=0646700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 19:53:15] (step=0646800) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 19:53:55] (step=0646900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 19:54:35] (step=0647000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 19:55:16] (step=0647100) Train Loss: 0.0612, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 19:55:56] (step=0647200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 19:56:36] (step=0647300) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 19:57:17] (step=0647400) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 19:57:57] (step=0647500) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 19:58:37] (step=0647600) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 19:59:18] (step=0647700) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 19:59:58] (step=0647800) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 20:00:38] (step=0647900) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 20:01:19] (step=0648000) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 20:01:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 20:02:11] (step=0648100) Train Loss: 0.0649, Train Steps/Sec: 1.92, Samples/Sec: 69.04
+[2025-05-17 20:02:51] (step=0648200) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 20:03:32] (step=0648300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 20:04:12] (step=0648400) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 20:04:52] (step=0648500) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:05:33] (step=0648600) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 20:06:13] (step=0648700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 20:06:53] (step=0648800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 20:07:33] (step=0648900) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 20:08:14] (step=0649000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 20:08:54] (step=0649100) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 20:09:35] (step=0649200) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 20:10:15] (step=0649300) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 20:10:55] (step=0649400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 20:11:36] (step=0649500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 20:12:16] (step=0649600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 20:12:56] (step=0649700) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 20:13:37] (step=0649800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 20:14:17] (step=0649900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 20:14:57] (step=0650000) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 20:15:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 20:15:12] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 20:17:11] (step=0650000) Perceptual Loss: 0.3165, Eval Time: 122.20
+[2025-05-17 20:17:52] (step=0650100) Train Loss: 0.0605, Train Steps/Sec: 0.57, Samples/Sec: 20.62
+[2025-05-17 20:18:32] (step=0650200) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-17 20:19:13] (step=0650300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-17 20:19:53] (step=0650400) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-17 20:20:09] Beginning epoch 161...
+[2025-05-17 20:20:34] (step=0650500) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-17 20:21:14] (step=0650600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:21:54] (step=0650700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 20:22:35] (step=0650800) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:23:15] (step=0650900) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 20:23:55] (step=0651000) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 20:24:36] (step=0651100) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 20:25:16] (step=0651200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 20:25:56] (step=0651300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 20:26:37] (step=0651400) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 20:27:17] (step=0651500) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 20:27:57] (step=0651600) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 20:28:38] (step=0651700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 20:29:18] (step=0651800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 20:29:58] (step=0651900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 20:30:39] (step=0652000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 20:30:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 20:31:31] (step=0652100) Train Loss: 0.0644, Train Steps/Sec: 1.91, Samples/Sec: 68.89
+[2025-05-17 20:32:11] (step=0652200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 20:32:51] (step=0652300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 20:33:32] (step=0652400) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 20:34:12] (step=0652500) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 20:34:52] (step=0652600) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:35:33] (step=0652700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:36:13] (step=0652800) Train Loss: 0.0617, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 20:36:53] (step=0652900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:37:34] (step=0653000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 20:38:14] (step=0653100) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 20:38:54] (step=0653200) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 20:39:35] (step=0653300) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 20:40:15] (step=0653400) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 20:40:56] (step=0653500) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:41:36] (step=0653600) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 20:42:16] (step=0653700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 20:42:57] (step=0653800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 20:43:37] (step=0653900) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 20:44:17] (step=0654000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 20:44:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 20:45:10] (step=0654100) Train Loss: 0.0703, Train Steps/Sec: 1.89, Samples/Sec: 68.13
+[2025-05-17 20:45:50] (step=0654200) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 20:46:31] (step=0654300) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 20:47:11] (step=0654400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 20:47:43] Beginning epoch 162...
+[2025-05-17 20:47:52] (step=0654500) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-17 20:48:32] (step=0654600) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 20:49:12] (step=0654700) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 20:49:52] (step=0654800) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 20:50:33] (step=0654900) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:51:13] (step=0655000) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-17 20:51:54] (step=0655100) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 20:52:34] (step=0655200) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:53:14] (step=0655300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 20:53:54] (step=0655400) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 20:54:35] (step=0655500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 20:55:15] (step=0655600) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-17 20:55:56] (step=0655700) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-17 20:56:36] (step=0655800) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-17 20:57:17] (step=0655900) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 20:57:57] (step=0656000) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-17 20:58:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 20:58:50] (step=0656100) Train Loss: 0.0649, Train Steps/Sec: 1.90, Samples/Sec: 68.34
+[2025-05-17 20:59:30] (step=0656200) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 21:00:11] (step=0656300) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 21:00:51] (step=0656400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 21:01:31] (step=0656500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:02:11] (step=0656600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 21:02:52] (step=0656700) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 21:03:32] (step=0656800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 21:04:12] (step=0656900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 21:04:53] (step=0657000) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 21:05:33] (step=0657100) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 21:06:13] (step=0657200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:06:54] (step=0657300) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:07:34] (step=0657400) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:08:14] (step=0657500) Train Loss: 0.0617, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 21:08:55] (step=0657600) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 21:09:35] (step=0657700) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 21:10:15] (step=0657800) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 21:10:56] (step=0657900) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 21:11:36] (step=0658000) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 21:11:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 21:12:29] (step=0658100) Train Loss: 0.0695, Train Steps/Sec: 1.91, Samples/Sec: 68.60
+[2025-05-17 21:13:09] (step=0658200) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 21:13:49] (step=0658300) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:14:30] (step=0658400) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 21:15:10] (step=0658500) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 21:15:18] Beginning epoch 163...
+[2025-05-17 21:15:50] (step=0658600) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-17 21:16:31] (step=0658700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:17:11] (step=0658800) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 21:17:51] (step=0658900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:18:32] (step=0659000) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 21:19:12] (step=0659100) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-17 21:19:53] (step=0659200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 21:20:33] (step=0659300) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:21:13] (step=0659400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-17 21:21:54] (step=0659500) Train Loss: 0.0720, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-17 21:22:34] (step=0659600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 21:23:14] (step=0659700) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-17 21:23:55] (step=0659800) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-17 21:24:35] (step=0659900) Train Loss: 0.0764, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-17 21:25:16] (step=0660000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 21:25:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0660000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 21:25:42] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 21:27:42] (step=0660000) Perceptual Loss: 0.3240, Eval Time: 122.32
+[2025-05-17 21:28:23] (step=0660100) Train Loss: 0.0666, Train Steps/Sec: 0.53, Samples/Sec: 19.26
+[2025-05-17 21:29:03] (step=0660200) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:29:43] (step=0660300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 21:30:24] (step=0660400) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-17 21:31:04] (step=0660500) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:31:44] (step=0660600) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 21:32:25] (step=0660700) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 21:33:05] (step=0660800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 21:33:45] (step=0660900) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 21:34:26] (step=0661000) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:35:06] (step=0661100) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 21:35:46] (step=0661200) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:36:27] (step=0661300) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 21:37:07] (step=0661400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 21:37:47] (step=0661500) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:38:28] (step=0661600) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 21:39:08] (step=0661700) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:39:48] (step=0661800) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 21:40:29] (step=0661900) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 21:41:09] (step=0662000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:41:21] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 21:42:02] (step=0662100) Train Loss: 0.0638, Train Steps/Sec: 1.89, Samples/Sec: 67.92
+[2025-05-17 21:42:42] (step=0662200) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 21:43:23] (step=0662300) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-17 21:44:03] (step=0662400) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-17 21:44:43] (step=0662500) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 21:45:08] Beginning epoch 164...
+[2025-05-17 21:45:24] (step=0662600) Train Loss: 0.0652, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-17 21:46:04] (step=0662700) Train Loss: 0.0614, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 21:46:45] (step=0662800) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-17 21:47:25] (step=0662900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 21:48:05] (step=0663000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 21:48:46] (step=0663100) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 21:49:26] (step=0663200) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 21:50:06] (step=0663300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 21:50:47] (step=0663400) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 21:51:27] (step=0663500) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 21:52:08] (step=0663600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 21:52:48] (step=0663700) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:53:28] (step=0663800) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 21:54:09] (step=0663900) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 21:54:49] (step=0664000) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 21:55:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 21:55:42] (step=0664100) Train Loss: 0.0670, Train Steps/Sec: 1.90, Samples/Sec: 68.24
+[2025-05-17 21:56:22] (step=0664200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 21:57:02] (step=0664300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 21:57:42] (step=0664400) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 21:58:23] (step=0664500) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 21:59:03] (step=0664600) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 21:59:43] (step=0664700) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 22:00:24] (step=0664800) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 22:01:04] (step=0664900) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 22:01:44] (step=0665000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 22:02:25] (step=0665100) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 22:03:05] (step=0665200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 22:03:45] (step=0665300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 22:04:26] (step=0665400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 22:05:06] (step=0665500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 22:05:46] (step=0665600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 22:06:27] (step=0665700) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-17 22:07:07] (step=0665800) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 22:07:47] (step=0665900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 22:08:28] (step=0666000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 22:08:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 22:09:20] (step=0666100) Train Loss: 0.0643, Train Steps/Sec: 1.90, Samples/Sec: 68.49
+[2025-05-17 22:10:01] (step=0666200) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-17 22:10:41] (step=0666300) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 22:11:21] (step=0666400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 22:12:02] (step=0666500) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 22:12:42] (step=0666600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.44
+[2025-05-17 22:12:42] Beginning epoch 165...
+[2025-05-17 22:13:22] (step=0666700) Train Loss: 0.0618, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-17 22:14:03] (step=0666800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 22:14:43] (step=0666900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:15:23] (step=0667000) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 22:16:04] (step=0667100) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 22:16:44] (step=0667200) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 22:17:24] (step=0667300) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 22:18:05] (step=0667400) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 22:18:45] (step=0667500) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 22:19:25] (step=0667600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 22:20:06] (step=0667700) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 22:20:46] (step=0667800) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 22:21:26] (step=0667900) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 22:22:07] (step=0668000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 22:22:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 22:22:59] (step=0668100) Train Loss: 0.0652, Train Steps/Sec: 1.91, Samples/Sec: 68.59
+[2025-05-17 22:23:40] (step=0668200) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 22:24:20] (step=0668300) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 22:25:00] (step=0668400) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 22:25:41] (step=0668500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 22:26:21] (step=0668600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 22:27:01] (step=0668700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 22:27:42] (step=0668800) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 22:28:22] (step=0668900) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 22:29:02] (step=0669000) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 22:29:43] (step=0669100) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 22:30:23] (step=0669200) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 22:31:03] (step=0669300) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:31:44] (step=0669400) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 22:32:24] (step=0669500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 22:33:04] (step=0669600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:33:45] (step=0669700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-17 22:34:25] (step=0669800) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 22:35:05] (step=0669900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 22:35:46] (step=0670000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 22:35:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 22:36:01] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 22:38:00] (step=0670000) Perceptual Loss: 0.3168, Eval Time: 122.23
+[2025-05-17 22:38:41] (step=0670100) Train Loss: 0.0664, Train Steps/Sec: 0.57, Samples/Sec: 20.57
+[2025-05-17 22:39:21] (step=0670200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 22:40:01] (step=0670300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 22:40:42] (step=0670400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 22:41:22] (step=0670500) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-17 22:42:02] (step=0670600) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 22:42:18] Beginning epoch 166...
+[2025-05-17 22:42:43] (step=0670700) Train Loss: 0.0644, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-17 22:43:23] (step=0670800) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:44:04] (step=0670900) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 22:44:44] (step=0671000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 22:45:24] (step=0671100) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 22:46:05] (step=0671200) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 22:46:45] (step=0671300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:47:25] (step=0671400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 22:48:06] (step=0671500) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 22:48:46] (step=0671600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 22:49:26] (step=0671700) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:50:06] (step=0671800) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 22:50:47] (step=0671900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 22:51:27] (step=0672000) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 22:51:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 22:52:20] (step=0672100) Train Loss: 0.0714, Train Steps/Sec: 1.90, Samples/Sec: 68.24
+[2025-05-17 22:53:00] (step=0672200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:53:41] (step=0672300) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 22:54:21] (step=0672400) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:55:01] (step=0672500) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 22:55:42] (step=0672600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 22:56:22] (step=0672700) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 22:57:02] (step=0672800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 22:57:42] (step=0672900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 22:58:23] (step=0673000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 22:59:03] (step=0673100) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 22:59:44] (step=0673200) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:00:24] (step=0673300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-17 23:01:04] (step=0673400) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 23:01:44] (step=0673500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 23:02:25] (step=0673600) Train Loss: 0.0741, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-17 23:03:05] (step=0673700) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 23:03:45] (step=0673800) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-17 23:04:26] (step=0673900) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-17 23:05:06] (step=0674000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 23:05:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 23:05:59] (step=0674100) Train Loss: 0.0652, Train Steps/Sec: 1.90, Samples/Sec: 68.22
+[2025-05-17 23:06:39] (step=0674200) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 23:07:19] (step=0674300) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:08:00] (step=0674400) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:08:40] (step=0674500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 23:09:21] (step=0674600) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 23:09:53] Beginning epoch 167...
+[2025-05-17 23:10:01] (step=0674700) Train Loss: 0.0655, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-17 23:10:41] (step=0674800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:11:22] (step=0674900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 23:12:02] (step=0675000) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 23:12:42] (step=0675100) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 23:13:23] (step=0675200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 23:14:03] (step=0675300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:14:43] (step=0675400) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 23:15:24] (step=0675500) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 23:16:04] (step=0675600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-17 23:16:45] (step=0675700) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 23:17:25] (step=0675800) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 23:18:05] (step=0675900) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-17 23:18:46] (step=0676000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 23:18:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 23:19:38] (step=0676100) Train Loss: 0.0669, Train Steps/Sec: 1.90, Samples/Sec: 68.31
+[2025-05-17 23:20:19] (step=0676200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 23:20:59] (step=0676300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 23:21:39] (step=0676400) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 23:22:20] (step=0676500) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 23:23:00] (step=0676600) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 23:23:40] (step=0676700) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 23:24:21] (step=0676800) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 23:25:01] (step=0676900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 23:25:41] (step=0677000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:26:22] (step=0677100) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 23:27:02] (step=0677200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 23:27:42] (step=0677300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-17 23:28:23] (step=0677400) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:29:03] (step=0677500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:29:43] (step=0677600) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 23:30:24] (step=0677700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 23:31:04] (step=0677800) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 23:31:45] (step=0677900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 23:32:25] (step=0678000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:32:37] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-17 23:33:18] (step=0678100) Train Loss: 0.0717, Train Steps/Sec: 1.90, Samples/Sec: 68.25
+[2025-05-17 23:33:58] (step=0678200) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 23:34:38] (step=0678300) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:35:19] (step=0678400) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:35:59] (step=0678500) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-17 23:36:39] (step=0678600) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-17 23:37:20] (step=0678700) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 23:37:28] Beginning epoch 168...
+[2025-05-17 23:38:00] (step=0678800) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-17 23:38:41] (step=0678900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-17 23:39:21] (step=0679000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-17 23:40:01] (step=0679100) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:40:42] (step=0679200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:41:22] (step=0679300) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 23:42:02] (step=0679400) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:42:43] (step=0679500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:43:23] (step=0679600) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:44:03] (step=0679700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:44:44] (step=0679800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-17 23:45:24] (step=0679900) Train Loss: 0.0606, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 23:46:04] (step=0680000) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 23:46:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0680000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-17 23:46:31] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-17 23:48:31] (step=0680000) Perceptual Loss: 0.3052, Eval Time: 122.21
+[2025-05-17 23:49:11] (step=0680100) Train Loss: 0.0616, Train Steps/Sec: 0.54, Samples/Sec: 19.31
+[2025-05-17 23:49:51] (step=0680200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 23:50:32] (step=0680300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-17 23:51:12] (step=0680400) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 23:51:52] (step=0680500) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-17 23:52:33] (step=0680600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-17 23:53:13] (step=0680700) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 23:53:53] (step=0680800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-17 23:54:34] (step=0680900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-17 23:55:14] (step=0681000) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 23:55:54] (step=0681100) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-17 23:56:35] (step=0681200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:57:15] (step=0681300) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-17 23:57:55] (step=0681400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-17 23:58:36] (step=0681500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-17 23:59:16] (step=0681600) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-17 23:59:56] (step=0681700) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 00:00:37] (step=0681800) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 00:01:17] (step=0681900) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 00:01:57] (step=0682000) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 00:02:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 00:02:50] (step=0682100) Train Loss: 0.0637, Train Steps/Sec: 1.89, Samples/Sec: 68.14
+[2025-05-18 00:03:30] (step=0682200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 00:04:11] (step=0682300) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 00:04:51] (step=0682400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 00:05:32] (step=0682500) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 00:06:12] (step=0682600) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-18 00:06:52] (step=0682700) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 00:07:17] Beginning epoch 169...
+[2025-05-18 00:07:33] (step=0682800) Train Loss: 0.0718, Train Steps/Sec: 2.46, Samples/Sec: 88.39
+[2025-05-18 00:08:13] (step=0682900) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-18 00:08:54] (step=0683000) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 00:09:34] (step=0683100) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 00:10:15] (step=0683200) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 00:10:55] (step=0683300) Train Loss: 0.0675, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-18 00:11:36] (step=0683400) Train Loss: 0.0687, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-18 00:12:16] (step=0683500) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 00:12:57] (step=0683600) Train Loss: 0.0646, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-18 00:13:37] (step=0683700) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 00:14:18] (step=0683800) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-18 00:14:58] (step=0683900) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-18 00:15:39] (step=0684000) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-18 00:15:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 00:16:32] (step=0684100) Train Loss: 0.0674, Train Steps/Sec: 1.88, Samples/Sec: 67.52
+[2025-05-18 00:17:12] (step=0684200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 00:17:53] (step=0684300) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 00:18:33] (step=0684400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 00:19:14] (step=0684500) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-18 00:19:54] (step=0684600) Train Loss: 0.0643, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-18 00:20:35] (step=0684700) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-18 00:21:15] (step=0684800) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 00:21:56] (step=0684900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 00:22:36] (step=0685000) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 00:23:17] (step=0685100) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 00:23:57] (step=0685200) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 00:24:37] (step=0685300) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-18 00:25:18] (step=0685400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 00:25:58] (step=0685500) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-18 00:26:39] (step=0685600) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-18 00:27:19] (step=0685700) Train Loss: 0.0660, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-18 00:28:00] (step=0685800) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-18 00:28:40] (step=0685900) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 00:29:21] (step=0686000) Train Loss: 0.0705, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-18 00:29:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 00:30:15] (step=0686100) Train Loss: 0.0661, Train Steps/Sec: 1.84, Samples/Sec: 66.18
+[2025-05-18 00:30:56] (step=0686200) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-18 00:31:36] (step=0686300) Train Loss: 0.0663, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-18 00:32:16] (step=0686400) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 00:32:57] (step=0686500) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 00:33:37] (step=0686600) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-18 00:34:18] (step=0686700) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 00:34:58] (step=0686800) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-18 00:34:58] Beginning epoch 170...
+[2025-05-18 00:35:39] (step=0686900) Train Loss: 0.0692, Train Steps/Sec: 2.46, Samples/Sec: 88.49
+[2025-05-18 00:36:20] (step=0687000) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 00:37:00] (step=0687100) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-18 00:37:41] (step=0687200) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 00:38:21] (step=0687300) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-18 00:39:02] (step=0687400) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 00:39:42] (step=0687500) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-18 00:40:23] (step=0687600) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 00:41:03] (step=0687700) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-18 00:41:44] (step=0687800) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-18 00:42:24] (step=0687900) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 00:43:05] (step=0688000) Train Loss: 0.0604, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-18 00:43:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 00:43:58] (step=0688100) Train Loss: 0.0617, Train Steps/Sec: 1.86, Samples/Sec: 66.90
+[2025-05-18 00:44:39] (step=0688200) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-18 00:45:19] (step=0688300) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 00:46:00] (step=0688400) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-18 00:46:41] (step=0688500) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-18 00:47:21] (step=0688600) Train Loss: 0.0629, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-18 00:48:02] (step=0688700) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-18 00:48:42] (step=0688800) Train Loss: 0.0636, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-18 00:49:23] (step=0688900) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-18 00:50:03] (step=0689000) Train Loss: 0.0717, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-18 00:50:44] (step=0689100) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-18 00:51:24] (step=0689200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 00:52:05] (step=0689300) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-18 00:52:45] (step=0689400) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-18 00:53:26] (step=0689500) Train Loss: 0.0661, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-18 00:54:06] (step=0689600) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-18 00:54:47] (step=0689700) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 00:55:27] (step=0689800) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 00:56:08] (step=0689900) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 00:56:48] (step=0690000) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 00:57:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 00:57:04] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 00:59:03] (step=0690000) Perceptual Loss: 0.3166, Eval Time: 122.24
+[2025-05-18 00:59:44] (step=0690100) Train Loss: 0.0701, Train Steps/Sec: 0.57, Samples/Sec: 20.48
+[2025-05-18 01:00:24] (step=0690200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 01:01:04] (step=0690300) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 01:01:45] (step=0690400) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 01:02:25] (step=0690500) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 01:03:05] (step=0690600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 01:03:46] (step=0690700) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 01:04:26] (step=0690800) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 01:04:42] Beginning epoch 171...
+[2025-05-18 01:05:07] (step=0690900) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-18 01:05:47] (step=0691000) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 01:06:27] (step=0691100) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 01:07:08] (step=0691200) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 01:07:48] (step=0691300) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 01:08:28] (step=0691400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 01:09:09] (step=0691500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 01:09:49] (step=0691600) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 01:10:29] (step=0691700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 01:11:10] (step=0691800) Train Loss: 0.0767, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 01:11:50] (step=0691900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 01:12:30] (step=0692000) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 01:12:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 01:13:24] (step=0692100) Train Loss: 0.0635, Train Steps/Sec: 1.87, Samples/Sec: 67.25
+[2025-05-18 01:14:04] (step=0692200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 01:14:45] (step=0692300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 01:15:25] (step=0692400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 01:16:05] (step=0692500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 01:16:46] (step=0692600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 01:17:26] (step=0692700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 01:18:06] (step=0692800) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 01:18:47] (step=0692900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 01:19:27] (step=0693000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 01:20:07] (step=0693100) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 01:20:48] (step=0693200) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 01:21:28] (step=0693300) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 01:22:08] (step=0693400) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 01:22:49] (step=0693500) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 01:23:29] (step=0693600) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 01:24:09] (step=0693700) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 01:24:50] (step=0693800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 01:25:30] (step=0693900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 01:26:11] (step=0694000) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 01:26:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 01:27:03] (step=0694100) Train Loss: 0.0682, Train Steps/Sec: 1.89, Samples/Sec: 68.08
+[2025-05-18 01:27:44] (step=0694200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 01:28:24] (step=0694300) Train Loss: 0.0739, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-18 01:29:05] (step=0694400) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 01:29:45] (step=0694500) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-18 01:30:25] (step=0694600) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-18 01:31:06] (step=0694700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 01:31:46] (step=0694800) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-18 01:32:19] Beginning epoch 172...
+[2025-05-18 01:32:27] (step=0694900) Train Loss: 0.0683, Train Steps/Sec: 2.45, Samples/Sec: 88.20
+[2025-05-18 01:33:08] (step=0695000) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-18 01:33:48] (step=0695100) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 01:34:29] (step=0695200) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 01:35:09] (step=0695300) Train Loss: 0.0676, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-18 01:35:50] (step=0695400) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-18 01:36:31] (step=0695500) Train Loss: 0.0649, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-18 01:37:11] (step=0695600) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-18 01:37:52] (step=0695700) Train Loss: 0.0645, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-18 01:38:32] (step=0695800) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-18 01:39:13] (step=0695900) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-18 01:39:53] (step=0696000) Train Loss: 0.0723, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 01:40:06] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 01:40:47] (step=0696100) Train Loss: 0.0689, Train Steps/Sec: 1.87, Samples/Sec: 67.20
+[2025-05-18 01:41:27] (step=0696200) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 01:42:07] (step=0696300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 01:42:48] (step=0696400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 01:43:28] (step=0696500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 01:44:08] (step=0696600) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 01:44:49] (step=0696700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 01:45:29] (step=0696800) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 01:46:09] (step=0696900) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 01:46:50] (step=0697000) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 01:47:30] (step=0697100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 01:48:10] (step=0697200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 01:48:51] (step=0697300) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 01:49:31] (step=0697400) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 01:50:12] (step=0697500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 01:50:52] (step=0697600) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 01:51:32] (step=0697700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 01:52:13] (step=0697800) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 01:52:53] (step=0697900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 01:53:33] (step=0698000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 01:53:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 01:54:25] (step=0698100) Train Loss: 0.0703, Train Steps/Sec: 1.92, Samples/Sec: 68.98
+[2025-05-18 01:55:06] (step=0698200) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 01:55:46] (step=0698300) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 01:56:26] (step=0698400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 01:57:07] (step=0698500) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 01:57:47] (step=0698600) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 01:58:27] (step=0698700) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 01:59:08] (step=0698800) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 01:59:48] (step=0698900) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 01:59:56] Beginning epoch 173...
+[2025-05-18 02:00:29] (step=0699000) Train Loss: 0.0622, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-18 02:01:09] (step=0699100) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 02:01:49] (step=0699200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 02:02:30] (step=0699300) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 02:03:10] (step=0699400) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 02:03:51] (step=0699500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 02:04:31] (step=0699600) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 02:05:11] (step=0699700) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 02:05:52] (step=0699800) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-18 02:06:32] (step=0699900) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-18 02:07:13] (step=0700000) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 02:07:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0700000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 02:07:43] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 02:09:43] (step=0700000) Perceptual Loss: 0.2936, Eval Time: 122.70
+[2025-05-18 02:10:23] (step=0700100) Train Loss: 0.0640, Train Steps/Sec: 0.53, Samples/Sec: 18.90
+[2025-05-18 02:11:04] (step=0700200) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 02:11:44] (step=0700300) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 02:12:24] (step=0700400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 02:13:05] (step=0700500) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 02:13:45] (step=0700600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 02:14:25] (step=0700700) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 02:15:06] (step=0700800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 02:15:46] (step=0700900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 02:16:26] (step=0701000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 02:17:07] (step=0701100) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 02:17:47] (step=0701200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 02:18:27] (step=0701300) Train Loss: 0.0736, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 02:19:08] (step=0701400) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-18 02:19:48] (step=0701500) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 02:20:28] (step=0701600) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 02:21:09] (step=0701700) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 02:21:49] (step=0701800) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 02:22:29] (step=0701900) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 02:23:09] (step=0702000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 02:23:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 02:24:03] (step=0702100) Train Loss: 0.0670, Train Steps/Sec: 1.88, Samples/Sec: 67.71
+[2025-05-18 02:24:43] (step=0702200) Train Loss: 0.0595, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 02:25:23] (step=0702300) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 02:26:04] (step=0702400) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 02:26:44] (step=0702500) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 02:27:24] (step=0702600) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 02:28:05] (step=0702700) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 02:28:45] (step=0702800) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-18 02:29:25] (step=0702900) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 02:29:50] Beginning epoch 174...
+[2025-05-18 02:30:06] (step=0703000) Train Loss: 0.0643, Train Steps/Sec: 2.46, Samples/Sec: 88.41
+[2025-05-18 02:30:46] (step=0703100) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:31:27] (step=0703200) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 02:32:07] (step=0703300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:32:48] (step=0703400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 02:33:28] (step=0703500) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 02:34:08] (step=0703600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 02:34:49] (step=0703700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 02:35:29] (step=0703800) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 02:36:09] (step=0703900) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 02:36:50] (step=0704000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:37:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 02:37:43] (step=0704100) Train Loss: 0.0687, Train Steps/Sec: 1.88, Samples/Sec: 67.56
+[2025-05-18 02:38:23] (step=0704200) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 02:39:04] (step=0704300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 02:39:44] (step=0704400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 02:40:24] (step=0704500) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 02:41:05] (step=0704600) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 02:41:45] (step=0704700) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:42:25] (step=0704800) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-18 02:43:06] (step=0704900) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 02:43:46] (step=0705000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 02:44:27] (step=0705100) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 02:45:07] (step=0705200) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 02:45:47] (step=0705300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 02:46:28] (step=0705400) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 02:47:08] (step=0705500) Train Loss: 0.0730, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-18 02:47:48] (step=0705600) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:48:29] (step=0705700) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-18 02:49:09] (step=0705800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 02:49:50] (step=0705900) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:50:30] (step=0706000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:50:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 02:51:23] (step=0706100) Train Loss: 0.0683, Train Steps/Sec: 1.89, Samples/Sec: 68.14
+[2025-05-18 02:52:03] (step=0706200) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:52:43] (step=0706300) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 02:53:24] (step=0706400) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 02:54:04] (step=0706500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 02:54:44] (step=0706600) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:55:25] (step=0706700) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 02:56:05] (step=0706800) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-18 02:56:45] (step=0706900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 02:57:26] (step=0707000) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 02:57:26] Beginning epoch 175...
+[2025-05-18 02:58:06] (step=0707100) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-18 02:58:47] (step=0707200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 02:59:27] (step=0707300) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 03:00:07] (step=0707400) Train Loss: 0.0614, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 03:00:48] (step=0707500) Train Loss: 0.0596, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 03:01:28] (step=0707600) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:02:09] (step=0707700) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 03:02:49] (step=0707800) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:03:29] (step=0707900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:04:10] (step=0708000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 03:04:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 03:05:02] (step=0708100) Train Loss: 0.0622, Train Steps/Sec: 1.90, Samples/Sec: 68.34
+[2025-05-18 03:05:43] (step=0708200) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 03:06:23] (step=0708300) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 03:07:03] (step=0708400) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 03:07:44] (step=0708500) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:08:24] (step=0708600) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 03:09:04] (step=0708700) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:09:45] (step=0708800) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-18 03:10:25] (step=0708900) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:11:05] (step=0709000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 03:11:46] (step=0709100) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 03:12:26] (step=0709200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 03:13:06] (step=0709300) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 03:13:47] (step=0709400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 03:14:27] (step=0709500) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 03:15:07] (step=0709600) Train Loss: 0.0596, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 03:15:48] (step=0709700) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 03:16:28] (step=0709800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 03:17:08] (step=0709900) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:17:49] (step=0710000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 03:18:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 03:18:04] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 03:20:04] (step=0710000) Perceptual Loss: 0.3223, Eval Time: 122.57
+[2025-05-18 03:20:44] (step=0710100) Train Loss: 0.0694, Train Steps/Sec: 0.57, Samples/Sec: 20.50
+[2025-05-18 03:21:25] (step=0710200) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 03:22:05] (step=0710300) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 03:22:45] (step=0710400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 03:23:26] (step=0710500) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 03:24:06] (step=0710600) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 03:24:47] (step=0710700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 03:25:27] (step=0710800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 03:26:07] (step=0710900) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 03:26:48] (step=0711000) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:27:04] Beginning epoch 176...
+[2025-05-18 03:27:28] (step=0711100) Train Loss: 0.0603, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-18 03:28:08] (step=0711200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 03:28:49] (step=0711300) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:29:29] (step=0711400) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 03:30:10] (step=0711500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 03:30:50] (step=0711600) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 03:31:30] (step=0711700) Train Loss: 0.0738, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 03:32:11] (step=0711800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:32:51] (step=0711900) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 03:33:31] (step=0712000) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:33:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 03:34:24] (step=0712100) Train Loss: 0.0638, Train Steps/Sec: 1.90, Samples/Sec: 68.31
+[2025-05-18 03:35:04] (step=0712200) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 03:35:45] (step=0712300) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 03:36:25] (step=0712400) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:37:05] (step=0712500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 03:37:46] (step=0712600) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 03:38:26] (step=0712700) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:39:06] (step=0712800) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-18 03:39:47] (step=0712900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 03:40:27] (step=0713000) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 03:41:07] (step=0713100) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:41:48] (step=0713200) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:42:28] (step=0713300) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 03:43:08] (step=0713400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 03:43:49] (step=0713500) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 03:44:29] (step=0713600) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-18 03:45:10] (step=0713700) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 03:45:50] (step=0713800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:46:30] (step=0713900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 03:47:11] (step=0714000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:47:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 03:48:04] (step=0714100) Train Loss: 0.0671, Train Steps/Sec: 1.88, Samples/Sec: 67.58
+[2025-05-18 03:48:44] (step=0714200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 03:49:25] (step=0714300) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 03:50:05] (step=0714400) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 03:50:45] (step=0714500) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 03:51:26] (step=0714600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 03:52:06] (step=0714700) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 03:52:46] (step=0714800) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 03:53:27] (step=0714900) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 03:54:07] (step=0715000) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 03:54:39] Beginning epoch 177...
+[2025-05-18 03:54:48] (step=0715100) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-18 03:55:28] (step=0715200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 03:56:08] (step=0715300) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 03:56:49] (step=0715400) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 03:57:29] (step=0715500) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 03:58:09] (step=0715600) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 03:58:50] (step=0715700) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 03:59:30] (step=0715800) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 04:00:11] (step=0715900) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 04:00:51] (step=0716000) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 04:01:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 04:01:43] (step=0716100) Train Loss: 0.0729, Train Steps/Sec: 1.91, Samples/Sec: 68.84
+[2025-05-18 04:02:23] (step=0716200) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:03:04] (step=0716300) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 04:03:44] (step=0716400) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:04:25] (step=0716500) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 04:05:05] (step=0716600) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 04:05:45] (step=0716700) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:06:26] (step=0716800) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 04:07:06] (step=0716900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 04:07:46] (step=0717000) Train Loss: 0.0730, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 04:08:27] (step=0717100) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 04:09:07] (step=0717200) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 04:09:47] (step=0717300) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 04:10:28] (step=0717400) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 04:11:08] (step=0717500) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-18 04:11:49] (step=0717600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-18 04:12:29] (step=0717700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 04:13:09] (step=0717800) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 04:13:50] (step=0717900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:14:30] (step=0718000) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:14:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 04:15:23] (step=0718100) Train Loss: 0.0676, Train Steps/Sec: 1.87, Samples/Sec: 67.44
+[2025-05-18 04:16:04] (step=0718200) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 04:16:44] (step=0718300) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 04:17:24] (step=0718400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 04:18:05] (step=0718500) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:18:45] (step=0718600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 04:19:25] (step=0718700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:20:06] (step=0718800) Train Loss: 0.0729, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-18 04:20:46] (step=0718900) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 04:21:27] (step=0719000) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 04:22:07] (step=0719100) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 04:22:15] Beginning epoch 178...
+[2025-05-18 04:22:48] (step=0719200) Train Loss: 0.0653, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-18 04:23:28] (step=0719300) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 04:24:08] (step=0719400) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 04:24:49] (step=0719500) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-18 04:25:29] (step=0719600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 04:26:09] (step=0719700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 04:26:50] (step=0719800) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:27:30] (step=0719900) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 04:28:10] (step=0720000) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 04:28:35] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0720000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 04:28:38] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 04:30:38] (step=0720000) Perceptual Loss: 0.3059, Eval Time: 122.22
+[2025-05-18 04:31:18] (step=0720100) Train Loss: 0.0658, Train Steps/Sec: 0.53, Samples/Sec: 19.17
+[2025-05-18 04:31:59] (step=0720200) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-18 04:32:39] (step=0720300) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 04:33:19] (step=0720400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 04:34:00] (step=0720500) Train Loss: 0.0588, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 04:34:40] (step=0720600) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 04:35:20] (step=0720700) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 04:36:01] (step=0720800) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 04:36:41] (step=0720900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 04:37:21] (step=0721000) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 04:38:02] (step=0721100) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 04:38:42] (step=0721200) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 04:39:22] (step=0721300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 04:40:03] (step=0721400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 04:40:43] (step=0721500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 04:41:23] (step=0721600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-18 04:42:04] (step=0721700) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 04:42:44] (step=0721800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 04:43:24] (step=0721900) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 04:44:05] (step=0722000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 04:44:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 04:44:57] (step=0722100) Train Loss: 0.0666, Train Steps/Sec: 1.89, Samples/Sec: 68.20
+[2025-05-18 04:45:38] (step=0722200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:46:18] (step=0722300) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 04:46:58] (step=0722400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 04:47:39] (step=0722500) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 04:48:19] (step=0722600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 04:48:59] (step=0722700) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 04:49:40] (step=0722800) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 04:50:20] (step=0722900) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 04:51:00] (step=0723000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 04:51:41] (step=0723100) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 04:52:05] Beginning epoch 179...
+[2025-05-18 04:52:21] (step=0723200) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-18 04:53:02] (step=0723300) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 04:53:42] (step=0723400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 04:54:23] (step=0723500) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 04:55:03] (step=0723600) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 04:55:43] (step=0723700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 04:56:24] (step=0723800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 04:57:04] (step=0723900) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 04:57:44] (step=0724000) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 04:57:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 04:58:37] (step=0724100) Train Loss: 0.0684, Train Steps/Sec: 1.89, Samples/Sec: 67.91
+[2025-05-18 04:59:18] (step=0724200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 04:59:58] (step=0724300) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 05:00:38] (step=0724400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 05:01:19] (step=0724500) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 05:01:59] (step=0724600) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:02:39] (step=0724700) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:03:20] (step=0724800) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-18 05:04:00] (step=0724900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 05:04:40] (step=0725000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 05:05:21] (step=0725100) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:06:01] (step=0725200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 05:06:41] (step=0725300) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 05:07:22] (step=0725400) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 05:08:02] (step=0725500) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-18 05:08:42] (step=0725600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 05:09:23] (step=0725700) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 05:10:03] (step=0725800) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 05:10:43] (step=0725900) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 05:11:24] (step=0726000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 05:11:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 05:12:17] (step=0726100) Train Loss: 0.0620, Train Steps/Sec: 1.89, Samples/Sec: 67.87
+[2025-05-18 05:12:57] (step=0726200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:13:38] (step=0726300) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 05:14:18] (step=0726400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:14:58] (step=0726500) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 05:15:39] (step=0726600) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:16:19] (step=0726700) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:16:59] (step=0726800) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-18 05:17:40] (step=0726900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:18:20] (step=0727000) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 05:19:00] (step=0727100) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 05:19:41] (step=0727200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-18 05:19:41] Beginning epoch 180...
+[2025-05-18 05:20:21] (step=0727300) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-18 05:21:02] (step=0727400) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 05:21:42] (step=0727500) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 05:22:22] (step=0727600) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:23:03] (step=0727700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 05:23:43] (step=0727800) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 05:24:24] (step=0727900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 05:25:04] (step=0728000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 05:25:16] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 05:25:56] (step=0728100) Train Loss: 0.0676, Train Steps/Sec: 1.90, Samples/Sec: 68.56
+[2025-05-18 05:26:37] (step=0728200) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 05:27:17] (step=0728300) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 05:27:58] (step=0728400) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 05:28:38] (step=0728500) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:29:18] (step=0728600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 05:29:59] (step=0728700) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 05:30:39] (step=0728800) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 05:31:19] (step=0728900) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 05:32:00] (step=0729000) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 05:32:40] (step=0729100) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 05:33:20] (step=0729200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 05:34:01] (step=0729300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 05:34:41] (step=0729400) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 05:35:21] (step=0729500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 05:36:02] (step=0729600) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 05:36:42] (step=0729700) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 05:37:23] (step=0729800) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:38:03] (step=0729900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:38:43] (step=0730000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 05:38:56] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 05:38:59] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 05:40:58] (step=0730000) Perceptual Loss: 0.2932, Eval Time: 122.45
+[2025-05-18 05:41:39] (step=0730100) Train Loss: 0.0657, Train Steps/Sec: 0.57, Samples/Sec: 20.49
+[2025-05-18 05:42:19] (step=0730200) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-18 05:43:00] (step=0730300) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 05:43:40] (step=0730400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 05:44:20] (step=0730500) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:45:01] (step=0730600) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 05:45:41] (step=0730700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 05:46:21] (step=0730800) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 05:47:02] (step=0730900) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-18 05:47:42] (step=0731000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 05:48:22] (step=0731100) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 05:49:03] (step=0731200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 05:49:19] Beginning epoch 181...
+[2025-05-18 05:49:43] (step=0731300) Train Loss: 0.0684, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-18 05:50:24] (step=0731400) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 05:51:04] (step=0731500) Train Loss: 0.0739, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 05:51:45] (step=0731600) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-18 05:52:25] (step=0731700) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 05:53:05] (step=0731800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 05:53:45] (step=0731900) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 05:54:26] (step=0732000) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 05:54:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 05:55:19] (step=0732100) Train Loss: 0.0656, Train Steps/Sec: 1.89, Samples/Sec: 68.04
+[2025-05-18 05:55:59] (step=0732200) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 05:56:40] (step=0732300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 05:57:20] (step=0732400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 05:58:00] (step=0732500) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 05:58:40] (step=0732600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 05:59:21] (step=0732700) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 06:00:01] (step=0732800) Train Loss: 0.0623, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 06:00:42] (step=0732900) Train Loss: 0.0729, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 06:01:22] (step=0733000) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 06:02:02] (step=0733100) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 06:02:43] (step=0733200) Train Loss: 0.0617, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-18 06:03:23] (step=0733300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 06:04:03] (step=0733400) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 06:04:44] (step=0733500) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 06:05:24] (step=0733600) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-18 06:06:05] (step=0733700) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 06:06:45] (step=0733800) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 06:07:25] (step=0733900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 06:08:06] (step=0734000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 06:08:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 06:08:58] (step=0734100) Train Loss: 0.0676, Train Steps/Sec: 1.90, Samples/Sec: 68.34
+[2025-05-18 06:09:39] (step=0734200) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 06:10:19] (step=0734300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 06:10:59] (step=0734400) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 06:11:40] (step=0734500) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 06:12:20] (step=0734600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:13:00] (step=0734700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 06:13:41] (step=0734800) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 06:14:21] (step=0734900) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 06:15:01] (step=0735000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 06:15:42] (step=0735100) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 06:16:22] (step=0735200) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 06:16:54] Beginning epoch 182...
+[2025-05-18 06:17:03] (step=0735300) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-18 06:17:43] (step=0735400) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:18:23] (step=0735500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 06:19:04] (step=0735600) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 06:19:44] (step=0735700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 06:20:24] (step=0735800) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:21:05] (step=0735900) Train Loss: 0.0749, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 06:21:45] (step=0736000) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 06:21:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 06:22:38] (step=0736100) Train Loss: 0.0618, Train Steps/Sec: 1.90, Samples/Sec: 68.37
+[2025-05-18 06:23:18] (step=0736200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 06:23:58] (step=0736300) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 06:24:39] (step=0736400) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 06:25:19] (step=0736500) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 06:25:59] (step=0736600) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 06:26:40] (step=0736700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 06:27:20] (step=0736800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 06:28:00] (step=0736900) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 06:28:41] (step=0737000) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-18 06:29:21] (step=0737100) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 06:30:01] (step=0737200) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:30:42] (step=0737300) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 06:31:22] (step=0737400) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 06:32:02] (step=0737500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 06:32:43] (step=0737600) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 06:33:23] (step=0737700) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 06:34:04] (step=0737800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 06:34:44] (step=0737900) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 06:35:24] (step=0738000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 06:35:37] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 06:36:17] (step=0738100) Train Loss: 0.0679, Train Steps/Sec: 1.90, Samples/Sec: 68.31
+[2025-05-18 06:36:57] (step=0738200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 06:37:38] (step=0738300) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 06:38:18] (step=0738400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 06:38:58] (step=0738500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 06:39:39] (step=0738600) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 06:40:19] (step=0738700) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:40:59] (step=0738800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 06:41:40] (step=0738900) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:42:20] (step=0739000) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 06:43:00] (step=0739100) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:43:41] (step=0739200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 06:44:21] (step=0739300) Train Loss: 0.0612, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 06:44:29] Beginning epoch 183...
+[2025-05-18 06:45:02] (step=0739400) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-18 06:45:42] (step=0739500) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 06:46:22] (step=0739600) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 06:47:03] (step=0739700) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-18 06:47:43] (step=0739800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 06:48:23] (step=0739900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 06:49:04] (step=0740000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 06:49:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0740000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 06:49:32] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 06:51:31] (step=0740000) Perceptual Loss: 0.2915, Eval Time: 122.51
+[2025-05-18 06:52:12] (step=0740100) Train Loss: 0.0652, Train Steps/Sec: 0.53, Samples/Sec: 19.13
+[2025-05-18 06:52:52] (step=0740200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:53:33] (step=0740300) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-18 06:54:13] (step=0740400) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 06:54:53] (step=0740500) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 06:55:34] (step=0740600) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:56:14] (step=0740700) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 06:56:54] (step=0740800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 06:57:35] (step=0740900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 06:58:15] (step=0741000) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 06:58:55] (step=0741100) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 06:59:36] (step=0741200) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 07:00:16] (step=0741300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 07:00:56] (step=0741400) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 07:01:37] (step=0741500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 07:02:17] (step=0741600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 07:02:57] (step=0741700) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 07:03:38] (step=0741800) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 07:04:18] (step=0741900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 07:04:58] (step=0742000) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 07:05:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 07:05:51] (step=0742100) Train Loss: 0.0599, Train Steps/Sec: 1.89, Samples/Sec: 67.99
+[2025-05-18 07:06:32] (step=0742200) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 07:07:12] (step=0742300) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-18 07:07:52] (step=0742400) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 07:08:33] (step=0742500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 07:09:13] (step=0742600) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 07:09:53] (step=0742700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 07:10:34] (step=0742800) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 07:11:14] (step=0742900) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 07:11:55] (step=0743000) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-18 07:12:35] (step=0743100) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 07:13:15] (step=0743200) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 07:13:55] (step=0743300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 07:14:20] Beginning epoch 184...
+[2025-05-18 07:14:36] (step=0743400) Train Loss: 0.0678, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-18 07:15:16] (step=0743500) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 07:15:57] (step=0743600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 07:16:37] (step=0743700) Train Loss: 0.0743, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-18 07:17:18] (step=0743800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 07:17:58] (step=0743900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 07:18:38] (step=0744000) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:18:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 07:19:32] (step=0744100) Train Loss: 0.0691, Train Steps/Sec: 1.88, Samples/Sec: 67.55
+[2025-05-18 07:20:12] (step=0744200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:20:52] (step=0744300) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-18 07:21:33] (step=0744400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 07:22:13] (step=0744500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:22:53] (step=0744600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:23:34] (step=0744700) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 07:24:14] (step=0744800) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 07:24:54] (step=0744900) Train Loss: 0.0602, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:25:35] (step=0745000) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-18 07:26:15] (step=0745100) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 07:26:56] (step=0745200) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:27:36] (step=0745300) Train Loss: 0.0607, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:28:16] (step=0745400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:28:57] (step=0745500) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 07:29:37] (step=0745600) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 07:30:17] (step=0745700) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-18 07:30:58] (step=0745800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 07:31:38] (step=0745900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 07:32:18] (step=0746000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:32:31] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 07:33:12] (step=0746100) Train Loss: 0.0695, Train Steps/Sec: 1.88, Samples/Sec: 67.63
+[2025-05-18 07:33:52] (step=0746200) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 07:34:32] (step=0746300) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-18 07:35:13] (step=0746400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:35:53] (step=0746500) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 07:36:33] (step=0746600) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 07:37:14] (step=0746700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 07:37:54] (step=0746800) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 07:38:34] (step=0746900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 07:39:15] (step=0747000) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-18 07:39:55] (step=0747100) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:40:36] (step=0747200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 07:41:16] (step=0747300) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 07:41:56] (step=0747400) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 07:41:56] Beginning epoch 185...
+[2025-05-18 07:42:37] (step=0747500) Train Loss: 0.0631, Train Steps/Sec: 2.45, Samples/Sec: 88.32
+[2025-05-18 07:43:17] (step=0747600) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 07:43:58] (step=0747700) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-18 07:44:38] (step=0747800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 07:45:18] (step=0747900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 07:45:59] (step=0748000) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 07:46:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 07:46:52] (step=0748100) Train Loss: 0.0695, Train Steps/Sec: 1.89, Samples/Sec: 67.91
+[2025-05-18 07:47:32] (step=0748200) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 07:48:13] (step=0748300) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 07:48:53] (step=0748400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 07:49:33] (step=0748500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 07:50:14] (step=0748600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:50:54] (step=0748700) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:51:34] (step=0748800) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 07:52:15] (step=0748900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:52:55] (step=0749000) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-18 07:53:36] (step=0749100) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 07:54:16] (step=0749200) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 07:54:56] (step=0749300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:55:37] (step=0749400) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 07:56:17] (step=0749500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 07:56:57] (step=0749600) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 07:57:38] (step=0749700) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-18 07:58:18] (step=0749800) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 07:58:58] (step=0749900) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 07:59:39] (step=0750000) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 07:59:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 07:59:55] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 08:01:54] (step=0750000) Perceptual Loss: 0.3108, Eval Time: 122.54
+[2025-05-18 08:02:35] (step=0750100) Train Loss: 0.0649, Train Steps/Sec: 0.57, Samples/Sec: 20.44
+[2025-05-18 08:03:15] (step=0750200) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 08:03:56] (step=0750300) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-18 08:04:36] (step=0750400) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 08:05:16] (step=0750500) Train Loss: 0.0758, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 08:05:57] (step=0750600) Train Loss: 0.0599, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 08:06:37] (step=0750700) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 08:07:17] (step=0750800) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:07:58] (step=0750900) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:08:38] (step=0751000) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 08:09:19] (step=0751100) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 08:09:59] (step=0751200) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:10:39] (step=0751300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:11:20] (step=0751400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 08:11:36] Beginning epoch 186...
+[2025-05-18 08:12:00] (step=0751500) Train Loss: 0.0639, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-18 08:12:41] (step=0751600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 08:13:21] (step=0751700) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 08:14:01] (step=0751800) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 08:14:42] (step=0751900) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:15:22] (step=0752000) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:15:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 08:16:15] (step=0752100) Train Loss: 0.0666, Train Steps/Sec: 1.89, Samples/Sec: 68.13
+[2025-05-18 08:16:55] (step=0752200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 08:17:35] (step=0752300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 08:18:16] (step=0752400) Train Loss: 0.0738, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 08:18:56] (step=0752500) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 08:19:37] (step=0752600) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 08:20:17] (step=0752700) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 08:20:57] (step=0752800) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-18 08:21:38] (step=0752900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:22:18] (step=0753000) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 08:22:58] (step=0753100) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:23:39] (step=0753200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 08:24:19] (step=0753300) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 08:24:59] (step=0753400) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 08:25:40] (step=0753500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:26:20] (step=0753600) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:27:01] (step=0753700) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 08:27:41] (step=0753800) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 08:28:21] (step=0753900) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:29:02] (step=0754000) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 08:29:14] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 08:29:55] (step=0754100) Train Loss: 0.0680, Train Steps/Sec: 1.87, Samples/Sec: 67.45
+[2025-05-18 08:30:35] (step=0754200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:31:16] (step=0754300) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 08:31:56] (step=0754400) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 08:32:36] (step=0754500) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 08:33:17] (step=0754600) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:33:57] (step=0754700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:34:37] (step=0754800) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 08:35:18] (step=0754900) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 08:35:58] (step=0755000) Train Loss: 0.0744, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 08:36:39] (step=0755100) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 08:37:19] (step=0755200) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:37:59] (step=0755300) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 08:38:39] (step=0755400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 08:39:12] Beginning epoch 187...
+[2025-05-18 08:39:20] (step=0755500) Train Loss: 0.0645, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-18 08:40:00] (step=0755600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 08:40:41] (step=0755700) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 08:41:21] (step=0755800) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 08:42:01] (step=0755900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 08:42:42] (step=0756000) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 08:42:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 08:43:35] (step=0756100) Train Loss: 0.0671, Train Steps/Sec: 1.88, Samples/Sec: 67.74
+[2025-05-18 08:44:15] (step=0756200) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:44:56] (step=0756300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:45:36] (step=0756400) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 08:46:16] (step=0756500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:46:57] (step=0756600) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:47:37] (step=0756700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 08:48:17] (step=0756800) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 08:48:58] (step=0756900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:49:38] (step=0757000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 08:50:19] (step=0757100) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 08:50:59] (step=0757200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:51:39] (step=0757300) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 08:52:19] (step=0757400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 08:53:00] (step=0757500) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 08:53:40] (step=0757600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 08:54:21] (step=0757700) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 08:55:01] (step=0757800) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 08:55:41] (step=0757900) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 08:56:22] (step=0758000) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 08:56:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 08:57:14] (step=0758100) Train Loss: 0.0630, Train Steps/Sec: 1.90, Samples/Sec: 68.35
+[2025-05-18 08:57:55] (step=0758200) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 08:58:35] (step=0758300) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 08:59:15] (step=0758400) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 08:59:56] (step=0758500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:00:36] (step=0758600) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 09:01:16] (step=0758700) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:01:57] (step=0758800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 09:02:37] (step=0758900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 09:03:17] (step=0759000) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 09:03:58] (step=0759100) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-18 09:04:38] (step=0759200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:05:18] (step=0759300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:05:59] (step=0759400) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:06:39] (step=0759500) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 09:06:47] Beginning epoch 188...
+[2025-05-18 09:07:20] (step=0759600) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-18 09:08:00] (step=0759700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 09:08:41] (step=0759800) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 09:09:21] (step=0759900) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:10:01] (step=0760000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 09:10:26] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0760000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 09:10:29] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 09:12:29] (step=0760000) Perceptual Loss: 0.3115, Eval Time: 122.44
+[2025-05-18 09:13:09] (step=0760100) Train Loss: 0.0667, Train Steps/Sec: 0.53, Samples/Sec: 19.14
+[2025-05-18 09:13:50] (step=0760200) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 09:14:30] (step=0760300) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 09:15:10] (step=0760400) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-18 09:15:51] (step=0760500) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 09:16:31] (step=0760600) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 09:17:11] (step=0760700) Train Loss: 0.0773, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 09:17:52] (step=0760800) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 09:18:32] (step=0760900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 09:19:13] (step=0761000) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 09:19:53] (step=0761100) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 09:20:33] (step=0761200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 09:21:14] (step=0761300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 09:21:54] (step=0761400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 09:22:34] (step=0761500) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 09:23:15] (step=0761600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 09:23:55] (step=0761700) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 09:24:35] (step=0761800) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 09:25:16] (step=0761900) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 09:25:56] (step=0762000) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 09:26:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 09:26:49] (step=0762100) Train Loss: 0.0698, Train Steps/Sec: 1.88, Samples/Sec: 67.85
+[2025-05-18 09:27:30] (step=0762200) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-18 09:28:10] (step=0762300) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-18 09:28:50] (step=0762400) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 09:29:31] (step=0762500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 09:30:11] (step=0762600) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 09:30:51] (step=0762700) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 09:31:32] (step=0762800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 09:32:12] (step=0762900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 09:32:52] (step=0763000) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 09:33:33] (step=0763100) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 09:34:13] (step=0763200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:34:53] (step=0763300) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 09:35:34] (step=0763400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 09:36:14] (step=0763500) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:36:38] Beginning epoch 189...
+[2025-05-18 09:36:55] (step=0763600) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-18 09:37:35] (step=0763700) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 09:38:16] (step=0763800) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 09:38:56] (step=0763900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:39:36] (step=0764000) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 09:39:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 09:40:29] (step=0764100) Train Loss: 0.0655, Train Steps/Sec: 1.90, Samples/Sec: 68.31
+[2025-05-18 09:41:09] (step=0764200) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:41:50] (step=0764300) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 09:42:30] (step=0764400) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 09:43:10] (step=0764500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:43:51] (step=0764600) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:44:31] (step=0764700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:45:11] (step=0764800) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 09:45:52] (step=0764900) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 09:46:32] (step=0765000) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 09:47:12] (step=0765100) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 09:47:53] (step=0765200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:48:33] (step=0765300) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 09:49:13] (step=0765400) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:49:54] (step=0765500) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 09:50:34] (step=0765600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 09:51:14] (step=0765700) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 09:51:55] (step=0765800) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 09:52:35] (step=0765900) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:53:15] (step=0766000) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:53:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 09:54:08] (step=0766100) Train Loss: 0.0676, Train Steps/Sec: 1.91, Samples/Sec: 68.71
+[2025-05-18 09:54:48] (step=0766200) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:55:28] (step=0766300) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:56:09] (step=0766400) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 09:56:49] (step=0766500) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 09:57:30] (step=0766600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 09:58:10] (step=0766700) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 09:58:50] (step=0766800) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 09:59:31] (step=0766900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 10:00:11] (step=0767000) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 10:00:51] (step=0767100) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 10:01:32] (step=0767200) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 10:02:12] (step=0767300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 10:02:52] (step=0767400) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 10:03:33] (step=0767500) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 10:04:13] (step=0767600) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-18 10:04:13] Beginning epoch 190...
+[2025-05-18 10:04:54] (step=0767700) Train Loss: 0.0700, Train Steps/Sec: 2.46, Samples/Sec: 88.58
+[2025-05-18 10:05:34] (step=0767800) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 10:06:14] (step=0767900) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 10:06:55] (step=0768000) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 10:07:07] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 10:07:47] (step=0768100) Train Loss: 0.0697, Train Steps/Sec: 1.92, Samples/Sec: 68.98
+[2025-05-18 10:08:27] (step=0768200) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 10:09:08] (step=0768300) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 10:09:48] (step=0768400) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 10:10:28] (step=0768500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 10:11:09] (step=0768600) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 10:11:49] (step=0768700) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 10:12:29] (step=0768800) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 10:13:10] (step=0768900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 10:13:50] (step=0769000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 10:14:31] (step=0769100) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-18 10:15:11] (step=0769200) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 10:15:51] (step=0769300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 10:16:32] (step=0769400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 10:17:12] (step=0769500) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 10:17:52] (step=0769600) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 10:18:33] (step=0769700) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 10:19:13] (step=0769800) Train Loss: 0.0612, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 10:19:53] (step=0769900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 10:20:34] (step=0770000) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 10:20:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 10:20:50] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 10:22:49] (step=0770000) Perceptual Loss: 0.3125, Eval Time: 122.37
+[2025-05-18 10:23:30] (step=0770100) Train Loss: 0.0693, Train Steps/Sec: 0.57, Samples/Sec: 20.46
+[2025-05-18 10:24:10] (step=0770200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 10:24:50] (step=0770300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 10:25:31] (step=0770400) Train Loss: 0.0719, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 10:26:11] (step=0770500) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 10:26:51] (step=0770600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 10:27:32] (step=0770700) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-18 10:28:12] (step=0770800) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 10:28:52] (step=0770900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 10:29:33] (step=0771000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 10:30:13] (step=0771100) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 10:30:53] (step=0771200) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 10:31:34] (step=0771300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 10:32:14] (step=0771400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 10:32:54] (step=0771500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 10:33:35] (step=0771600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 10:33:51] Beginning epoch 191...
+[2025-05-18 10:34:15] (step=0771700) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-18 10:34:56] (step=0771800) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 10:35:36] (step=0771900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 10:36:16] (step=0772000) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 10:36:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 10:37:09] (step=0772100) Train Loss: 0.0681, Train Steps/Sec: 1.89, Samples/Sec: 68.00
+[2025-05-18 10:37:49] (step=0772200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 10:38:30] (step=0772300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 10:39:10] (step=0772400) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 10:39:50] (step=0772500) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 10:40:31] (step=0772600) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 10:41:11] (step=0772700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 10:41:52] (step=0772800) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 10:42:32] (step=0772900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 10:43:12] (step=0773000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 10:43:52] (step=0773100) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 10:44:33] (step=0773200) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 10:45:13] (step=0773300) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 10:45:54] (step=0773400) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 10:46:34] (step=0773500) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 10:47:14] (step=0773600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 10:47:55] (step=0773700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 10:48:35] (step=0773800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 10:49:15] (step=0773900) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 10:49:56] (step=0774000) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 10:50:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 10:50:48] (step=0774100) Train Loss: 0.0699, Train Steps/Sec: 1.89, Samples/Sec: 67.97
+[2025-05-18 10:51:29] (step=0774200) Train Loss: 0.0607, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 10:52:09] (step=0774300) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 10:52:49] (step=0774400) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 10:53:30] (step=0774500) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 10:54:10] (step=0774600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 10:54:50] (step=0774700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 10:55:31] (step=0774800) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 10:56:11] (step=0774900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 10:56:52] (step=0775000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 10:57:32] (step=0775100) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 10:58:12] (step=0775200) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 10:58:53] (step=0775300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 10:59:33] (step=0775400) Train Loss: 0.0742, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:00:13] (step=0775500) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 11:00:54] (step=0775600) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 11:01:26] Beginning epoch 192...
+[2025-05-18 11:01:34] (step=0775700) Train Loss: 0.0638, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-18 11:02:14] (step=0775800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 11:02:55] (step=0775900) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 11:03:35] (step=0776000) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 11:03:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 11:04:28] (step=0776100) Train Loss: 0.0699, Train Steps/Sec: 1.91, Samples/Sec: 68.69
+[2025-05-18 11:05:08] (step=0776200) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 11:05:48] (step=0776300) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 11:06:29] (step=0776400) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 11:07:09] (step=0776500) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 11:07:49] (step=0776600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 11:08:30] (step=0776700) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:09:10] (step=0776800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 11:09:50] (step=0776900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 11:10:31] (step=0777000) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 11:11:11] (step=0777100) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 11:11:51] (step=0777200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 11:12:32] (step=0777300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 11:13:12] (step=0777400) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 11:13:52] (step=0777500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 11:14:33] (step=0777600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 11:15:13] (step=0777700) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 11:15:53] (step=0777800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 11:16:34] (step=0777900) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 11:17:14] (step=0778000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 11:17:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 11:18:07] (step=0778100) Train Loss: 0.0668, Train Steps/Sec: 1.90, Samples/Sec: 68.26
+[2025-05-18 11:18:47] (step=0778200) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 11:19:28] (step=0778300) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 11:20:08] (step=0778400) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 11:20:48] (step=0778500) Train Loss: 0.0719, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 11:21:29] (step=0778600) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 11:22:09] (step=0778700) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 11:22:49] (step=0778800) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 11:23:30] (step=0778900) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 11:24:10] (step=0779000) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 11:24:50] (step=0779100) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 11:25:31] (step=0779200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 11:26:11] (step=0779300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 11:26:51] (step=0779400) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 11:27:32] (step=0779500) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 11:28:12] (step=0779600) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 11:28:52] (step=0779700) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 11:29:00] Beginning epoch 193...
+[2025-05-18 11:29:33] (step=0779800) Train Loss: 0.0649, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-18 11:30:13] (step=0779900) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 11:30:54] (step=0780000) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 11:31:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0780000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 11:31:22] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 11:33:22] (step=0780000) Perceptual Loss: 0.2828, Eval Time: 122.65
+[2025-05-18 11:34:02] (step=0780100) Train Loss: 0.0591, Train Steps/Sec: 0.53, Samples/Sec: 19.11
+[2025-05-18 11:34:42] (step=0780200) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 11:35:23] (step=0780300) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 11:36:03] (step=0780400) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:36:44] (step=0780500) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 11:37:24] (step=0780600) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 11:38:04] (step=0780700) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 11:38:45] (step=0780800) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 11:39:25] (step=0780900) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:40:05] (step=0781000) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 11:40:46] (step=0781100) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:41:26] (step=0781200) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 11:42:06] (step=0781300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 11:42:47] (step=0781400) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 11:43:27] (step=0781500) Train Loss: 0.0746, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 11:44:07] (step=0781600) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 11:44:48] (step=0781700) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 11:45:28] (step=0781800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 11:46:08] (step=0781900) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 11:46:49] (step=0782000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 11:47:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 11:47:42] (step=0782100) Train Loss: 0.0650, Train Steps/Sec: 1.88, Samples/Sec: 67.67
+[2025-05-18 11:48:22] (step=0782200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 11:49:03] (step=0782300) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 11:49:43] (step=0782400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:50:23] (step=0782500) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 11:51:04] (step=0782600) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:51:44] (step=0782700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 11:52:24] (step=0782800) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 11:53:05] (step=0782900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:53:45] (step=0783000) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 11:54:25] (step=0783100) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 11:55:06] (step=0783200) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 11:55:46] (step=0783300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 11:56:27] (step=0783400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 11:57:07] (step=0783500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 11:57:47] (step=0783600) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 11:58:28] (step=0783700) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 11:58:52] Beginning epoch 194...
+[2025-05-18 11:59:08] (step=0783800) Train Loss: 0.0643, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-18 11:59:49] (step=0783900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 12:00:29] (step=0784000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 12:00:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 12:01:22] (step=0784100) Train Loss: 0.0706, Train Steps/Sec: 1.90, Samples/Sec: 68.31
+[2025-05-18 12:02:02] (step=0784200) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:02:42] (step=0784300) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 12:03:23] (step=0784400) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 12:04:03] (step=0784500) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 12:04:43] (step=0784600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 12:05:24] (step=0784700) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 12:06:04] (step=0784800) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 12:06:44] (step=0784900) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 12:07:25] (step=0785000) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 12:08:05] (step=0785100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:08:45] (step=0785200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 12:09:26] (step=0785300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 12:10:06] (step=0785400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 12:10:46] (step=0785500) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 12:11:27] (step=0785600) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:12:07] (step=0785700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 12:12:48] (step=0785800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:13:28] (step=0785900) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 12:14:08] (step=0786000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 12:14:21] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 12:15:01] (step=0786100) Train Loss: 0.0649, Train Steps/Sec: 1.89, Samples/Sec: 68.00
+[2025-05-18 12:15:41] (step=0786200) Train Loss: 0.0736, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 12:16:22] (step=0786300) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:17:02] (step=0786400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 12:17:43] (step=0786500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 12:18:23] (step=0786600) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 12:19:03] (step=0786700) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:19:44] (step=0786800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 12:20:24] (step=0786900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 12:21:04] (step=0787000) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 12:21:45] (step=0787100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 12:22:25] (step=0787200) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 12:23:05] (step=0787300) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:23:46] (step=0787400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 12:24:26] (step=0787500) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 12:25:06] (step=0787600) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:25:47] (step=0787700) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 12:26:27] (step=0787800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 12:26:27] Beginning epoch 195...
+[2025-05-18 12:27:08] (step=0787900) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.47
+[2025-05-18 12:27:48] (step=0788000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 12:28:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 12:28:41] (step=0788100) Train Loss: 0.0698, Train Steps/Sec: 1.88, Samples/Sec: 67.72
+[2025-05-18 12:29:22] (step=0788200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 12:30:02] (step=0788300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:30:42] (step=0788400) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 12:31:23] (step=0788500) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 12:32:03] (step=0788600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 12:32:43] (step=0788700) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 12:33:24] (step=0788800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 12:34:04] (step=0788900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 12:34:44] (step=0789000) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 12:35:25] (step=0789100) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 12:36:05] (step=0789200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 12:36:45] (step=0789300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 12:37:26] (step=0789400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 12:38:06] (step=0789500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 12:38:46] (step=0789600) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 12:39:27] (step=0789700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 12:40:07] (step=0789800) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 12:40:48] (step=0789900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 12:41:28] (step=0790000) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 12:41:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 12:41:44] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 12:43:43] (step=0790000) Perceptual Loss: 0.2996, Eval Time: 122.48
+[2025-05-18 12:44:24] (step=0790100) Train Loss: 0.0657, Train Steps/Sec: 0.57, Samples/Sec: 20.46
+[2025-05-18 12:45:04] (step=0790200) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:45:44] (step=0790300) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 12:46:25] (step=0790400) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 12:47:05] (step=0790500) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 12:47:46] (step=0790600) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 12:48:26] (step=0790700) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 12:49:06] (step=0790800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 12:49:47] (step=0790900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 12:50:27] (step=0791000) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 12:51:07] (step=0791100) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 12:51:48] (step=0791200) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 12:52:28] (step=0791300) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 12:53:08] (step=0791400) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 12:53:49] (step=0791500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 12:54:29] (step=0791600) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 12:55:09] (step=0791700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 12:55:50] (step=0791800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 12:56:06] Beginning epoch 196...
+[2025-05-18 12:56:30] (step=0791900) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-18 12:57:11] (step=0792000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 12:57:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 12:58:03] (step=0792100) Train Loss: 0.0624, Train Steps/Sec: 1.89, Samples/Sec: 68.17
+[2025-05-18 12:58:44] (step=0792200) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 12:59:24] (step=0792300) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 13:00:04] (step=0792400) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 13:00:45] (step=0792500) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 13:01:25] (step=0792600) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 13:02:05] (step=0792700) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 13:02:46] (step=0792800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 13:03:26] (step=0792900) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 13:04:06] (step=0793000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 13:04:47] (step=0793100) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 13:05:27] (step=0793200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:06:07] (step=0793300) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 13:06:48] (step=0793400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 13:07:28] (step=0793500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 13:08:08] (step=0793600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:08:49] (step=0793700) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 13:09:29] (step=0793800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 13:10:09] (step=0793900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 13:10:50] (step=0794000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 13:11:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 13:11:43] (step=0794100) Train Loss: 0.0694, Train Steps/Sec: 1.89, Samples/Sec: 68.19
+[2025-05-18 13:12:23] (step=0794200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:13:03] (step=0794300) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 13:13:44] (step=0794400) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 13:14:24] (step=0794500) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 13:15:04] (step=0794600) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 13:15:45] (step=0794700) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:16:25] (step=0794800) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 13:17:05] (step=0794900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:17:46] (step=0795000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 13:18:26] (step=0795100) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:19:06] (step=0795200) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:19:47] (step=0795300) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 13:20:27] (step=0795400) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:21:07] (step=0795500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 13:21:48] (step=0795600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 13:22:28] (step=0795700) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 13:23:08] (step=0795800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 13:23:41] Beginning epoch 197...
+[2025-05-18 13:23:49] (step=0795900) Train Loss: 0.0709, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-18 13:24:29] (step=0796000) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 13:24:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 13:25:23] (step=0796100) Train Loss: 0.0641, Train Steps/Sec: 1.88, Samples/Sec: 67.59
+[2025-05-18 13:26:03] (step=0796200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 13:26:43] (step=0796300) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 13:27:24] (step=0796400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 13:28:04] (step=0796500) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 13:28:45] (step=0796600) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 13:29:25] (step=0796700) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 13:30:05] (step=0796800) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 13:30:46] (step=0796900) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 13:31:26] (step=0797000) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 13:32:06] (step=0797100) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 13:32:47] (step=0797200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 13:33:27] (step=0797300) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 13:34:07] (step=0797400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 13:34:48] (step=0797500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 13:35:28] (step=0797600) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 13:36:08] (step=0797700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 13:36:49] (step=0797800) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 13:37:29] (step=0797900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 13:38:09] (step=0798000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 13:38:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 13:39:03] (step=0798100) Train Loss: 0.0636, Train Steps/Sec: 1.88, Samples/Sec: 67.68
+[2025-05-18 13:39:43] (step=0798200) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 13:40:23] (step=0798300) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 13:41:04] (step=0798400) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 13:41:44] (step=0798500) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 13:42:24] (step=0798600) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 13:43:05] (step=0798700) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 13:43:45] (step=0798800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 13:44:25] (step=0798900) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 13:45:06] (step=0799000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 13:45:46] (step=0799100) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 13:46:27] (step=0799200) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 13:47:07] (step=0799300) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 13:47:47] (step=0799400) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 13:48:28] (step=0799500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 13:49:08] (step=0799600) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 13:49:48] (step=0799700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 13:50:29] (step=0799800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 13:51:09] (step=0799900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 13:51:17] Beginning epoch 198...
+[2025-05-18 13:51:50] (step=0800000) Train Loss: 0.0647, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 13:52:15] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0800000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 13:52:18] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 13:54:17] (step=0800000) Perceptual Loss: 0.3139, Eval Time: 122.33
+[2025-05-18 13:54:58] (step=0800100) Train Loss: 0.0678, Train Steps/Sec: 0.53, Samples/Sec: 19.15
+[2025-05-18 13:55:38] (step=0800200) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 13:56:18] (step=0800300) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 13:56:59] (step=0800400) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 13:57:39] (step=0800500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 13:58:19] (step=0800600) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 13:59:00] (step=0800700) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 13:59:40] (step=0800800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:00:20] (step=0800900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:01:01] (step=0801000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 14:01:41] (step=0801100) Train Loss: 0.0602, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:02:21] (step=0801200) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 14:03:02] (step=0801300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 14:03:42] (step=0801400) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 14:04:22] (step=0801500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:05:03] (step=0801600) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 14:05:43] (step=0801700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:06:23] (step=0801800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:07:04] (step=0801900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:07:44] (step=0802000) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 14:07:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 14:08:37] (step=0802100) Train Loss: 0.0638, Train Steps/Sec: 1.89, Samples/Sec: 68.10
+[2025-05-18 14:09:17] (step=0802200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 14:09:58] (step=0802300) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 14:10:38] (step=0802400) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 14:11:18] (step=0802500) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 14:11:59] (step=0802600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 14:12:39] (step=0802700) Train Loss: 0.0619, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 14:13:20] (step=0802800) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 14:14:00] (step=0802900) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:14:40] (step=0803000) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 14:15:21] (step=0803100) Train Loss: 0.0615, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:16:01] (step=0803200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:16:41] (step=0803300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 14:17:22] (step=0803400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 14:18:02] (step=0803500) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:18:42] (step=0803600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:19:23] (step=0803700) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 14:20:03] (step=0803800) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:20:43] (step=0803900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:21:07] Beginning epoch 199...
+[2025-05-18 14:21:24] (step=0804000) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-18 14:21:37] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 14:22:17] (step=0804100) Train Loss: 0.0653, Train Steps/Sec: 1.89, Samples/Sec: 67.94
+[2025-05-18 14:22:57] (step=0804200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:23:38] (step=0804300) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 14:24:18] (step=0804400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 14:24:58] (step=0804500) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 14:25:39] (step=0804600) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 14:26:19] (step=0804700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 14:26:59] (step=0804800) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 14:27:40] (step=0804900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:28:20] (step=0805000) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 14:29:00] (step=0805100) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:29:41] (step=0805200) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 14:30:21] (step=0805300) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:31:01] (step=0805400) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 14:31:42] (step=0805500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 14:32:22] (step=0805600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 14:33:02] (step=0805700) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 14:33:43] (step=0805800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:34:23] (step=0805900) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 14:35:03] (step=0806000) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:35:16] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 14:35:57] (step=0806100) Train Loss: 0.0653, Train Steps/Sec: 1.88, Samples/Sec: 67.69
+[2025-05-18 14:36:37] (step=0806200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 14:37:17] (step=0806300) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:37:58] (step=0806400) Train Loss: 0.0602, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 14:38:38] (step=0806500) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:39:18] (step=0806600) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:39:59] (step=0806700) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:40:39] (step=0806800) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-18 14:41:19] (step=0806900) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:42:00] (step=0807000) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 14:42:40] (step=0807100) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:43:20] (step=0807200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 14:44:01] (step=0807300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 14:44:41] (step=0807400) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:45:21] (step=0807500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 14:46:02] (step=0807600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 14:46:42] (step=0807700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 14:47:22] (step=0807800) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:48:03] (step=0807900) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:48:43] (step=0808000) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.41
+[2025-05-18 14:48:55] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 14:48:55] Beginning epoch 200...
+[2025-05-18 14:49:36] (step=0808100) Train Loss: 0.0700, Train Steps/Sec: 1.88, Samples/Sec: 67.66
+[2025-05-18 14:50:17] (step=0808200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 14:50:57] (step=0808300) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 14:51:37] (step=0808400) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 14:52:18] (step=0808500) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:52:58] (step=0808600) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 14:53:38] (step=0808700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:54:19] (step=0808800) Train Loss: 0.0623, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 14:54:59] (step=0808900) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 14:55:39] (step=0809000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 14:56:20] (step=0809100) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 14:57:00] (step=0809200) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 14:57:40] (step=0809300) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 14:58:21] (step=0809400) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 14:59:01] (step=0809500) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-18 14:59:41] (step=0809600) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 15:00:22] (step=0809700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 15:01:02] (step=0809800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 15:01:42] (step=0809900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 15:02:23] (step=0810000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 15:02:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 15:02:39] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 15:04:38] (step=0810000) Perceptual Loss: 0.3181, Eval Time: 122.38
+[2025-05-18 15:05:19] (step=0810100) Train Loss: 0.0699, Train Steps/Sec: 0.57, Samples/Sec: 20.44
+[2025-05-18 15:05:59] (step=0810200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 15:06:40] (step=0810300) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 15:07:20] (step=0810400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 15:08:00] (step=0810500) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 15:08:41] (step=0810600) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 15:09:21] (step=0810700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 15:10:01] (step=0810800) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 15:10:42] (step=0810900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 15:11:22] (step=0811000) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 15:12:02] (step=0811100) Train Loss: 0.0598, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 15:12:43] (step=0811200) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:13:23] (step=0811300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 15:14:03] (step=0811400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 15:14:44] (step=0811500) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 15:15:24] (step=0811600) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 15:16:05] (step=0811700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 15:16:45] (step=0811800) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:17:25] (step=0811900) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 15:18:05] (step=0812000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 15:18:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 15:18:34] Beginning epoch 201...
+[2025-05-18 15:18:59] (step=0812100) Train Loss: 0.0653, Train Steps/Sec: 1.87, Samples/Sec: 67.22
+[2025-05-18 15:19:39] (step=0812200) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:20:20] (step=0812300) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 15:21:00] (step=0812400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 15:21:40] (step=0812500) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 15:22:21] (step=0812600) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 15:23:01] (step=0812700) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 15:23:42] (step=0812800) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-18 15:24:22] (step=0812900) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 15:25:02] (step=0813000) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 15:25:43] (step=0813100) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 15:26:23] (step=0813200) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-18 15:27:03] (step=0813300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 15:27:44] (step=0813400) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 15:28:24] (step=0813500) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 15:29:05] (step=0813600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 15:29:45] (step=0813700) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 15:30:25] (step=0813800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-18 15:31:06] (step=0813900) Train Loss: 0.0602, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 15:31:46] (step=0814000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 15:31:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 15:32:39] (step=0814100) Train Loss: 0.0649, Train Steps/Sec: 1.89, Samples/Sec: 68.07
+[2025-05-18 15:33:19] (step=0814200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 15:34:00] (step=0814300) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 15:34:40] (step=0814400) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:35:20] (step=0814500) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:36:01] (step=0814600) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 15:36:41] (step=0814700) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 15:37:21] (step=0814800) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-18 15:38:02] (step=0814900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 15:38:42] (step=0815000) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 15:39:22] (step=0815100) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:40:03] (step=0815200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 15:40:43] (step=0815300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 15:41:23] (step=0815400) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 15:42:04] (step=0815500) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-18 15:42:44] (step=0815600) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 15:43:24] (step=0815700) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 15:44:05] (step=0815800) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 15:44:45] (step=0815900) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 15:45:25] (step=0816000) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 15:45:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 15:46:11] Beginning epoch 202...
+[2025-05-18 15:46:19] (step=0816100) Train Loss: 0.0684, Train Steps/Sec: 1.87, Samples/Sec: 67.25
+[2025-05-18 15:46:59] (step=0816200) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-18 15:47:40] (step=0816300) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 15:48:20] (step=0816400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 15:49:00] (step=0816500) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 15:49:41] (step=0816600) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 15:50:21] (step=0816700) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 15:51:01] (step=0816800) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 15:51:42] (step=0816900) Train Loss: 0.0616, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 15:52:22] (step=0817000) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 15:53:03] (step=0817100) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 15:53:43] (step=0817200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:54:23] (step=0817300) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:55:04] (step=0817400) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:55:44] (step=0817500) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 15:56:24] (step=0817600) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 15:57:05] (step=0817700) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 15:57:45] (step=0817800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 15:58:25] (step=0817900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 15:59:06] (step=0818000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 15:59:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 15:59:58] (step=0818100) Train Loss: 0.0613, Train Steps/Sec: 1.90, Samples/Sec: 68.23
+[2025-05-18 16:00:39] (step=0818200) Train Loss: 0.0686, Train Steps/Sec: 2.46, Samples/Sec: 88.41
+[2025-05-18 16:01:20] (step=0818300) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-18 16:02:00] (step=0818400) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 16:02:40] (step=0818500) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 16:03:21] (step=0818600) Train Loss: 0.0601, Train Steps/Sec: 2.45, Samples/Sec: 88.37
+[2025-05-18 16:04:02] (step=0818700) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 16:04:42] (step=0818800) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-18 16:05:22] (step=0818900) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 16:06:03] (step=0819000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 16:06:43] (step=0819100) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 16:07:24] (step=0819200) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 16:08:04] (step=0819300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 16:08:44] (step=0819400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 16:09:25] (step=0819500) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 16:10:05] (step=0819600) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 16:10:45] (step=0819700) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 16:11:26] (step=0819800) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 16:12:06] (step=0819900) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:12:46] (step=0820000) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 16:13:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0820000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 16:13:13] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 16:15:12] (step=0820000) Perceptual Loss: 0.3195, Eval Time: 122.28
+[2025-05-18 16:15:53] (step=0820100) Train Loss: 0.0661, Train Steps/Sec: 0.54, Samples/Sec: 19.27
+[2025-05-18 16:16:01] Beginning epoch 203...
+[2025-05-18 16:16:34] (step=0820200) Train Loss: 0.0670, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-18 16:17:14] (step=0820300) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 16:17:54] (step=0820400) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 16:18:35] (step=0820500) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 16:19:15] (step=0820600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 16:19:55] (step=0820700) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 16:20:36] (step=0820800) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-18 16:21:16] (step=0820900) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 16:21:57] (step=0821000) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-18 16:22:37] (step=0821100) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.51
+[2025-05-18 16:23:18] (step=0821200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 16:23:58] (step=0821300) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 16:24:38] (step=0821400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 16:25:19] (step=0821500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 16:25:59] (step=0821600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 16:26:39] (step=0821700) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 16:27:20] (step=0821800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 16:28:00] (step=0821900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:28:40] (step=0822000) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 16:28:53] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 16:29:34] (step=0822100) Train Loss: 0.0667, Train Steps/Sec: 1.88, Samples/Sec: 67.51
+[2025-05-18 16:30:14] (step=0822200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:30:54] (step=0822300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 16:31:35] (step=0822400) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:32:15] (step=0822500) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 16:32:55] (step=0822600) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:33:36] (step=0822700) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:34:16] (step=0822800) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 16:34:56] (step=0822900) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:35:37] (step=0823000) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 16:36:17] (step=0823100) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:36:57] (step=0823200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:37:38] (step=0823300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:38:18] (step=0823400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 16:38:58] (step=0823500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 16:39:39] (step=0823600) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:40:19] (step=0823700) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 16:40:59] (step=0823800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:41:40] (step=0823900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 16:42:20] (step=0824000) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:42:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 16:43:12] (step=0824100) Train Loss: 0.0643, Train Steps/Sec: 1.91, Samples/Sec: 68.59
+[2025-05-18 16:43:36] Beginning epoch 204...
+[2025-05-18 16:43:53] (step=0824200) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-18 16:44:33] (step=0824300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 16:45:14] (step=0824400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:45:54] (step=0824500) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 16:46:34] (step=0824600) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:47:14] (step=0824700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 16:47:55] (step=0824800) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-18 16:48:35] (step=0824900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 16:49:15] (step=0825000) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 16:49:56] (step=0825100) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 16:50:36] (step=0825200) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:51:16] (step=0825300) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 16:51:57] (step=0825400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:52:37] (step=0825500) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 16:53:17] (step=0825600) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:53:58] (step=0825700) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 16:54:38] (step=0825800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:55:18] (step=0825900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 16:55:59] (step=0826000) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 16:56:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 16:56:51] (step=0826100) Train Loss: 0.0652, Train Steps/Sec: 1.91, Samples/Sec: 68.65
+[2025-05-18 16:57:31] (step=0826200) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 16:58:12] (step=0826300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 16:58:52] (step=0826400) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 16:59:32] (step=0826500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:00:13] (step=0826600) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 17:00:53] (step=0826700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 17:01:33] (step=0826800) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 17:02:14] (step=0826900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 17:02:54] (step=0827000) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 17:03:34] (step=0827100) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:04:15] (step=0827200) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 17:04:55] (step=0827300) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 17:05:35] (step=0827400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 17:06:16] (step=0827500) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-18 17:06:56] (step=0827600) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 17:07:36] (step=0827700) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 17:08:17] (step=0827800) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:08:57] (step=0827900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 17:09:37] (step=0828000) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:09:49] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 17:10:30] (step=0828100) Train Loss: 0.0702, Train Steps/Sec: 1.91, Samples/Sec: 68.78
+[2025-05-18 17:11:10] (step=0828200) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 17:11:10] Beginning epoch 205...
+[2025-05-18 17:11:51] (step=0828300) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-18 17:12:31] (step=0828400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 17:13:11] (step=0828500) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 17:13:52] (step=0828600) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:14:32] (step=0828700) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 17:15:12] (step=0828800) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 17:15:53] (step=0828900) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 17:16:33] (step=0829000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:17:13] (step=0829100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 17:17:54] (step=0829200) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 17:18:34] (step=0829300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:19:14] (step=0829400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:19:55] (step=0829500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 17:20:35] (step=0829600) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 17:21:15] (step=0829700) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 17:21:56] (step=0829800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 17:22:36] (step=0829900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 17:23:16] (step=0830000) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 17:23:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 17:23:31] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 17:25:30] (step=0830000) Perceptual Loss: 0.2877, Eval Time: 122.16
+[2025-05-18 17:26:11] (step=0830100) Train Loss: 0.0667, Train Steps/Sec: 0.57, Samples/Sec: 20.62
+[2025-05-18 17:26:51] (step=0830200) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 17:27:32] (step=0830300) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 17:28:12] (step=0830400) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 17:28:52] (step=0830500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 17:29:33] (step=0830600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 17:30:13] (step=0830700) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 17:30:53] (step=0830800) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 17:31:34] (step=0830900) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 17:32:14] (step=0831000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 17:32:54] (step=0831100) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 17:33:35] (step=0831200) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 17:34:15] (step=0831300) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 17:34:55] (step=0831400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 17:35:36] (step=0831500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:36:16] (step=0831600) Train Loss: 0.0623, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-18 17:36:57] (step=0831700) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 17:37:37] (step=0831800) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 17:38:17] (step=0831900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 17:38:57] (step=0832000) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 17:39:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 17:39:50] (step=0832100) Train Loss: 0.0668, Train Steps/Sec: 1.91, Samples/Sec: 68.64
+[2025-05-18 17:40:30] (step=0832200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 17:40:46] Beginning epoch 206...
+[2025-05-18 17:41:11] (step=0832300) Train Loss: 0.0689, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-18 17:41:51] (step=0832400) Train Loss: 0.0617, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 17:42:32] (step=0832500) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:43:12] (step=0832600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:43:52] (step=0832700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 17:44:32] (step=0832800) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 17:45:13] (step=0832900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 17:45:53] (step=0833000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 17:46:34] (step=0833100) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:47:14] (step=0833200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:47:54] (step=0833300) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:48:35] (step=0833400) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-18 17:49:15] (step=0833500) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:49:55] (step=0833600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 17:50:36] (step=0833700) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 17:51:16] (step=0833800) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 17:51:56] (step=0833900) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:52:36] (step=0834000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 17:52:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 17:53:29] (step=0834100) Train Loss: 0.0656, Train Steps/Sec: 1.91, Samples/Sec: 68.88
+[2025-05-18 17:54:09] (step=0834200) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 17:54:49] (step=0834300) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 17:55:30] (step=0834400) Train Loss: 0.0610, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 17:56:10] (step=0834500) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:56:50] (step=0834600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:57:31] (step=0834700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 17:58:11] (step=0834800) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 17:58:51] (step=0834900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 17:59:32] (step=0835000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 18:00:12] (step=0835100) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:00:52] (step=0835200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:01:33] (step=0835300) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:02:13] (step=0835400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:02:53] (step=0835500) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 18:03:34] (step=0835600) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 18:04:14] (step=0835700) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 18:04:54] (step=0835800) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 18:05:35] (step=0835900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:06:15] (step=0836000) Train Loss: 0.0608, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 18:06:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 18:07:07] (step=0836100) Train Loss: 0.0709, Train Steps/Sec: 1.91, Samples/Sec: 68.93
+[2025-05-18 18:07:47] (step=0836200) Train Loss: 0.0716, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:08:20] Beginning epoch 207...
+[2025-05-18 18:08:28] (step=0836300) Train Loss: 0.0656, Train Steps/Sec: 2.46, Samples/Sec: 88.58
+[2025-05-18 18:09:08] (step=0836400) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 18:09:49] (step=0836500) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 18:10:29] (step=0836600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 18:11:09] (step=0836700) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:11:50] (step=0836800) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 18:12:30] (step=0836900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-18 18:13:10] (step=0837000) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-18 18:13:51] (step=0837100) Train Loss: 0.0600, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:14:31] (step=0837200) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:15:11] (step=0837300) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:15:52] (step=0837400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-18 18:16:32] (step=0837500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-18 18:17:12] (step=0837600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 18:17:53] (step=0837700) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-18 18:18:33] (step=0837800) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:19:13] (step=0837900) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:19:54] (step=0838000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:20:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 18:20:46] (step=0838100) Train Loss: 0.0682, Train Steps/Sec: 1.92, Samples/Sec: 69.03
+[2025-05-18 18:21:26] (step=0838200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 18:22:06] (step=0838300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 18:22:47] (step=0838400) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 18:23:27] (step=0838500) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 18:24:07] (step=0838600) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:24:48] (step=0838700) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:25:28] (step=0838800) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 18:26:08] (step=0838900) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 18:26:49] (step=0839000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 18:27:29] (step=0839100) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 18:28:09] (step=0839200) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:28:50] (step=0839300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:29:30] (step=0839400) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 18:30:10] (step=0839500) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 18:30:51] (step=0839600) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:31:31] (step=0839700) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 18:32:11] (step=0839800) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:32:52] (step=0839900) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 18:33:32] (step=0840000) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 18:33:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0840000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 18:33:59] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 18:35:59] (step=0840000) Perceptual Loss: 0.3160, Eval Time: 122.28
+[2025-05-18 18:36:39] (step=0840100) Train Loss: 0.0672, Train Steps/Sec: 0.53, Samples/Sec: 19.21
+[2025-05-18 18:37:20] (step=0840200) Train Loss: 0.0738, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 18:38:00] (step=0840300) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 18:38:08] Beginning epoch 208...
+[2025-05-18 18:38:41] (step=0840400) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-18 18:39:21] (step=0840500) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 18:40:01] (step=0840600) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 18:40:42] (step=0840700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 18:41:22] (step=0840800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 18:42:02] (step=0840900) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 18:42:43] (step=0841000) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 18:43:23] (step=0841100) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 18:44:03] (step=0841200) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 18:44:44] (step=0841300) Train Loss: 0.0588, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 18:45:24] (step=0841400) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 18:46:04] (step=0841500) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:46:45] (step=0841600) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 18:47:25] (step=0841700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 18:48:05] (step=0841800) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:48:46] (step=0841900) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 18:49:26] (step=0842000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 18:49:39] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 18:50:19] (step=0842100) Train Loss: 0.0654, Train Steps/Sec: 1.88, Samples/Sec: 67.50
+[2025-05-18 18:51:00] (step=0842200) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 18:51:40] (step=0842300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 18:52:20] (step=0842400) Train Loss: 0.0598, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 18:53:01] (step=0842500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 18:53:41] (step=0842600) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 18:54:21] (step=0842700) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 18:55:02] (step=0842800) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 18:55:42] (step=0842900) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 18:56:22] (step=0843000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 18:57:03] (step=0843100) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 18:57:43] (step=0843200) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 18:58:23] (step=0843300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 18:59:04] (step=0843400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 18:59:44] (step=0843500) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:00:24] (step=0843600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 19:01:05] (step=0843700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 19:01:45] (step=0843800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 19:02:25] (step=0843900) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 19:03:06] (step=0844000) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:03:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 19:03:58] (step=0844100) Train Loss: 0.0675, Train Steps/Sec: 1.91, Samples/Sec: 68.84
+[2025-05-18 19:04:38] (step=0844200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 19:05:19] (step=0844300) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-18 19:05:43] Beginning epoch 209...
+[2025-05-18 19:05:59] (step=0844400) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-18 19:06:40] (step=0844500) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:07:20] (step=0844600) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 19:08:00] (step=0844700) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 19:08:41] (step=0844800) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 19:09:21] (step=0844900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 19:10:01] (step=0845000) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-18 19:10:42] (step=0845100) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 19:11:22] (step=0845200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 19:12:02] (step=0845300) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 19:12:43] (step=0845400) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:13:23] (step=0845500) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 19:14:03] (step=0845600) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 19:14:44] (step=0845700) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 19:15:24] (step=0845800) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:16:04] (step=0845900) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 19:16:45] (step=0846000) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 19:16:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 19:17:37] (step=0846100) Train Loss: 0.0702, Train Steps/Sec: 1.91, Samples/Sec: 68.66
+[2025-05-18 19:18:17] (step=0846200) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:18:58] (step=0846300) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 19:19:38] (step=0846400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 19:20:18] (step=0846500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 19:20:59] (step=0846600) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 19:21:39] (step=0846700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:22:19] (step=0846800) Train Loss: 0.0598, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 19:23:00] (step=0846900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:23:40] (step=0847000) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 19:24:20] (step=0847100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 19:25:01] (step=0847200) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:25:41] (step=0847300) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 19:26:21] (step=0847400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:27:02] (step=0847500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-18 19:27:42] (step=0847600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:28:22] (step=0847700) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-18 19:29:03] (step=0847800) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:29:43] (step=0847900) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:30:23] (step=0848000) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 19:30:35] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 19:31:16] (step=0848100) Train Loss: 0.0684, Train Steps/Sec: 1.91, Samples/Sec: 68.79
+[2025-05-18 19:31:56] (step=0848200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 19:32:36] (step=0848300) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 19:33:17] (step=0848400) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 19:33:17] Beginning epoch 210...
+[2025-05-18 19:33:57] (step=0848500) Train Loss: 0.0739, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-18 19:34:38] (step=0848600) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 19:35:18] (step=0848700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 19:35:58] (step=0848800) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 19:36:39] (step=0848900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:37:19] (step=0849000) Train Loss: 0.0608, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 19:37:59] (step=0849100) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 19:38:40] (step=0849200) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:39:20] (step=0849300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 19:40:00] (step=0849400) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 19:40:41] (step=0849500) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 19:41:21] (step=0849600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:42:01] (step=0849700) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 19:42:42] (step=0849800) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 19:43:22] (step=0849900) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 19:44:02] (step=0850000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 19:44:14] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 19:44:17] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 19:46:17] (step=0850000) Perceptual Loss: 0.2980, Eval Time: 122.24
+[2025-05-18 19:46:57] (step=0850100) Train Loss: 0.0682, Train Steps/Sec: 0.57, Samples/Sec: 20.60
+[2025-05-18 19:47:37] (step=0850200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 19:48:18] (step=0850300) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-18 19:48:58] (step=0850400) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 19:49:38] (step=0850500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 19:50:19] (step=0850600) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 19:50:59] (step=0850700) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 19:51:39] (step=0850800) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:52:20] (step=0850900) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 19:53:00] (step=0851000) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-18 19:53:40] (step=0851100) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 19:54:21] (step=0851200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 19:55:01] (step=0851300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:55:41] (step=0851400) Train Loss: 0.0604, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 19:56:22] (step=0851500) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-18 19:57:02] (step=0851600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-18 19:57:42] (step=0851700) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 19:58:23] (step=0851800) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 19:59:03] (step=0851900) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 19:59:43] (step=0852000) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-18 19:59:56] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 20:00:36] (step=0852100) Train Loss: 0.0688, Train Steps/Sec: 1.89, Samples/Sec: 68.17
+[2025-05-18 20:01:16] (step=0852200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.37
+[2025-05-18 20:01:57] (step=0852300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 20:02:37] (step=0852400) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 20:02:53] Beginning epoch 211...
+[2025-05-18 20:03:17] (step=0852500) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-18 20:03:58] (step=0852600) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:04:38] (step=0852700) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 20:05:18] (step=0852800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 20:05:59] (step=0852900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 20:06:39] (step=0853000) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 20:07:20] (step=0853100) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 20:08:00] (step=0853200) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 20:08:40] (step=0853300) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 20:09:21] (step=0853400) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 20:10:01] (step=0853500) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 20:10:41] (step=0853600) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 20:11:22] (step=0853700) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 20:12:02] (step=0853800) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 20:12:42] (step=0853900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 20:13:23] (step=0854000) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 20:13:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 20:14:15] (step=0854100) Train Loss: 0.0679, Train Steps/Sec: 1.92, Samples/Sec: 68.95
+[2025-05-18 20:14:55] (step=0854200) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:15:35] (step=0854300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 20:16:16] (step=0854400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 20:16:56] (step=0854500) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 20:17:36] (step=0854600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:18:17] (step=0854700) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:18:57] (step=0854800) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 20:19:37] (step=0854900) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 20:20:18] (step=0855000) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 20:20:58] (step=0855100) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 20:21:38] (step=0855200) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 20:22:19] (step=0855300) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 20:22:59] (step=0855400) Train Loss: 0.0617, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:23:39] (step=0855500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-18 20:24:20] (step=0855600) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 20:25:00] (step=0855700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 20:25:40] (step=0855800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 20:26:21] (step=0855900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 20:27:01] (step=0856000) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 20:27:14] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 20:27:54] (step=0856100) Train Loss: 0.0676, Train Steps/Sec: 1.88, Samples/Sec: 67.80
+[2025-05-18 20:28:34] (step=0856200) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 20:29:15] (step=0856300) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 20:29:55] (step=0856400) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-18 20:30:27] Beginning epoch 212...
+[2025-05-18 20:30:36] (step=0856500) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-18 20:31:16] (step=0856600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 20:31:56] (step=0856700) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 20:32:37] (step=0856800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 20:33:17] (step=0856900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 20:33:58] (step=0857000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 20:34:38] (step=0857100) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 20:35:18] (step=0857200) Train Loss: 0.0736, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:35:58] (step=0857300) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 20:36:39] (step=0857400) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:37:19] (step=0857500) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-18 20:37:59] (step=0857600) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 20:38:40] (step=0857700) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 20:39:20] (step=0857800) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 20:40:01] (step=0857900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 20:40:41] (step=0858000) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 20:40:53] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 20:41:34] (step=0858100) Train Loss: 0.0655, Train Steps/Sec: 1.89, Samples/Sec: 68.11
+[2025-05-18 20:42:14] (step=0858200) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 20:42:54] (step=0858300) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 20:43:35] (step=0858400) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-18 20:44:15] (step=0858500) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 20:44:55] (step=0858600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 20:45:36] (step=0858700) Train Loss: 0.0609, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 20:46:16] (step=0858800) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 20:46:56] (step=0858900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 20:47:37] (step=0859000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:48:17] (step=0859100) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-18 20:48:58] (step=0859200) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:49:38] (step=0859300) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 20:50:18] (step=0859400) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 20:50:59] (step=0859500) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 20:51:39] (step=0859600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 20:52:19] (step=0859700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 20:53:00] (step=0859800) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 20:53:40] (step=0859900) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 20:54:20] (step=0860000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 20:54:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0860000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 20:54:47] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 20:56:46] (step=0860000) Perceptual Loss: 0.3003, Eval Time: 122.40
+[2025-05-18 20:57:27] (step=0860100) Train Loss: 0.0661, Train Steps/Sec: 0.54, Samples/Sec: 19.29
+[2025-05-18 20:58:07] (step=0860200) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 20:58:48] (step=0860300) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 20:59:28] (step=0860400) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 21:00:08] (step=0860500) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 21:00:16] Beginning epoch 213...
+[2025-05-18 21:00:49] (step=0860600) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-18 21:01:29] (step=0860700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 21:02:10] (step=0860800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 21:02:50] (step=0860900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 21:03:30] (step=0861000) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 21:04:11] (step=0861100) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 21:04:51] (step=0861200) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:05:31] (step=0861300) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:06:12] (step=0861400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 21:06:52] (step=0861500) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 21:07:32] (step=0861600) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 21:08:13] (step=0861700) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 21:08:53] (step=0861800) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 21:09:33] (step=0861900) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:10:14] (step=0862000) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:10:26] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 21:11:06] (step=0862100) Train Loss: 0.0645, Train Steps/Sec: 1.91, Samples/Sec: 68.90
+[2025-05-18 21:11:46] (step=0862200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 21:12:27] (step=0862300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 21:13:07] (step=0862400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 21:13:47] (step=0862500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 21:14:28] (step=0862600) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 21:15:08] (step=0862700) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 21:15:48] (step=0862800) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:16:29] (step=0862900) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:17:09] (step=0863000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 21:17:50] (step=0863100) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 21:18:30] (step=0863200) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 21:19:10] (step=0863300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:19:51] (step=0863400) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 21:20:31] (step=0863500) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 21:21:11] (step=0863600) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:21:52] (step=0863700) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 21:22:32] (step=0863800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 21:23:12] (step=0863900) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 21:23:53] (step=0864000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:24:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 21:24:45] (step=0864100) Train Loss: 0.0689, Train Steps/Sec: 1.90, Samples/Sec: 68.43
+[2025-05-18 21:25:26] (step=0864200) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 21:26:06] (step=0864300) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:26:46] (step=0864400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 21:27:27] (step=0864500) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 21:27:51] Beginning epoch 214...
+[2025-05-18 21:28:07] (step=0864600) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-18 21:28:47] (step=0864700) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 21:29:28] (step=0864800) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:30:08] (step=0864900) Train Loss: 0.0603, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 21:30:49] (step=0865000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 21:31:29] (step=0865100) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:32:09] (step=0865200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 21:32:50] (step=0865300) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:33:30] (step=0865400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:34:10] (step=0865500) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 21:34:51] (step=0865600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:35:31] (step=0865700) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:36:11] (step=0865800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 21:36:52] (step=0865900) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-18 21:37:32] (step=0866000) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 21:37:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 21:38:25] (step=0866100) Train Loss: 0.0741, Train Steps/Sec: 1.89, Samples/Sec: 68.13
+[2025-05-18 21:39:05] (step=0866200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:39:46] (step=0866300) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 21:40:26] (step=0866400) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:41:06] (step=0866500) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 21:41:47] (step=0866600) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:42:27] (step=0866700) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:43:07] (step=0866800) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 21:43:48] (step=0866900) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:44:28] (step=0867000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:45:08] (step=0867100) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:45:49] (step=0867200) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:46:29] (step=0867300) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:47:09] (step=0867400) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:47:50] (step=0867500) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 21:48:30] (step=0867600) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 21:49:11] (step=0867700) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:49:51] (step=0867800) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:50:31] (step=0867900) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 21:51:12] (step=0868000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:51:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 21:52:04] (step=0868100) Train Loss: 0.0615, Train Steps/Sec: 1.90, Samples/Sec: 68.30
+[2025-05-18 21:52:45] (step=0868200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 21:53:25] (step=0868300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:54:05] (step=0868400) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 21:54:46] (step=0868500) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 21:55:26] (step=0868600) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-18 21:55:26] Beginning epoch 215...
+[2025-05-18 21:56:07] (step=0868700) Train Loss: 0.0702, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-18 21:56:47] (step=0868800) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 21:57:27] (step=0868900) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 21:58:08] (step=0869000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 21:58:48] (step=0869100) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 21:59:28] (step=0869200) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 22:00:09] (step=0869300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:00:49] (step=0869400) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:01:29] (step=0869500) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:02:10] (step=0869600) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:02:50] (step=0869700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 22:03:31] (step=0869800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 22:04:11] (step=0869900) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 22:04:51] (step=0870000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 22:05:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 22:05:06] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 22:07:06] (step=0870000) Perceptual Loss: 0.3104, Eval Time: 122.18
+[2025-05-18 22:07:46] (step=0870100) Train Loss: 0.0690, Train Steps/Sec: 0.57, Samples/Sec: 20.56
+[2025-05-18 22:08:27] (step=0870200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 22:09:07] (step=0870300) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:09:47] (step=0870400) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 22:10:28] (step=0870500) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 22:11:08] (step=0870600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:11:48] (step=0870700) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 22:12:29] (step=0870800) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 22:13:09] (step=0870900) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:13:49] (step=0871000) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 22:14:30] (step=0871100) Train Loss: 0.0597, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:15:10] (step=0871200) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:15:50] (step=0871300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 22:16:31] (step=0871400) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:17:11] (step=0871500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 22:17:52] (step=0871600) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 22:18:32] (step=0871700) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:19:12] (step=0871800) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:19:53] (step=0871900) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 22:20:33] (step=0872000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 22:20:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 22:21:26] (step=0872100) Train Loss: 0.0689, Train Steps/Sec: 1.89, Samples/Sec: 67.88
+[2025-05-18 22:22:06] (step=0872200) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:22:47] (step=0872300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 22:23:27] (step=0872400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 22:24:07] (step=0872500) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 22:24:48] (step=0872600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 22:25:04] Beginning epoch 216...
+[2025-05-18 22:25:28] (step=0872700) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-18 22:26:09] (step=0872800) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 22:26:49] (step=0872900) Train Loss: 0.0737, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:27:29] (step=0873000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:28:10] (step=0873100) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:28:50] (step=0873200) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 22:29:30] (step=0873300) Train Loss: 0.0603, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 22:30:11] (step=0873400) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 22:30:51] (step=0873500) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:31:31] (step=0873600) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:32:12] (step=0873700) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:32:52] (step=0873800) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 22:33:32] (step=0873900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:34:13] (step=0874000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 22:34:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 22:35:06] (step=0874100) Train Loss: 0.0695, Train Steps/Sec: 1.90, Samples/Sec: 68.25
+[2025-05-18 22:35:46] (step=0874200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 22:36:26] (step=0874300) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 22:37:06] (step=0874400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:37:47] (step=0874500) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:38:27] (step=0874600) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 22:39:08] (step=0874700) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:39:48] (step=0874800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 22:40:28] (step=0874900) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 22:41:09] (step=0875000) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 22:41:49] (step=0875100) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 22:42:29] (step=0875200) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 22:43:10] (step=0875300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:43:50] (step=0875400) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:44:30] (step=0875500) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 22:45:11] (step=0875600) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 22:45:51] (step=0875700) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 22:46:31] (step=0875800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 22:47:12] (step=0875900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 22:47:52] (step=0876000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 22:48:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 22:48:45] (step=0876100) Train Loss: 0.0741, Train Steps/Sec: 1.90, Samples/Sec: 68.25
+[2025-05-18 22:49:25] (step=0876200) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 22:50:05] (step=0876300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:50:46] (step=0876400) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 22:51:26] (step=0876500) Train Loss: 0.0736, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:52:06] (step=0876600) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:52:39] Beginning epoch 217...
+[2025-05-18 22:52:47] (step=0876700) Train Loss: 0.0678, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-18 22:53:27] (step=0876800) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:54:08] (step=0876900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:54:48] (step=0877000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-18 22:55:28] (step=0877100) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 22:56:09] (step=0877200) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 22:56:49] (step=0877300) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 22:57:29] (step=0877400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:58:10] (step=0877500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 22:58:50] (step=0877600) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 22:59:30] (step=0877700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 23:00:11] (step=0877800) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 23:00:51] (step=0877900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 23:01:32] (step=0878000) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-18 23:01:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 23:02:24] (step=0878100) Train Loss: 0.0626, Train Steps/Sec: 1.89, Samples/Sec: 68.19
+[2025-05-18 23:03:05] (step=0878200) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 23:03:45] (step=0878300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 23:04:25] (step=0878400) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 23:05:06] (step=0878500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 23:05:46] (step=0878600) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 23:06:26] (step=0878700) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 23:07:07] (step=0878800) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-18 23:07:47] (step=0878900) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 23:08:27] (step=0879000) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 23:09:08] (step=0879100) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 23:09:48] (step=0879200) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 23:10:29] (step=0879300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 23:11:09] (step=0879400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 23:11:49] (step=0879500) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 23:12:30] (step=0879600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 23:13:10] (step=0879700) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-18 23:13:50] (step=0879800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 23:14:31] (step=0879900) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-18 23:15:11] (step=0880000) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 23:15:35] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0880000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-18 23:15:38] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-18 23:17:37] (step=0880000) Perceptual Loss: 0.2990, Eval Time: 122.17
+[2025-05-18 23:18:18] (step=0880100) Train Loss: 0.0655, Train Steps/Sec: 0.54, Samples/Sec: 19.28
+[2025-05-18 23:18:58] (step=0880200) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 23:19:39] (step=0880300) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-18 23:20:19] (step=0880400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 23:20:59] (step=0880500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-18 23:21:40] (step=0880600) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 23:22:20] (step=0880700) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 23:22:28] Beginning epoch 218...
+[2025-05-18 23:23:01] (step=0880800) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-18 23:23:41] (step=0880900) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-18 23:24:21] (step=0881000) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-18 23:25:02] (step=0881100) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 23:25:42] (step=0881200) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:26:22] (step=0881300) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-18 23:27:03] (step=0881400) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 23:27:43] (step=0881500) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 23:28:24] (step=0881600) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 23:29:04] (step=0881700) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 23:29:44] (step=0881800) Train Loss: 0.0742, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 23:30:25] (step=0881900) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:31:05] (step=0882000) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 23:31:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 23:31:58] (step=0882100) Train Loss: 0.0709, Train Steps/Sec: 1.88, Samples/Sec: 67.77
+[2025-05-18 23:32:39] (step=0882200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:33:19] (step=0882300) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 23:33:59] (step=0882400) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:34:40] (step=0882500) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:35:20] (step=0882600) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-18 23:36:00] (step=0882700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:36:41] (step=0882800) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 23:37:21] (step=0882900) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:38:02] (step=0883000) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 23:38:42] (step=0883100) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:39:22] (step=0883200) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:40:03] (step=0883300) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-18 23:40:43] (step=0883400) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 23:41:24] (step=0883500) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-18 23:42:04] (step=0883600) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 23:42:44] (step=0883700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-18 23:43:25] (step=0883800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-18 23:44:05] (step=0883900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-18 23:44:45] (step=0884000) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-18 23:44:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 23:45:38] (step=0884100) Train Loss: 0.0678, Train Steps/Sec: 1.89, Samples/Sec: 67.92
+[2025-05-18 23:46:18] (step=0884200) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-18 23:46:59] (step=0884300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-18 23:47:39] (step=0884400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-18 23:48:20] (step=0884500) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-18 23:49:00] (step=0884600) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-18 23:49:40] (step=0884700) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 23:50:05] Beginning epoch 219...
+[2025-05-18 23:50:21] (step=0884800) Train Loss: 0.0591, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-18 23:51:01] (step=0884900) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-18 23:51:42] (step=0885000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-18 23:52:22] (step=0885100) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-18 23:53:02] (step=0885200) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-18 23:53:43] (step=0885300) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-18 23:54:23] (step=0885400) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-18 23:55:03] (step=0885500) Train Loss: 0.0746, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-18 23:55:44] (step=0885600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-18 23:56:24] (step=0885700) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-18 23:57:04] (step=0885800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-18 23:57:45] (step=0885900) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-18 23:58:25] (step=0886000) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-18 23:58:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-18 23:59:18] (step=0886100) Train Loss: 0.0671, Train Steps/Sec: 1.89, Samples/Sec: 68.03
+[2025-05-18 23:59:58] (step=0886200) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 00:00:39] (step=0886300) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 00:01:19] (step=0886400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 00:02:00] (step=0886500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 00:02:40] (step=0886600) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 00:03:20] (step=0886700) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 00:04:01] (step=0886800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 00:04:41] (step=0886900) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:05:21] (step=0887000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 00:06:02] (step=0887100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:06:42] (step=0887200) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 00:07:22] (step=0887300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 00:08:03] (step=0887400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 00:08:43] (step=0887500) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 00:09:23] (step=0887600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 00:10:04] (step=0887700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 00:10:44] (step=0887800) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 00:11:24] (step=0887900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 00:12:05] (step=0888000) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 00:12:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 00:12:57] (step=0888100) Train Loss: 0.0642, Train Steps/Sec: 1.90, Samples/Sec: 68.36
+[2025-05-19 00:13:38] (step=0888200) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 00:14:18] (step=0888300) Train Loss: 0.0607, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 00:14:58] (step=0888400) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-19 00:15:39] (step=0888500) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 00:16:19] (step=0888600) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 00:17:00] (step=0888700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 00:17:40] (step=0888800) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 00:17:40] Beginning epoch 220...
+[2025-05-19 00:18:20] (step=0888900) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 00:19:01] (step=0889000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 00:19:41] (step=0889100) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-19 00:20:21] (step=0889200) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 00:21:02] (step=0889300) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 00:21:42] (step=0889400) Train Loss: 0.0612, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:22:23] (step=0889500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 00:23:03] (step=0889600) Train Loss: 0.0739, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:23:43] (step=0889700) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-19 00:24:24] (step=0889800) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 00:25:04] (step=0889900) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:25:44] (step=0890000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 00:25:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 00:26:00] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 00:27:59] (step=0890000) Perceptual Loss: 0.3281, Eval Time: 122.12
+[2025-05-19 00:28:39] (step=0890100) Train Loss: 0.0654, Train Steps/Sec: 0.57, Samples/Sec: 20.54
+[2025-05-19 00:29:20] (step=0890200) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-19 00:30:00] (step=0890300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 00:30:40] (step=0890400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 00:31:21] (step=0890500) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 00:32:01] (step=0890600) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-19 00:32:42] (step=0890700) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 00:33:22] (step=0890800) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:34:02] (step=0890900) Train Loss: 0.0606, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:34:43] (step=0891000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-19 00:35:23] (step=0891100) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-19 00:36:03] (step=0891200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-19 00:36:43] (step=0891300) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 00:37:24] (step=0891400) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 00:38:04] (step=0891500) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 00:38:45] (step=0891600) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 00:39:25] (step=0891700) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 00:40:05] (step=0891800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 00:40:46] (step=0891900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 00:41:26] (step=0892000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:41:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 00:42:19] (step=0892100) Train Loss: 0.0670, Train Steps/Sec: 1.89, Samples/Sec: 68.00
+[2025-05-19 00:42:59] (step=0892200) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 00:43:39] (step=0892300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 00:44:20] (step=0892400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 00:45:00] (step=0892500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 00:45:40] (step=0892600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 00:46:21] (step=0892700) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 00:47:01] (step=0892800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 00:47:17] Beginning epoch 221...
+[2025-05-19 00:47:42] (step=0892900) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 00:48:22] (step=0893000) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 00:49:02] (step=0893100) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 00:49:43] (step=0893200) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 00:50:23] (step=0893300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 00:51:04] (step=0893400) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 00:51:44] (step=0893500) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 00:52:24] (step=0893600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 00:53:05] (step=0893700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 00:53:45] (step=0893800) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 00:54:25] (step=0893900) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 00:55:06] (step=0894000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 00:55:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 00:55:59] (step=0894100) Train Loss: 0.0627, Train Steps/Sec: 1.88, Samples/Sec: 67.52
+[2025-05-19 00:56:39] (step=0894200) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:57:20] (step=0894300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-19 00:58:00] (step=0894400) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 00:58:40] (step=0894500) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 00:59:20] (step=0894600) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 01:00:01] (step=0894700) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 01:00:41] (step=0894800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 01:01:22] (step=0894900) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 01:02:02] (step=0895000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 01:02:42] (step=0895100) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 01:03:23] (step=0895200) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 01:04:03] (step=0895300) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 01:04:43] (step=0895400) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 01:05:24] (step=0895500) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 01:06:04] (step=0895600) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 01:06:44] (step=0895700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 01:07:25] (step=0895800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 01:08:05] (step=0895900) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 01:08:45] (step=0896000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 01:08:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 01:09:39] (step=0896100) Train Loss: 0.0664, Train Steps/Sec: 1.87, Samples/Sec: 67.49
+[2025-05-19 01:10:19] (step=0896200) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 01:10:59] (step=0896300) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 01:11:40] (step=0896400) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 01:12:20] (step=0896500) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 01:13:00] (step=0896600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 01:13:41] (step=0896700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 01:14:21] (step=0896800) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 01:14:53] Beginning epoch 222...
+[2025-05-19 01:15:02] (step=0896900) Train Loss: 0.0708, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 01:15:42] (step=0897000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 01:16:22] (step=0897100) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 01:17:03] (step=0897200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 01:17:43] (step=0897300) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 01:18:23] (step=0897400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 01:19:04] (step=0897500) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 01:19:44] (step=0897600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 01:20:25] (step=0897700) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 01:21:05] (step=0897800) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-19 01:21:45] (step=0897900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 01:22:26] (step=0898000) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 01:22:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 01:23:19] (step=0898100) Train Loss: 0.0624, Train Steps/Sec: 1.89, Samples/Sec: 67.92
+[2025-05-19 01:23:59] (step=0898200) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-19 01:24:39] (step=0898300) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 01:25:20] (step=0898400) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 01:26:00] (step=0898500) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 01:26:41] (step=0898600) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 01:27:21] (step=0898700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 01:28:01] (step=0898800) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 01:28:42] (step=0898900) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 01:29:22] (step=0899000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 01:30:02] (step=0899100) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 01:30:43] (step=0899200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 01:31:23] (step=0899300) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 01:32:03] (step=0899400) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 01:32:44] (step=0899500) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 01:33:24] (step=0899600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-19 01:34:05] (step=0899700) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-19 01:34:45] (step=0899800) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 01:35:26] (step=0899900) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 01:36:06] (step=0900000) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 01:36:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0900000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 01:36:33] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 01:38:32] (step=0900000) Perceptual Loss: 0.3183, Eval Time: 122.65
+[2025-05-19 01:39:13] (step=0900100) Train Loss: 0.0698, Train Steps/Sec: 0.54, Samples/Sec: 19.27
+[2025-05-19 01:39:53] (step=0900200) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 01:40:34] (step=0900300) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 01:41:14] (step=0900400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 01:41:54] (step=0900500) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 01:42:35] (step=0900600) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 01:43:15] (step=0900700) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 01:43:55] (step=0900800) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 01:44:36] (step=0900900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 01:44:44] Beginning epoch 223...
+[2025-05-19 01:45:16] (step=0901000) Train Loss: 0.0707, Train Steps/Sec: 2.46, Samples/Sec: 88.43
+[2025-05-19 01:45:57] (step=0901100) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 01:46:37] (step=0901200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 01:47:17] (step=0901300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 01:47:58] (step=0901400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 01:48:38] (step=0901500) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 01:49:19] (step=0901600) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 01:49:59] (step=0901700) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 01:50:39] (step=0901800) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 01:51:20] (step=0901900) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 01:52:00] (step=0902000) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 01:52:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 01:52:53] (step=0902100) Train Loss: 0.0651, Train Steps/Sec: 1.90, Samples/Sec: 68.45
+[2025-05-19 01:53:33] (step=0902200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 01:54:13] (step=0902300) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-19 01:54:54] (step=0902400) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 01:55:34] (step=0902500) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 01:56:14] (step=0902600) Train Loss: 0.0606, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 01:56:55] (step=0902700) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 01:57:35] (step=0902800) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 01:58:15] (step=0902900) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 01:58:56] (step=0903000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 01:59:36] (step=0903100) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 02:00:17] (step=0903200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 02:00:57] (step=0903300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 02:01:37] (step=0903400) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-19 02:02:18] (step=0903500) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 02:02:58] (step=0903600) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 02:03:38] (step=0903700) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-19 02:04:19] (step=0903800) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 02:04:59] (step=0903900) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 02:05:40] (step=0904000) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 02:05:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 02:06:33] (step=0904100) Train Loss: 0.0634, Train Steps/Sec: 1.88, Samples/Sec: 67.54
+[2025-05-19 02:07:13] (step=0904200) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 02:07:54] (step=0904300) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 02:08:34] (step=0904400) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 02:09:14] (step=0904500) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 02:09:55] (step=0904600) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 02:10:35] (step=0904700) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 02:11:16] (step=0904800) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 02:11:56] (step=0904900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 02:12:21] Beginning epoch 224...
+[2025-05-19 02:12:37] (step=0905000) Train Loss: 0.0699, Train Steps/Sec: 2.45, Samples/Sec: 88.23
+[2025-05-19 02:13:17] (step=0905100) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-19 02:13:58] (step=0905200) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 02:14:38] (step=0905300) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 02:15:18] (step=0905400) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 02:15:59] (step=0905500) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 02:16:39] (step=0905600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 02:17:20] (step=0905700) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-19 02:18:00] (step=0905800) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 02:18:40] (step=0905900) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 02:19:21] (step=0906000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 02:19:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 02:20:13] (step=0906100) Train Loss: 0.0667, Train Steps/Sec: 1.91, Samples/Sec: 68.93
+[2025-05-19 02:20:53] (step=0906200) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 02:21:34] (step=0906300) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 02:22:14] (step=0906400) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 02:22:54] (step=0906500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 02:23:35] (step=0906600) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 02:24:15] (step=0906700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 02:24:55] (step=0906800) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 02:25:36] (step=0906900) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 02:26:16] (step=0907000) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 02:26:56] (step=0907100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 02:27:37] (step=0907200) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 02:28:17] (step=0907300) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 02:28:57] (step=0907400) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 02:29:38] (step=0907500) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 02:30:18] (step=0907600) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 02:30:58] (step=0907700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 02:31:39] (step=0907800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 02:32:19] (step=0907900) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 02:32:59] (step=0908000) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 02:33:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 02:33:52] (step=0908100) Train Loss: 0.0696, Train Steps/Sec: 1.92, Samples/Sec: 68.97
+[2025-05-19 02:34:32] (step=0908200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 02:35:12] (step=0908300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 02:35:53] (step=0908400) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 02:36:33] (step=0908500) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 02:37:13] (step=0908600) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 02:37:54] (step=0908700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 02:38:34] (step=0908800) Train Loss: 0.0735, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-19 02:39:14] (step=0908900) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 02:39:55] (step=0909000) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.38
+[2025-05-19 02:39:55] Beginning epoch 225...
+[2025-05-19 02:40:35] (step=0909100) Train Loss: 0.0683, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-19 02:41:16] (step=0909200) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 02:41:56] (step=0909300) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 02:42:36] (step=0909400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 02:43:17] (step=0909500) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 02:43:57] (step=0909600) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 02:44:37] (step=0909700) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 02:45:18] (step=0909800) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 02:45:58] (step=0909900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 02:46:38] (step=0910000) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 02:46:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 02:46:53] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 02:48:53] (step=0910000) Perceptual Loss: 0.2883, Eval Time: 122.10
+[2025-05-19 02:49:33] (step=0910100) Train Loss: 0.0664, Train Steps/Sec: 0.57, Samples/Sec: 20.62
+[2025-05-19 02:50:13] (step=0910200) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 02:50:54] (step=0910300) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 02:51:34] (step=0910400) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 02:52:14] (step=0910500) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 02:52:55] (step=0910600) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-19 02:53:35] (step=0910700) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-19 02:54:15] (step=0910800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 02:54:56] (step=0910900) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 02:55:36] (step=0911000) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 02:56:17] (step=0911100) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 02:56:57] (step=0911200) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 02:57:37] (step=0911300) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 02:58:18] (step=0911400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-19 02:58:58] (step=0911500) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-19 02:59:38] (step=0911600) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 03:00:19] (step=0911700) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 03:00:59] (step=0911800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:01:39] (step=0911900) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 03:02:20] (step=0912000) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 03:02:32] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 03:03:13] (step=0912100) Train Loss: 0.0661, Train Steps/Sec: 1.88, Samples/Sec: 67.59
+[2025-05-19 03:03:53] (step=0912200) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 03:04:34] (step=0912300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 03:05:14] (step=0912400) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 03:05:54] (step=0912500) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 03:06:34] (step=0912600) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 03:07:15] (step=0912700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 03:07:55] (step=0912800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 03:08:36] (step=0912900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:09:16] (step=0913000) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 03:09:32] Beginning epoch 226...
+[2025-05-19 03:09:56] (step=0913100) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 03:10:37] (step=0913200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 03:11:17] (step=0913300) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 03:11:58] (step=0913400) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 03:12:38] (step=0913500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:13:18] (step=0913600) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 03:13:59] (step=0913700) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 03:14:39] (step=0913800) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 03:15:19] (step=0913900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:16:00] (step=0914000) Train Loss: 0.0763, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 03:16:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 03:16:52] (step=0914100) Train Loss: 0.0684, Train Steps/Sec: 1.92, Samples/Sec: 69.06
+[2025-05-19 03:17:32] (step=0914200) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 03:18:13] (step=0914300) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 03:18:53] (step=0914400) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 03:19:33] (step=0914500) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:20:13] (step=0914600) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:20:54] (step=0914700) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 03:21:34] (step=0914800) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 03:22:15] (step=0914900) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 03:22:55] (step=0915000) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 03:23:35] (step=0915100) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 03:24:16] (step=0915200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 03:24:56] (step=0915300) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:25:36] (step=0915400) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:26:17] (step=0915500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 03:26:57] (step=0915600) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 03:27:37] (step=0915700) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 03:28:18] (step=0915800) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:28:58] (step=0915900) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 03:29:38] (step=0916000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:29:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 03:30:31] (step=0916100) Train Loss: 0.0652, Train Steps/Sec: 1.92, Samples/Sec: 68.98
+[2025-05-19 03:31:11] (step=0916200) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 03:31:51] (step=0916300) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 03:32:32] (step=0916400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:33:12] (step=0916500) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:33:52] (step=0916600) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 03:34:33] (step=0916700) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:35:13] (step=0916800) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 03:35:53] (step=0916900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 03:36:34] (step=0917000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 03:37:06] Beginning epoch 227...
+[2025-05-19 03:37:14] (step=0917100) Train Loss: 0.0674, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-19 03:37:55] (step=0917200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:38:35] (step=0917300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:39:15] (step=0917400) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:39:56] (step=0917500) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-19 03:40:36] (step=0917600) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 03:41:17] (step=0917700) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 03:41:57] (step=0917800) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 03:42:37] (step=0917900) Train Loss: 0.0738, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 03:43:17] (step=0918000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 03:43:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 03:44:10] (step=0918100) Train Loss: 0.0657, Train Steps/Sec: 1.92, Samples/Sec: 69.16
+[2025-05-19 03:44:50] (step=0918200) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 03:45:30] (step=0918300) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 03:46:11] (step=0918400) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 03:46:51] (step=0918500) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:47:31] (step=0918600) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:48:12] (step=0918700) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 03:48:52] (step=0918800) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 03:49:32] (step=0918900) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 03:50:13] (step=0919000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 03:50:53] (step=0919100) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 03:51:33] (step=0919200) Train Loss: 0.0744, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:52:14] (step=0919300) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:52:54] (step=0919400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:53:34] (step=0919500) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 03:54:15] (step=0919600) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 03:54:55] (step=0919700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 03:55:36] (step=0919800) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 03:56:16] (step=0919900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 03:56:56] (step=0920000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 03:57:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0920000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 03:57:25] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 03:59:24] (step=0920000) Perceptual Loss: 0.3195, Eval Time: 122.43
+[2025-05-19 04:00:05] (step=0920100) Train Loss: 0.0693, Train Steps/Sec: 0.53, Samples/Sec: 19.10
+[2025-05-19 04:00:45] (step=0920200) Train Loss: 0.0603, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-19 04:01:25] (step=0920300) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 04:02:06] (step=0920400) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:02:46] (step=0920500) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 04:03:26] (step=0920600) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 04:04:07] (step=0920700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 04:04:47] (step=0920800) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 04:05:28] (step=0920900) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 04:06:08] (step=0921000) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 04:06:48] (step=0921100) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 04:06:56] Beginning epoch 228...
+[2025-05-19 04:07:29] (step=0921200) Train Loss: 0.0689, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 04:08:09] (step=0921300) Train Loss: 0.0608, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 04:08:49] (step=0921400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 04:09:30] (step=0921500) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 04:10:10] (step=0921600) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 04:10:51] (step=0921700) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:11:31] (step=0921800) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 04:12:11] (step=0921900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 04:12:52] (step=0922000) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 04:13:04] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 04:13:45] (step=0922100) Train Loss: 0.0680, Train Steps/Sec: 1.88, Samples/Sec: 67.54
+[2025-05-19 04:14:25] (step=0922200) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 04:15:06] (step=0922300) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-19 04:15:46] (step=0922400) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 04:16:26] (step=0922500) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 04:17:07] (step=0922600) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 04:17:47] (step=0922700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-19 04:18:27] (step=0922800) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 04:19:08] (step=0922900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 04:19:48] (step=0923000) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 04:20:29] (step=0923100) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 04:21:09] (step=0923200) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 04:21:49] (step=0923300) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-19 04:22:30] (step=0923400) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 04:23:10] (step=0923500) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 04:23:50] (step=0923600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:24:31] (step=0923700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 04:25:11] (step=0923800) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:25:51] (step=0923900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:26:32] (step=0924000) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:26:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 04:27:24] (step=0924100) Train Loss: 0.0666, Train Steps/Sec: 1.90, Samples/Sec: 68.55
+[2025-05-19 04:28:05] (step=0924200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 04:28:45] (step=0924300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 04:29:25] (step=0924400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:30:06] (step=0924500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 04:30:46] (step=0924600) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 04:31:26] (step=0924700) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 04:32:07] (step=0924800) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 04:32:47] (step=0924900) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:33:27] (step=0925000) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 04:34:08] (step=0925100) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 04:34:32] Beginning epoch 229...
+[2025-05-19 04:34:48] (step=0925200) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-19 04:35:29] (step=0925300) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 04:36:09] (step=0925400) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 04:36:49] (step=0925500) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 04:37:30] (step=0925600) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:38:10] (step=0925700) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 04:38:50] (step=0925800) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:39:31] (step=0925900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:40:11] (step=0926000) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:40:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 04:41:04] (step=0926100) Train Loss: 0.0723, Train Steps/Sec: 1.90, Samples/Sec: 68.39
+[2025-05-19 04:41:44] (step=0926200) Train Loss: 0.0728, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 04:42:25] (step=0926300) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:43:05] (step=0926400) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 04:43:45] (step=0926500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:44:26] (step=0926600) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 04:45:06] (step=0926700) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:45:46] (step=0926800) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 04:46:27] (step=0926900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:47:07] (step=0927000) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 04:47:47] (step=0927100) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:48:28] (step=0927200) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 04:49:08] (step=0927300) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 04:49:48] (step=0927400) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:50:29] (step=0927500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 04:51:09] (step=0927600) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 04:51:50] (step=0927700) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 04:52:30] (step=0927800) Train Loss: 0.0612, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:53:10] (step=0927900) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 04:53:51] (step=0928000) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 04:54:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 04:54:43] (step=0928100) Train Loss: 0.0683, Train Steps/Sec: 1.91, Samples/Sec: 68.80
+[2025-05-19 04:55:23] (step=0928200) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 04:56:04] (step=0928300) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 04:56:44] (step=0928400) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 04:57:24] (step=0928500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:58:05] (step=0928600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 04:58:45] (step=0928700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 04:59:26] (step=0928800) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 05:00:06] (step=0928900) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 05:00:46] (step=0929000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:01:27] (step=0929100) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 05:02:07] (step=0929200) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.39
+[2025-05-19 05:02:07] Beginning epoch 230...
+[2025-05-19 05:02:48] (step=0929300) Train Loss: 0.0703, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-19 05:03:28] (step=0929400) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 05:04:08] (step=0929500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 05:04:49] (step=0929600) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-19 05:05:29] (step=0929700) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 05:06:09] (step=0929800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 05:06:50] (step=0929900) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:07:30] (step=0930000) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 05:07:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 05:07:46] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 05:09:45] (step=0930000) Perceptual Loss: 0.2954, Eval Time: 122.26
+[2025-05-19 05:10:26] (step=0930100) Train Loss: 0.0639, Train Steps/Sec: 0.57, Samples/Sec: 20.51
+[2025-05-19 05:11:06] (step=0930200) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-19 05:11:47] (step=0930300) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 05:12:27] (step=0930400) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-19 05:13:07] (step=0930500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:13:48] (step=0930600) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 05:14:28] (step=0930700) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 05:15:08] (step=0930800) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 05:15:49] (step=0930900) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 05:16:29] (step=0931000) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-19 05:17:09] (step=0931100) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:17:50] (step=0931200) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 05:18:30] (step=0931300) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:19:10] (step=0931400) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 05:19:51] (step=0931500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:20:31] (step=0931600) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 05:21:12] (step=0931700) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 05:21:52] (step=0931800) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:22:32] (step=0931900) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 05:23:13] (step=0932000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:23:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 05:24:06] (step=0932100) Train Loss: 0.0654, Train Steps/Sec: 1.88, Samples/Sec: 67.74
+[2025-05-19 05:24:46] (step=0932200) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 05:25:27] (step=0932300) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 05:26:07] (step=0932400) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 05:26:47] (step=0932500) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 05:27:28] (step=0932600) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:28:08] (step=0932700) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 05:28:48] (step=0932800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 05:29:29] (step=0932900) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 05:30:09] (step=0933000) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 05:30:49] (step=0933100) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 05:31:30] (step=0933200) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 05:31:46] Beginning epoch 231...
+[2025-05-19 05:32:10] (step=0933300) Train Loss: 0.0687, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 05:32:51] (step=0933400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 05:33:31] (step=0933500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 05:34:11] (step=0933600) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-19 05:34:52] (step=0933700) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 05:35:32] (step=0933800) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 05:36:13] (step=0933900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:36:53] (step=0934000) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 05:37:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 05:37:46] (step=0934100) Train Loss: 0.0667, Train Steps/Sec: 1.89, Samples/Sec: 67.95
+[2025-05-19 05:38:26] (step=0934200) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 05:39:07] (step=0934300) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 05:39:47] (step=0934400) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:40:27] (step=0934500) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 05:41:08] (step=0934600) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 05:41:48] (step=0934700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 05:42:28] (step=0934800) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 05:43:09] (step=0934900) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 05:43:49] (step=0935000) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 05:44:29] (step=0935100) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 05:45:10] (step=0935200) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 05:45:50] (step=0935300) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 05:46:30] (step=0935400) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 05:47:11] (step=0935500) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 05:47:51] (step=0935600) Train Loss: 0.0594, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-19 05:48:32] (step=0935700) Train Loss: 0.0746, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 05:49:12] (step=0935800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 05:49:52] (step=0935900) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 05:50:33] (step=0936000) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 05:50:46] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 05:51:26] (step=0936100) Train Loss: 0.0665, Train Steps/Sec: 1.88, Samples/Sec: 67.52
+[2025-05-19 05:52:06] (step=0936200) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 05:52:47] (step=0936300) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 05:53:27] (step=0936400) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 05:54:07] (step=0936500) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 05:54:48] (step=0936600) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 05:55:28] (step=0936700) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 05:56:08] (step=0936800) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 05:56:49] (step=0936900) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 05:57:29] (step=0937000) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 05:58:10] (step=0937100) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 05:58:50] (step=0937200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 05:59:22] Beginning epoch 232...
+[2025-05-19 05:59:31] (step=0937300) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 06:00:11] (step=0937400) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 06:00:51] (step=0937500) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 06:01:32] (step=0937600) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-19 06:02:12] (step=0937700) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 06:02:52] (step=0937800) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 06:03:33] (step=0937900) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 06:04:13] (step=0938000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 06:04:26] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 06:05:06] (step=0938100) Train Loss: 0.0662, Train Steps/Sec: 1.89, Samples/Sec: 68.05
+[2025-05-19 06:05:46] (step=0938200) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 06:06:27] (step=0938300) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 06:07:07] (step=0938400) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 06:07:48] (step=0938500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:08:28] (step=0938600) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:09:08] (step=0938700) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:09:49] (step=0938800) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 06:10:29] (step=0938900) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 06:11:09] (step=0939000) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:11:50] (step=0939100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 06:12:30] (step=0939200) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 06:13:10] (step=0939300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:13:51] (step=0939400) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:14:31] (step=0939500) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 06:15:11] (step=0939600) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 06:15:52] (step=0939700) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 06:16:32] (step=0939800) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:17:13] (step=0939900) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:17:53] (step=0940000) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 06:18:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0940000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 06:18:21] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 06:20:21] (step=0940000) Perceptual Loss: 0.3166, Eval Time: 122.35
+[2025-05-19 06:21:01] (step=0940100) Train Loss: 0.0688, Train Steps/Sec: 0.53, Samples/Sec: 19.12
+[2025-05-19 06:21:41] (step=0940200) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 06:22:22] (step=0940300) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 06:23:02] (step=0940400) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 06:23:43] (step=0940500) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:24:23] (step=0940600) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:25:03] (step=0940700) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 06:25:44] (step=0940800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:26:24] (step=0940900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 06:27:04] (step=0941000) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 06:27:45] (step=0941100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 06:28:25] (step=0941200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 06:29:05] (step=0941300) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 06:29:13] Beginning epoch 233...
+[2025-05-19 06:29:46] (step=0941400) Train Loss: 0.0774, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-19 06:30:26] (step=0941500) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 06:31:07] (step=0941600) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 06:31:47] (step=0941700) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:32:27] (step=0941800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:33:08] (step=0941900) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:33:48] (step=0942000) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 06:34:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 06:34:41] (step=0942100) Train Loss: 0.0732, Train Steps/Sec: 1.87, Samples/Sec: 67.48
+[2025-05-19 06:35:22] (step=0942200) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 06:36:02] (step=0942300) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 06:36:42] (step=0942400) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:37:23] (step=0942500) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 06:38:03] (step=0942600) Train Loss: 0.0587, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:38:43] (step=0942700) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:39:24] (step=0942800) Train Loss: 0.0623, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 06:40:04] (step=0942900) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:40:45] (step=0943000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 06:41:25] (step=0943100) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:42:05] (step=0943200) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:42:46] (step=0943300) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:43:26] (step=0943400) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-19 06:44:06] (step=0943500) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 06:44:47] (step=0943600) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:45:27] (step=0943700) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 06:46:07] (step=0943800) Train Loss: 0.0618, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:46:48] (step=0943900) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:47:28] (step=0944000) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:47:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 06:48:20] (step=0944100) Train Loss: 0.0654, Train Steps/Sec: 1.91, Samples/Sec: 68.75
+[2025-05-19 06:49:01] (step=0944200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 06:49:41] (step=0944300) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 06:50:21] (step=0944400) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:51:02] (step=0944500) Train Loss: 0.0743, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 06:51:42] (step=0944600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 06:52:22] (step=0944700) Train Loss: 0.0745, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:53:03] (step=0944800) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 06:53:43] (step=0944900) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 06:54:24] (step=0945000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 06:55:04] (step=0945100) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 06:55:44] (step=0945200) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 06:56:24] (step=0945300) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 06:56:49] Beginning epoch 234...
+[2025-05-19 06:57:05] (step=0945400) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 06:57:46] (step=0945500) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 06:58:26] (step=0945600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 06:59:06] (step=0945700) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 06:59:47] (step=0945800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 07:00:27] (step=0945900) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:01:07] (step=0946000) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:01:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 07:02:00] (step=0946100) Train Loss: 0.0673, Train Steps/Sec: 1.88, Samples/Sec: 67.84
+[2025-05-19 07:02:41] (step=0946200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:03:21] (step=0946300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:04:01] (step=0946400) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 07:04:42] (step=0946500) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:05:22] (step=0946600) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:06:02] (step=0946700) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:06:43] (step=0946800) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 07:07:23] (step=0946900) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 07:08:03] (step=0947000) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 07:08:44] (step=0947100) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:09:24] (step=0947200) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 07:10:04] (step=0947300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:10:45] (step=0947400) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 07:11:25] (step=0947500) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 07:12:06] (step=0947600) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 07:12:46] (step=0947700) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 07:13:26] (step=0947800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 07:14:07] (step=0947900) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 07:14:47] (step=0948000) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 07:14:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 07:15:39] (step=0948100) Train Loss: 0.0723, Train Steps/Sec: 1.92, Samples/Sec: 68.99
+[2025-05-19 07:16:19] (step=0948200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 07:17:00] (step=0948300) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:17:40] (step=0948400) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 07:18:21] (step=0948500) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 07:19:01] (step=0948600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 07:19:41] (step=0948700) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 07:20:22] (step=0948800) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 07:21:02] (step=0948900) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 07:21:42] (step=0949000) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 07:22:23] (step=0949100) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 07:23:03] (step=0949200) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:23:43] (step=0949300) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 07:24:24] (step=0949400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.36
+[2025-05-19 07:24:24] Beginning epoch 235...
+[2025-05-19 07:25:04] (step=0949500) Train Loss: 0.0683, Train Steps/Sec: 2.45, Samples/Sec: 88.23
+[2025-05-19 07:25:45] (step=0949600) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 07:26:25] (step=0949700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 07:27:05] (step=0949800) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:27:46] (step=0949900) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:28:26] (step=0950000) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 07:28:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 07:28:41] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 07:30:41] (step=0950000) Perceptual Loss: 0.3043, Eval Time: 122.40
+[2025-05-19 07:31:21] (step=0950100) Train Loss: 0.0657, Train Steps/Sec: 0.57, Samples/Sec: 20.57
+[2025-05-19 07:32:01] (step=0950200) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 07:32:42] (step=0950300) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 07:33:22] (step=0950400) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 07:34:02] (step=0950500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 07:34:43] (step=0950600) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:35:23] (step=0950700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 07:36:04] (step=0950800) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 07:36:44] (step=0950900) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 07:37:24] (step=0951000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 07:38:05] (step=0951100) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:38:45] (step=0951200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 07:39:25] (step=0951300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:40:06] (step=0951400) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 07:40:46] (step=0951500) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 07:41:26] (step=0951600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 07:42:07] (step=0951700) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 07:42:47] (step=0951800) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:43:27] (step=0951900) Train Loss: 0.0747, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:44:08] (step=0952000) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:44:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 07:45:01] (step=0952100) Train Loss: 0.0657, Train Steps/Sec: 1.89, Samples/Sec: 67.87
+[2025-05-19 07:45:41] (step=0952200) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:46:21] (step=0952300) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 07:47:02] (step=0952400) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 07:47:42] (step=0952500) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:48:22] (step=0952600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:49:03] (step=0952700) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:49:43] (step=0952800) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 07:50:23] (step=0952900) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 07:51:04] (step=0953000) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 07:51:44] (step=0953100) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 07:52:24] (step=0953200) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 07:53:05] (step=0953300) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 07:53:45] (step=0953400) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 07:54:01] Beginning epoch 236...
+[2025-05-19 07:54:26] (step=0953500) Train Loss: 0.0697, Train Steps/Sec: 2.46, Samples/Sec: 88.58
+[2025-05-19 07:55:06] (step=0953600) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 07:55:46] (step=0953700) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 07:56:27] (step=0953800) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 07:57:07] (step=0953900) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 07:57:47] (step=0954000) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 07:58:00] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 07:58:40] (step=0954100) Train Loss: 0.0692, Train Steps/Sec: 1.89, Samples/Sec: 68.02
+[2025-05-19 07:59:21] (step=0954200) Train Loss: 0.0632, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 08:00:01] (step=0954300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 08:00:41] (step=0954400) Train Loss: 0.0607, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 08:01:22] (step=0954500) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:02:02] (step=0954600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:02:42] (step=0954700) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:03:23] (step=0954800) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 08:04:03] (step=0954900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 08:04:43] (step=0955000) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 08:05:24] (step=0955100) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 08:06:04] (step=0955200) Train Loss: 0.0738, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 08:06:44] (step=0955300) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 08:07:25] (step=0955400) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 08:08:05] (step=0955500) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 08:08:46] (step=0955600) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 08:09:26] (step=0955700) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 08:10:06] (step=0955800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 08:10:47] (step=0955900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 08:11:27] (step=0956000) Train Loss: 0.0734, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:11:39] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 08:12:19] (step=0956100) Train Loss: 0.0640, Train Steps/Sec: 1.91, Samples/Sec: 68.86
+[2025-05-19 08:12:59] (step=0956200) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:13:40] (step=0956300) Train Loss: 0.0714, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 08:14:20] (step=0956400) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 08:15:00] (step=0956500) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:15:41] (step=0956600) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:16:21] (step=0956700) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 08:17:02] (step=0956800) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 08:17:42] (step=0956900) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 08:18:22] (step=0957000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 08:19:03] (step=0957100) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 08:19:43] (step=0957200) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 08:20:23] (step=0957300) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 08:21:04] (step=0957400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 08:21:36] Beginning epoch 237...
+[2025-05-19 08:21:44] (step=0957500) Train Loss: 0.0682, Train Steps/Sec: 2.46, Samples/Sec: 88.40
+[2025-05-19 08:22:25] (step=0957600) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 08:23:05] (step=0957700) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 08:23:45] (step=0957800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 08:24:26] (step=0957900) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 08:25:06] (step=0958000) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 08:25:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 08:25:59] (step=0958100) Train Loss: 0.0681, Train Steps/Sec: 1.88, Samples/Sec: 67.64
+[2025-05-19 08:26:40] (step=0958200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 08:27:20] (step=0958300) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 08:28:00] (step=0958400) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 08:28:41] (step=0958500) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:29:21] (step=0958600) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:30:01] (step=0958700) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:30:42] (step=0958800) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 08:31:22] (step=0958900) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:32:02] (step=0959000) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 08:32:43] (step=0959100) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 08:33:23] (step=0959200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 08:34:03] (step=0959300) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:34:44] (step=0959400) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:35:24] (step=0959500) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 08:36:04] (step=0959600) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 08:36:45] (step=0959700) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 08:37:25] (step=0959800) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:38:05] (step=0959900) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 08:38:46] (step=0960000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 08:39:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0960000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 08:39:15] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 08:41:14] (step=0960000) Perceptual Loss: 0.3192, Eval Time: 122.69
+[2025-05-19 08:41:55] (step=0960100) Train Loss: 0.0725, Train Steps/Sec: 0.53, Samples/Sec: 19.05
+[2025-05-19 08:42:35] (step=0960200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 08:43:15] (step=0960300) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 08:43:56] (step=0960400) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:44:36] (step=0960500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 08:45:16] (step=0960600) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 08:45:57] (step=0960700) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-19 08:46:37] (step=0960800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 08:47:17] (step=0960900) Train Loss: 0.0722, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:47:58] (step=0961000) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 08:48:38] (step=0961100) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:49:18] (step=0961200) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:49:59] (step=0961300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:50:39] (step=0961400) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 08:51:19] (step=0961500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 08:51:27] Beginning epoch 238...
+[2025-05-19 08:52:00] (step=0961600) Train Loss: 0.0659, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-19 08:52:40] (step=0961700) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 08:53:21] (step=0961800) Train Loss: 0.0728, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:54:01] (step=0961900) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 08:54:41] (step=0962000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:54:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 08:55:35] (step=0962100) Train Loss: 0.0644, Train Steps/Sec: 1.87, Samples/Sec: 67.35
+[2025-05-19 08:56:15] (step=0962200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:56:55] (step=0962300) Train Loss: 0.0614, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 08:57:36] (step=0962400) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 08:58:16] (step=0962500) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 08:58:56] (step=0962600) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 08:59:37] (step=0962700) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 09:00:17] (step=0962800) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 09:00:57] (step=0962900) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 09:01:38] (step=0963000) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 09:02:18] (step=0963100) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 09:02:58] (step=0963200) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 09:03:39] (step=0963300) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 09:04:19] (step=0963400) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-19 09:05:00] (step=0963500) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 09:05:40] (step=0963600) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 09:06:20] (step=0963700) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-19 09:07:01] (step=0963800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 09:07:41] (step=0963900) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 09:08:21] (step=0964000) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-19 09:08:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 09:09:14] (step=0964100) Train Loss: 0.0619, Train Steps/Sec: 1.88, Samples/Sec: 67.60
+[2025-05-19 09:09:55] (step=0964200) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 09:10:35] (step=0964300) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 09:11:15] (step=0964400) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 09:11:56] (step=0964500) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 09:12:36] (step=0964600) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 09:13:16] (step=0964700) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 09:13:57] (step=0964800) Train Loss: 0.0612, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 09:14:37] (step=0964900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 09:15:17] (step=0965000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 09:15:58] (step=0965100) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 09:16:38] (step=0965200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 09:17:18] (step=0965300) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 09:17:59] (step=0965400) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 09:18:39] (step=0965500) Train Loss: 0.0727, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 09:19:03] Beginning epoch 239...
+[2025-05-19 09:19:20] (step=0965600) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-19 09:20:00] (step=0965700) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 09:20:41] (step=0965800) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-19 09:21:21] (step=0965900) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 09:22:01] (step=0966000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-19 09:22:14] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 09:22:54] (step=0966100) Train Loss: 0.0684, Train Steps/Sec: 1.88, Samples/Sec: 67.85
+[2025-05-19 09:23:35] (step=0966200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-19 09:24:15] (step=0966300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-19 09:24:55] (step=0966400) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 09:25:36] (step=0966500) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 09:26:16] (step=0966600) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-19 09:26:56] (step=0966700) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 09:27:37] (step=0966800) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 09:28:17] (step=0966900) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 09:28:57] (step=0967000) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 09:29:38] (step=0967100) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 09:30:18] (step=0967200) Train Loss: 0.0613, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 09:30:58] (step=0967300) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 09:31:39] (step=0967400) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 09:32:19] (step=0967500) Train Loss: 0.0745, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 09:32:59] (step=0967600) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 09:33:40] (step=0967700) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 09:34:20] (step=0967800) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 09:35:01] (step=0967900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 09:35:41] (step=0968000) Train Loss: 0.0663, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 09:35:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 09:36:34] (step=0968100) Train Loss: 0.0726, Train Steps/Sec: 1.88, Samples/Sec: 67.83
+[2025-05-19 09:37:14] (step=0968200) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 09:37:55] (step=0968300) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 09:38:35] (step=0968400) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-19 09:39:15] (step=0968500) Train Loss: 0.0723, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 09:39:56] (step=0968600) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 09:40:36] (step=0968700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 09:41:17] (step=0968800) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-19 09:41:57] (step=0968900) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 09:42:37] (step=0969000) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 09:43:18] (step=0969100) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 09:43:58] (step=0969200) Train Loss: 0.0727, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 09:44:38] (step=0969300) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 09:45:19] (step=0969400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 09:45:59] (step=0969500) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 09:46:40] (step=0969600) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 09:46:40] Beginning epoch 240...
+[2025-05-19 09:47:20] (step=0969700) Train Loss: 0.0683, Train Steps/Sec: 2.46, Samples/Sec: 88.46
+[2025-05-19 09:48:01] (step=0969800) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 09:48:41] (step=0969900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 09:49:21] (step=0970000) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 09:49:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 09:49:37] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 09:51:37] (step=0970000) Perceptual Loss: 0.3214, Eval Time: 122.56
+[2025-05-19 09:52:17] (step=0970100) Train Loss: 0.0651, Train Steps/Sec: 0.57, Samples/Sec: 20.44
+[2025-05-19 09:52:58] (step=0970200) Train Loss: 0.0721, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-19 09:53:38] (step=0970300) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 09:54:19] (step=0970400) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 09:54:59] (step=0970500) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 09:55:39] (step=0970600) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 09:56:20] (step=0970700) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 09:57:00] (step=0970800) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 09:57:40] (step=0970900) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-19 09:58:21] (step=0971000) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-19 09:59:01] (step=0971100) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 09:59:41] (step=0971200) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-19 10:00:22] (step=0971300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 10:01:02] (step=0971400) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 10:01:42] (step=0971500) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 10:02:23] (step=0971600) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 10:03:03] (step=0971700) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 10:03:44] (step=0971800) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-19 10:04:24] (step=0971900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-19 10:05:04] (step=0972000) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-19 10:05:17] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 10:05:57] (step=0972100) Train Loss: 0.0675, Train Steps/Sec: 1.88, Samples/Sec: 67.53
+[2025-05-19 10:06:38] (step=0972200) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-19 10:07:18] (step=0972300) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-19 10:07:59] (step=0972400) Train Loss: 0.0745, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 10:08:39] (step=0972500) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 10:09:19] (step=0972600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-19 10:10:00] (step=0972700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 10:10:40] (step=0972800) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-19 10:11:21] (step=0972900) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 10:12:01] (step=0973000) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 10:12:42] (step=0973100) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-19 10:13:22] (step=0973200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-19 10:14:02] (step=0973300) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 10:14:43] (step=0973400) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-19 10:15:23] (step=0973500) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 10:16:03] (step=0973600) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 10:16:20] Beginning epoch 241...
+[2025-05-19 10:16:44] (step=0973700) Train Loss: 0.0697, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 10:17:24] (step=0973800) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 10:18:05] (step=0973900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 10:18:45] (step=0974000) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 10:18:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 10:19:38] (step=0974100) Train Loss: 0.0628, Train Steps/Sec: 1.88, Samples/Sec: 67.70
+[2025-05-19 10:20:19] (step=0974200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-19 10:20:59] (step=0974300) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 10:21:39] (step=0974400) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-19 10:22:20] (step=0974500) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-19 10:23:00] (step=0974600) Train Loss: 0.0742, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 10:23:40] (step=0974700) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-19 10:24:21] (step=0974800) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-19 10:25:01] (step=0974900) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 10:25:42] (step=0975000) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-19 10:26:22] (step=0975100) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-19 10:27:02] (step=0975200) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-19 10:27:43] (step=0975300) Train Loss: 0.0718, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-19 10:28:23] (step=0975400) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-19 10:29:03] (step=0975500) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-19 10:29:44] (step=0975600) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-19 10:30:24] (step=0975700) Train Loss: 0.0717, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-19 10:31:05] (step=0975800) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-19 10:31:45] (step=0975900) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-19 10:32:26] (step=0976000) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 10:32:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 10:33:19] (step=0976100) Train Loss: 0.0665, Train Steps/Sec: 1.88, Samples/Sec: 67.59
+[2025-05-19 10:33:59] (step=0976200) Train Loss: 0.0605, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 10:34:40] (step=0976300) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 10:35:21] (step=0976400) Train Loss: 0.0691, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 10:36:01] (step=0976500) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 10:36:42] (step=0976600) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 10:37:22] (step=0976700) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 10:38:03] (step=0976800) Train Loss: 0.0764, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 10:38:43] (step=0976900) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 10:39:24] (step=0977000) Train Loss: 0.0700, Train Steps/Sec: 2.46, Samples/Sec: 88.54
+[2025-05-19 10:40:04] (step=0977100) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 10:40:45] (step=0977200) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 10:41:25] (step=0977300) Train Loss: 0.0729, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 10:42:06] (step=0977400) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 10:42:46] (step=0977500) Train Loss: 0.0703, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 10:43:27] (step=0977600) Train Loss: 0.0639, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-19 10:44:00] Beginning epoch 242...
+[2025-05-19 10:44:08] (step=0977700) Train Loss: 0.0657, Train Steps/Sec: 2.44, Samples/Sec: 87.93
+[2025-05-19 10:44:49] (step=0977800) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 10:45:29] (step=0977900) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 10:46:10] (step=0978000) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 10:46:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 10:47:03] (step=0978100) Train Loss: 0.0642, Train Steps/Sec: 1.89, Samples/Sec: 67.94
+[2025-05-19 10:47:43] (step=0978200) Train Loss: 0.0689, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 10:48:24] (step=0978300) Train Loss: 0.0673, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 10:49:04] (step=0978400) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 10:49:45] (step=0978500) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 10:50:25] (step=0978600) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 10:51:06] (step=0978700) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 10:51:46] (step=0978800) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 10:52:27] (step=0978900) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 10:53:07] (step=0979000) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 10:53:48] (step=0979100) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 10:54:28] (step=0979200) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 10:55:09] (step=0979300) Train Loss: 0.0746, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 10:55:49] (step=0979400) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 10:56:30] (step=0979500) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 10:57:10] (step=0979600) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 10:57:51] (step=0979700) Train Loss: 0.0689, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-19 10:58:31] (step=0979800) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 10:59:12] (step=0979900) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 10:59:52] (step=0980000) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 11:00:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/0980000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 11:00:23] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 11:02:23] (step=0980000) Perceptual Loss: 0.3199, Eval Time: 123.01
+[2025-05-19 11:03:04] (step=0980100) Train Loss: 0.0658, Train Steps/Sec: 0.52, Samples/Sec: 18.81
+[2025-05-19 11:03:44] (step=0980200) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 11:04:25] (step=0980300) Train Loss: 0.0625, Train Steps/Sec: 2.46, Samples/Sec: 88.40
+[2025-05-19 11:05:05] (step=0980400) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 11:05:46] (step=0980500) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 11:06:26] (step=0980600) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:07:07] (step=0980700) Train Loss: 0.0691, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 11:07:48] (step=0980800) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 11:08:28] (step=0980900) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 11:09:09] (step=0981000) Train Loss: 0.0644, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-19 11:09:49] (step=0981100) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 11:10:30] (step=0981200) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 11:11:10] (step=0981300) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 11:11:51] (step=0981400) Train Loss: 0.0706, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 11:12:31] (step=0981500) Train Loss: 0.0614, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 11:13:12] (step=0981600) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 11:13:53] (step=0981700) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 11:14:01] Beginning epoch 243...
+[2025-05-19 11:14:33] (step=0981800) Train Loss: 0.0665, Train Steps/Sec: 2.45, Samples/Sec: 88.12
+[2025-05-19 11:15:14] (step=0981900) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:15:54] (step=0982000) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 11:16:07] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 11:16:48] (step=0982100) Train Loss: 0.0671, Train Steps/Sec: 1.87, Samples/Sec: 67.37
+[2025-05-19 11:17:28] (step=0982200) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 11:18:09] (step=0982300) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.51
+[2025-05-19 11:18:50] (step=0982400) Train Loss: 0.0737, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:19:30] (step=0982500) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:20:11] (step=0982600) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 11:20:51] (step=0982700) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 11:21:32] (step=0982800) Train Loss: 0.0655, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 11:22:12] (step=0982900) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 11:22:53] (step=0983000) Train Loss: 0.0708, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 11:23:33] (step=0983100) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 11:24:14] (step=0983200) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 11:24:54] (step=0983300) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 11:25:35] (step=0983400) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 11:26:15] (step=0983500) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 11:26:56] (step=0983600) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:27:36] (step=0983700) Train Loss: 0.0653, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-19 11:28:17] (step=0983800) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 11:28:57] (step=0983900) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 11:29:38] (step=0984000) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 11:29:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 11:30:32] (step=0984100) Train Loss: 0.0661, Train Steps/Sec: 1.86, Samples/Sec: 66.93
+[2025-05-19 11:31:12] (step=0984200) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:31:53] (step=0984300) Train Loss: 0.0634, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-19 11:32:33] (step=0984400) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 11:33:14] (step=0984500) Train Loss: 0.0720, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 11:33:54] (step=0984600) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 11:34:35] (step=0984700) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 11:35:15] (step=0984800) Train Loss: 0.0623, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-19 11:35:56] (step=0984900) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 11:36:37] (step=0985000) Train Loss: 0.0688, Train Steps/Sec: 2.46, Samples/Sec: 88.48
+[2025-05-19 11:37:17] (step=0985100) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 11:37:58] (step=0985200) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 11:38:38] (step=0985300) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 11:39:19] (step=0985400) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 11:39:59] (step=0985500) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 11:40:40] (step=0985600) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 11:41:20] (step=0985700) Train Loss: 0.0642, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-19 11:41:45] Beginning epoch 244...
+[2025-05-19 11:42:01] (step=0985800) Train Loss: 0.0650, Train Steps/Sec: 2.45, Samples/Sec: 88.18
+[2025-05-19 11:42:42] (step=0985900) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:43:22] (step=0986000) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 11:43:35] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 11:44:15] (step=0986100) Train Loss: 0.0674, Train Steps/Sec: 1.88, Samples/Sec: 67.75
+[2025-05-19 11:44:56] (step=0986200) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 11:45:36] (step=0986300) Train Loss: 0.0662, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 11:46:17] (step=0986400) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 11:46:57] (step=0986500) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:47:38] (step=0986600) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 11:48:18] (step=0986700) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 11:48:59] (step=0986800) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 11:49:40] (step=0986900) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 11:50:20] (step=0987000) Train Loss: 0.0673, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-19 11:51:01] (step=0987100) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 11:51:41] (step=0987200) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 11:52:22] (step=0987300) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 11:53:02] (step=0987400) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 11:53:43] (step=0987500) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 11:54:23] (step=0987600) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 11:55:04] (step=0987700) Train Loss: 0.0697, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-19 11:55:44] (step=0987800) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 11:56:25] (step=0987900) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 11:57:05] (step=0988000) Train Loss: 0.0605, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 11:57:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 11:57:58] (step=0988100) Train Loss: 0.0660, Train Steps/Sec: 1.89, Samples/Sec: 68.17
+[2025-05-19 11:58:39] (step=0988200) Train Loss: 0.0634, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 11:59:19] (step=0988300) Train Loss: 0.0654, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 12:00:00] (step=0988400) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 12:00:40] (step=0988500) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 12:01:21] (step=0988600) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:02:01] (step=0988700) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:02:42] (step=0988800) Train Loss: 0.0613, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 12:03:22] (step=0988900) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:04:03] (step=0989000) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 12:04:43] (step=0989100) Train Loss: 0.0653, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 12:05:24] (step=0989200) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 12:06:04] (step=0989300) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:06:45] (step=0989400) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 12:07:26] (step=0989500) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-19 12:08:06] (step=0989600) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 12:08:47] (step=0989700) Train Loss: 0.0650, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-19 12:09:27] (step=0989800) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 12:09:27] Beginning epoch 245...
+[2025-05-19 12:10:08] (step=0989900) Train Loss: 0.0632, Train Steps/Sec: 2.45, Samples/Sec: 88.25
+[2025-05-19 12:10:48] (step=0990000) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 12:11:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 12:11:04] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 12:13:04] (step=0990000) Perceptual Loss: 0.2995, Eval Time: 122.48
+[2025-05-19 12:13:44] (step=0990100) Train Loss: 0.0643, Train Steps/Sec: 0.57, Samples/Sec: 20.45
+[2025-05-19 12:14:25] (step=0990200) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 12:15:06] (step=0990300) Train Loss: 0.0623, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 12:15:46] (step=0990400) Train Loss: 0.0680, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-19 12:16:27] (step=0990500) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 12:17:07] (step=0990600) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 12:17:48] (step=0990700) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 12:18:28] (step=0990800) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:19:09] (step=0990900) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 12:19:49] (step=0991000) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 12:20:30] (step=0991100) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 12:21:10] (step=0991200) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 12:21:51] (step=0991300) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 12:22:31] (step=0991400) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 12:23:12] (step=0991500) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 12:23:52] (step=0991600) Train Loss: 0.0716, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 12:24:33] (step=0991700) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:25:13] (step=0991800) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 12:25:54] (step=0991900) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 12:26:34] (step=0992000) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 12:26:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 12:27:28] (step=0992100) Train Loss: 0.0631, Train Steps/Sec: 1.86, Samples/Sec: 66.98
+[2025-05-19 12:28:09] (step=0992200) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 12:28:49] (step=0992300) Train Loss: 0.0614, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 12:29:30] (step=0992400) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 12:30:10] (step=0992500) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:30:51] (step=0992600) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 12:31:31] (step=0992700) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 12:32:12] (step=0992800) Train Loss: 0.0734, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-19 12:32:52] (step=0992900) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:33:33] (step=0993000) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 12:34:13] (step=0993100) Train Loss: 0.0705, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 12:34:54] (step=0993200) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 12:35:34] (step=0993300) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 12:36:15] (step=0993400) Train Loss: 0.0704, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 12:36:55] (step=0993500) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 12:37:36] (step=0993600) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:38:16] (step=0993700) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 12:38:57] (step=0993800) Train Loss: 0.0712, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 12:39:13] Beginning epoch 246...
+[2025-05-19 12:39:38] (step=0993900) Train Loss: 0.0644, Train Steps/Sec: 2.45, Samples/Sec: 88.13
+[2025-05-19 12:40:18] (step=0994000) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 12:40:31] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 12:41:11] (step=0994100) Train Loss: 0.0677, Train Steps/Sec: 1.89, Samples/Sec: 67.91
+[2025-05-19 12:41:52] (step=0994200) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:42:32] (step=0994300) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 12:43:13] (step=0994400) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-19 12:43:54] (step=0994500) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:44:34] (step=0994600) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 12:45:15] (step=0994700) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 12:45:55] (step=0994800) Train Loss: 0.0682, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 12:46:36] (step=0994900) Train Loss: 0.0619, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 12:47:16] (step=0995000) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 12:47:57] (step=0995100) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-19 12:48:37] (step=0995200) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:49:18] (step=0995300) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 12:49:58] (step=0995400) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 12:50:39] (step=0995500) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 12:51:19] (step=0995600) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 12:52:00] (step=0995700) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 12:52:40] (step=0995800) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 12:53:21] (step=0995900) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 12:54:01] (step=0996000) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 12:54:14] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 12:54:54] (step=0996100) Train Loss: 0.0680, Train Steps/Sec: 1.88, Samples/Sec: 67.80
+[2025-05-19 12:55:35] (step=0996200) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 12:56:15] (step=0996300) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 12:56:56] (step=0996400) Train Loss: 0.0663, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 12:57:37] (step=0996500) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 12:58:17] (step=0996600) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 12:58:58] (step=0996700) Train Loss: 0.0602, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 12:59:38] (step=0996800) Train Loss: 0.0652, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 13:00:19] (step=0996900) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 13:00:59] (step=0997000) Train Loss: 0.0597, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 13:01:40] (step=0997100) Train Loss: 0.0687, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 13:02:20] (step=0997200) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 13:03:01] (step=0997300) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 13:03:41] (step=0997400) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 13:04:22] (step=0997500) Train Loss: 0.0642, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 13:05:02] (step=0997600) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 13:05:43] (step=0997700) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 13:06:23] (step=0997800) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 13:06:56] Beginning epoch 247...
+[2025-05-19 13:07:04] (step=0997900) Train Loss: 0.0630, Train Steps/Sec: 2.45, Samples/Sec: 88.14
+[2025-05-19 13:07:45] (step=0998000) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 13:07:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 13:08:38] (step=0998100) Train Loss: 0.0649, Train Steps/Sec: 1.87, Samples/Sec: 67.25
+[2025-05-19 13:09:19] (step=0998200) Train Loss: 0.0714, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 13:09:59] (step=0998300) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 13:10:40] (step=0998400) Train Loss: 0.0610, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-19 13:11:21] (step=0998500) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 13:12:01] (step=0998600) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 13:12:42] (step=0998700) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 13:13:22] (step=0998800) Train Loss: 0.0646, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 13:14:03] (step=0998900) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 13:14:43] (step=0999000) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 13:15:24] (step=0999100) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-19 13:16:04] (step=0999200) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 13:16:45] (step=0999300) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 13:17:25] (step=0999400) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 13:18:06] (step=0999500) Train Loss: 0.0732, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 13:18:46] (step=0999600) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 13:19:27] (step=0999700) Train Loss: 0.0692, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 13:20:08] (step=0999800) Train Loss: 0.0670, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 13:20:48] (step=0999900) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 13:21:29] (step=1000000) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 13:21:56] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1000000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 13:21:59] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 13:23:59] (step=1000000) Perceptual Loss: 0.3109, Eval Time: 123.02
+[2025-05-19 13:24:40] (step=1000100) Train Loss: 0.0698, Train Steps/Sec: 0.52, Samples/Sec: 18.83
+[2025-05-19 13:25:20] (step=1000200) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 13:26:01] (step=1000300) Train Loss: 0.0643, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 13:26:42] (step=1000400) Train Loss: 0.0669, Train Steps/Sec: 2.46, Samples/Sec: 88.63
+[2025-05-19 13:27:22] (step=1000500) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 13:28:03] (step=1000600) Train Loss: 0.0654, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 13:28:43] (step=1000700) Train Loss: 0.0662, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 13:29:24] (step=1000800) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 13:30:04] (step=1000900) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 13:30:45] (step=1001000) Train Loss: 0.0738, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 13:31:26] (step=1001100) Train Loss: 0.0668, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-19 13:32:06] (step=1001200) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 13:32:47] (step=1001300) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 13:33:27] (step=1001400) Train Loss: 0.0650, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 13:34:08] (step=1001500) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 13:34:48] (step=1001600) Train Loss: 0.0670, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 13:35:29] (step=1001700) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 13:36:09] (step=1001800) Train Loss: 0.0668, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 13:36:50] (step=1001900) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 13:36:58] Beginning epoch 248...
+[2025-05-19 13:37:31] (step=1002000) Train Loss: 0.0683, Train Steps/Sec: 2.45, Samples/Sec: 88.22
+[2025-05-19 13:37:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 13:38:24] (step=1002100) Train Loss: 0.0684, Train Steps/Sec: 1.89, Samples/Sec: 67.94
+[2025-05-19 13:39:04] (step=1002200) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 13:39:45] (step=1002300) Train Loss: 0.0690, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 13:40:25] (step=1002400) Train Loss: 0.0614, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 13:41:06] (step=1002500) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 13:41:46] (step=1002600) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 13:42:27] (step=1002700) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 13:43:07] (step=1002800) Train Loss: 0.0640, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 13:43:48] (step=1002900) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 13:44:29] (step=1003000) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 13:45:09] (step=1003100) Train Loss: 0.0684, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 13:45:50] (step=1003200) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 13:46:30] (step=1003300) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 13:47:11] (step=1003400) Train Loss: 0.0701, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 13:47:51] (step=1003500) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 13:48:32] (step=1003600) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 13:49:12] (step=1003700) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 13:49:53] (step=1003800) Train Loss: 0.0702, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 13:50:33] (step=1003900) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 13:51:14] (step=1004000) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 13:51:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 13:52:08] (step=1004100) Train Loss: 0.0628, Train Steps/Sec: 1.86, Samples/Sec: 66.79
+[2025-05-19 13:52:48] (step=1004200) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 13:53:29] (step=1004300) Train Loss: 0.0669, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 13:54:09] (step=1004400) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 13:54:50] (step=1004500) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 13:55:30] (step=1004600) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 13:56:11] (step=1004700) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 13:56:52] (step=1004800) Train Loss: 0.0730, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 13:57:32] (step=1004900) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 13:58:13] (step=1005000) Train Loss: 0.0705, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 13:58:53] (step=1005100) Train Loss: 0.0679, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 13:59:34] (step=1005200) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 14:00:14] (step=1005300) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 14:00:55] (step=1005400) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 14:01:35] (step=1005500) Train Loss: 0.0694, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-19 14:02:16] (step=1005600) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 14:02:57] (step=1005700) Train Loss: 0.0664, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 14:03:37] (step=1005800) Train Loss: 0.0704, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-19 14:04:18] (step=1005900) Train Loss: 0.0616, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 14:04:42] Beginning epoch 249...
+[2025-05-19 14:04:58] (step=1006000) Train Loss: 0.0667, Train Steps/Sec: 2.45, Samples/Sec: 88.24
+[2025-05-19 14:05:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 14:05:52] (step=1006100) Train Loss: 0.0639, Train Steps/Sec: 1.88, Samples/Sec: 67.85
+[2025-05-19 14:06:32] (step=1006200) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 14:07:13] (step=1006300) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 14:07:53] (step=1006400) Train Loss: 0.0713, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 14:08:34] (step=1006500) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 14:09:14] (step=1006600) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 14:09:55] (step=1006700) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 14:10:35] (step=1006800) Train Loss: 0.0639, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 14:11:16] (step=1006900) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 14:11:56] (step=1007000) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 14:12:37] (step=1007100) Train Loss: 0.0692, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 14:13:17] (step=1007200) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 14:13:58] (step=1007300) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 14:14:38] (step=1007400) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 14:15:19] (step=1007500) Train Loss: 0.0645, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-19 14:16:00] (step=1007600) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 14:16:40] (step=1007700) Train Loss: 0.0749, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 14:17:21] (step=1007800) Train Loss: 0.0713, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 14:18:01] (step=1007900) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 14:18:42] (step=1008000) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 14:18:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 14:19:34] (step=1008100) Train Loss: 0.0688, Train Steps/Sec: 1.90, Samples/Sec: 68.27
+[2025-05-19 14:20:15] (step=1008200) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 14:20:56] (step=1008300) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 14:21:36] (step=1008400) Train Loss: 0.0679, Train Steps/Sec: 2.46, Samples/Sec: 88.48
+[2025-05-19 14:22:17] (step=1008500) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 14:22:57] (step=1008600) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 14:23:38] (step=1008700) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 14:24:18] (step=1008800) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 14:24:59] (step=1008900) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 14:25:39] (step=1009000) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 14:26:20] (step=1009100) Train Loss: 0.0611, Train Steps/Sec: 2.46, Samples/Sec: 88.47
+[2025-05-19 14:27:00] (step=1009200) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 14:27:41] (step=1009300) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 14:28:21] (step=1009400) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 14:29:02] (step=1009500) Train Loss: 0.0663, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 14:29:43] (step=1009600) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 14:30:23] (step=1009700) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 14:31:04] (step=1009800) Train Loss: 0.0654, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-19 14:31:44] (step=1009900) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 14:32:25] (step=1010000) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 14:32:37] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 14:32:40] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 14:34:40] (step=1010000) Perceptual Loss: 0.2973, Eval Time: 122.88
+[2025-05-19 14:34:40] Beginning epoch 250...
+[2025-05-19 14:35:21] (step=1010100) Train Loss: 0.0652, Train Steps/Sec: 0.57, Samples/Sec: 20.41
+[2025-05-19 14:36:02] (step=1010200) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 14:36:42] (step=1010300) Train Loss: 0.0660, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 14:37:23] (step=1010400) Train Loss: 0.0706, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-19 14:38:03] (step=1010500) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 14:38:44] (step=1010600) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 14:39:24] (step=1010700) Train Loss: 0.0715, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 14:40:05] (step=1010800) Train Loss: 0.0735, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 14:40:45] (step=1010900) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 14:41:26] (step=1011000) Train Loss: 0.0722, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 14:42:07] (step=1011100) Train Loss: 0.0680, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-19 14:42:47] (step=1011200) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 14:43:28] (step=1011300) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 14:44:08] (step=1011400) Train Loss: 0.0605, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-19 14:44:49] (step=1011500) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 14:45:29] (step=1011600) Train Loss: 0.0737, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 14:46:10] (step=1011700) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 14:46:51] (step=1011800) Train Loss: 0.0665, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 14:47:31] (step=1011900) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 14:48:12] (step=1012000) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 14:48:24] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 14:49:04] (step=1012100) Train Loss: 0.0667, Train Steps/Sec: 1.89, Samples/Sec: 68.00
+[2025-05-19 14:49:45] (step=1012200) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 14:50:26] (step=1012300) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 14:51:06] (step=1012400) Train Loss: 0.0660, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 14:51:47] (step=1012500) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 14:52:27] (step=1012600) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 14:53:08] (step=1012700) Train Loss: 0.0740, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 14:53:48] (step=1012800) Train Loss: 0.0686, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 14:54:29] (step=1012900) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 14:55:09] (step=1013000) Train Loss: 0.0606, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 14:55:50] (step=1013100) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.63
+[2025-05-19 14:56:30] (step=1013200) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 14:57:11] (step=1013300) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 14:57:52] (step=1013400) Train Loss: 0.0650, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 14:58:32] (step=1013500) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 14:59:12] (step=1013600) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 14:59:53] (step=1013700) Train Loss: 0.0650, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 15:00:34] (step=1013800) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 15:01:14] (step=1013900) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 15:01:55] (step=1014000) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 15:02:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 15:02:24] Beginning epoch 251...
+[2025-05-19 15:02:49] (step=1014100) Train Loss: 0.0676, Train Steps/Sec: 1.84, Samples/Sec: 66.32
+[2025-05-19 15:03:29] (step=1014200) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 15:04:10] (step=1014300) Train Loss: 0.0699, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 15:04:51] (step=1014400) Train Loss: 0.0619, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 15:05:31] (step=1014500) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 15:06:12] (step=1014600) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 15:06:52] (step=1014700) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 15:07:33] (step=1014800) Train Loss: 0.0666, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 15:08:13] (step=1014900) Train Loss: 0.0731, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 15:08:54] (step=1015000) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 15:09:34] (step=1015100) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 15:10:15] (step=1015200) Train Loss: 0.0710, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-19 15:10:55] (step=1015300) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 15:11:36] (step=1015400) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 15:12:16] (step=1015500) Train Loss: 0.0748, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 15:12:57] (step=1015600) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 15:13:37] (step=1015700) Train Loss: 0.0632, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 15:14:18] (step=1015800) Train Loss: 0.0673, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 15:14:59] (step=1015900) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 15:15:39] (step=1016000) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 15:15:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 15:16:32] (step=1016100) Train Loss: 0.0693, Train Steps/Sec: 1.88, Samples/Sec: 67.81
+[2025-05-19 15:17:13] (step=1016200) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 15:17:53] (step=1016300) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 15:18:34] (step=1016400) Train Loss: 0.0700, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 15:19:14] (step=1016500) Train Loss: 0.0729, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 15:19:55] (step=1016600) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 15:20:35] (step=1016700) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 15:21:16] (step=1016800) Train Loss: 0.0682, Train Steps/Sec: 2.46, Samples/Sec: 88.63
+[2025-05-19 15:21:57] (step=1016900) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 15:22:37] (step=1017000) Train Loss: 0.0647, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 15:23:18] (step=1017100) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 15:23:58] (step=1017200) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 15:24:39] (step=1017300) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 15:25:19] (step=1017400) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 15:26:00] (step=1017500) Train Loss: 0.0678, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 15:26:40] (step=1017600) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 15:27:21] (step=1017700) Train Loss: 0.0699, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 15:28:01] (step=1017800) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 15:28:42] (step=1017900) Train Loss: 0.0638, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 15:29:22] (step=1018000) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 15:29:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 15:30:08] Beginning epoch 252...
+[2025-05-19 15:30:16] (step=1018100) Train Loss: 0.0643, Train Steps/Sec: 1.85, Samples/Sec: 66.75
+[2025-05-19 15:30:57] (step=1018200) Train Loss: 0.0714, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 15:31:37] (step=1018300) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 15:32:18] (step=1018400) Train Loss: 0.0684, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 15:32:59] (step=1018500) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 15:33:39] (step=1018600) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 15:34:20] (step=1018700) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 15:35:00] (step=1018800) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 15:35:41] (step=1018900) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 15:36:21] (step=1019000) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 15:37:02] (step=1019100) Train Loss: 0.0685, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 15:37:42] (step=1019200) Train Loss: 0.0654, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 15:38:23] (step=1019300) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 15:39:03] (step=1019400) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-19 15:39:44] (step=1019500) Train Loss: 0.0649, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 15:40:24] (step=1019600) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 15:41:05] (step=1019700) Train Loss: 0.0620, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 15:41:46] (step=1019800) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 15:42:26] (step=1019900) Train Loss: 0.0646, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 15:43:07] (step=1020000) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 15:43:34] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1020000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 15:43:37] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 15:45:37] (step=1020000) Perceptual Loss: 0.3171, Eval Time: 123.19
+[2025-05-19 15:46:17] (step=1020100) Train Loss: 0.0706, Train Steps/Sec: 0.52, Samples/Sec: 18.88
+[2025-05-19 15:46:58] (step=1020200) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 15:47:38] (step=1020300) Train Loss: 0.0691, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 15:48:19] (step=1020400) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 15:49:00] (step=1020500) Train Loss: 0.0701, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-19 15:49:40] (step=1020600) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 15:50:21] (step=1020700) Train Loss: 0.0747, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 15:51:01] (step=1020800) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 15:51:42] (step=1020900) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 15:52:22] (step=1021000) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 15:53:03] (step=1021100) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 15:53:43] (step=1021200) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-19 15:54:24] (step=1021300) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 15:55:05] (step=1021400) Train Loss: 0.0692, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 15:55:45] (step=1021500) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 15:56:26] (step=1021600) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 15:57:06] (step=1021700) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 15:57:47] (step=1021800) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 15:58:27] (step=1021900) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-19 15:59:08] (step=1022000) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 15:59:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 16:00:01] (step=1022100) Train Loss: 0.0698, Train Steps/Sec: 1.88, Samples/Sec: 67.78
+[2025-05-19 16:00:09] Beginning epoch 253...
+[2025-05-19 16:00:42] (step=1022200) Train Loss: 0.0691, Train Steps/Sec: 2.45, Samples/Sec: 88.16
+[2025-05-19 16:01:22] (step=1022300) Train Loss: 0.0636, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 16:02:03] (step=1022400) Train Loss: 0.0741, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 16:02:43] (step=1022500) Train Loss: 0.0682, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 16:03:24] (step=1022600) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 16:04:04] (step=1022700) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 16:04:45] (step=1022800) Train Loss: 0.0679, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 16:05:25] (step=1022900) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 16:06:06] (step=1023000) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 16:06:47] (step=1023100) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 16:07:27] (step=1023200) Train Loss: 0.0678, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 16:08:08] (step=1023300) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 16:08:48] (step=1023400) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 16:09:29] (step=1023500) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 16:10:09] (step=1023600) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 16:10:50] (step=1023700) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 16:11:30] (step=1023800) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 16:12:11] (step=1023900) Train Loss: 0.0656, Train Steps/Sec: 2.46, Samples/Sec: 88.49
+[2025-05-19 16:12:52] (step=1024000) Train Loss: 0.0686, Train Steps/Sec: 2.46, Samples/Sec: 88.45
+[2025-05-19 16:13:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 16:13:46] (step=1024100) Train Loss: 0.0709, Train Steps/Sec: 1.84, Samples/Sec: 66.33
+[2025-05-19 16:14:26] (step=1024200) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 16:15:07] (step=1024300) Train Loss: 0.0665, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 16:15:48] (step=1024400) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 16:16:28] (step=1024500) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 16:17:09] (step=1024600) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 16:17:49] (step=1024700) Train Loss: 0.0724, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 16:18:30] (step=1024800) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 16:19:10] (step=1024900) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 16:19:51] (step=1025000) Train Loss: 0.0665, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 16:20:31] (step=1025100) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 16:21:12] (step=1025200) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 16:21:52] (step=1025300) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 16:22:33] (step=1025400) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 16:23:13] (step=1025500) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 16:23:54] (step=1025600) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 16:24:34] (step=1025700) Train Loss: 0.0639, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 16:25:15] (step=1025800) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 16:25:56] (step=1025900) Train Loss: 0.0651, Train Steps/Sec: 2.46, Samples/Sec: 88.63
+[2025-05-19 16:26:36] (step=1026000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 16:26:49] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 16:27:29] (step=1026100) Train Loss: 0.0669, Train Steps/Sec: 1.88, Samples/Sec: 67.65
+[2025-05-19 16:27:54] Beginning epoch 254...
+[2025-05-19 16:28:10] (step=1026200) Train Loss: 0.0656, Train Steps/Sec: 2.45, Samples/Sec: 88.19
+[2025-05-19 16:28:51] (step=1026300) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 16:29:31] (step=1026400) Train Loss: 0.0707, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 16:30:12] (step=1026500) Train Loss: 0.0687, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 16:30:52] (step=1026600) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 16:31:33] (step=1026700) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 16:32:13] (step=1026800) Train Loss: 0.0676, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-19 16:32:54] (step=1026900) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 16:33:35] (step=1027000) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.47
+[2025-05-19 16:34:15] (step=1027100) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 16:34:56] (step=1027200) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 16:35:36] (step=1027300) Train Loss: 0.0733, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 16:36:17] (step=1027400) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 16:36:57] (step=1027500) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 16:37:38] (step=1027600) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 16:38:18] (step=1027700) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 16:38:59] (step=1027800) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 16:39:39] (step=1027900) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 16:40:20] (step=1028000) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-19 16:40:33] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 16:41:14] (step=1028100) Train Loss: 0.0695, Train Steps/Sec: 1.85, Samples/Sec: 66.60
+[2025-05-19 16:41:54] (step=1028200) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 16:42:35] (step=1028300) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 16:43:15] (step=1028400) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 16:43:56] (step=1028500) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 16:44:36] (step=1028600) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 16:45:17] (step=1028700) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 16:45:57] (step=1028800) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 16:46:38] (step=1028900) Train Loss: 0.0744, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 16:47:18] (step=1029000) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 16:47:59] (step=1029100) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 16:48:39] (step=1029200) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 16:49:20] (step=1029300) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 16:50:00] (step=1029400) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 16:50:41] (step=1029500) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 16:51:21] (step=1029600) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 16:52:02] (step=1029700) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 16:52:42] (step=1029800) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 16:53:23] (step=1029900) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 16:54:03] (step=1030000) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 16:54:16] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 16:54:19] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 16:56:18] (step=1030000) Perceptual Loss: 0.3146, Eval Time: 122.40
+[2025-05-19 16:56:59] (step=1030100) Train Loss: 0.0643, Train Steps/Sec: 0.57, Samples/Sec: 20.51
+[2025-05-19 16:57:39] (step=1030200) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-19 16:57:39] Beginning epoch 255...
+[2025-05-19 16:58:20] (step=1030300) Train Loss: 0.0686, Train Steps/Sec: 2.45, Samples/Sec: 88.06
+[2025-05-19 16:59:01] (step=1030400) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-19 16:59:41] (step=1030500) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 17:00:22] (step=1030600) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 17:01:02] (step=1030700) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 17:01:43] (step=1030800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 17:02:23] (step=1030900) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 17:03:04] (step=1031000) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 17:03:44] (step=1031100) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 17:04:25] (step=1031200) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 17:05:05] (step=1031300) Train Loss: 0.0631, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 17:05:46] (step=1031400) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 17:06:26] (step=1031500) Train Loss: 0.0609, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 17:07:07] (step=1031600) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-19 17:07:47] (step=1031700) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 17:08:28] (step=1031800) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 17:09:08] (step=1031900) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 17:09:49] (step=1032000) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 17:10:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 17:10:42] (step=1032100) Train Loss: 0.0661, Train Steps/Sec: 1.88, Samples/Sec: 67.76
+[2025-05-19 17:11:22] (step=1032200) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 17:12:03] (step=1032300) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 17:12:43] (step=1032400) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 17:13:24] (step=1032500) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 17:14:05] (step=1032600) Train Loss: 0.0694, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 17:14:45] (step=1032700) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 17:15:26] (step=1032800) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 17:16:06] (step=1032900) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 17:16:47] (step=1033000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 17:17:27] (step=1033100) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 17:18:08] (step=1033200) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 17:18:48] (step=1033300) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 17:19:29] (step=1033400) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 17:20:09] (step=1033500) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 17:20:50] (step=1033600) Train Loss: 0.0744, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 17:21:30] (step=1033700) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 17:22:11] (step=1033800) Train Loss: 0.0599, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 17:22:51] (step=1033900) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 17:23:32] (step=1034000) Train Loss: 0.0626, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 17:23:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 17:24:25] (step=1034100) Train Loss: 0.0681, Train Steps/Sec: 1.89, Samples/Sec: 67.86
+[2025-05-19 17:25:05] (step=1034200) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 17:25:21] Beginning epoch 256...
+[2025-05-19 17:25:46] (step=1034300) Train Loss: 0.0677, Train Steps/Sec: 2.45, Samples/Sec: 88.03
+[2025-05-19 17:26:27] (step=1034400) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 17:27:07] (step=1034500) Train Loss: 0.0612, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 17:27:48] (step=1034600) Train Loss: 0.0692, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 17:28:28] (step=1034700) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 17:29:09] (step=1034800) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 17:29:49] (step=1034900) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 17:30:30] (step=1035000) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 17:31:10] (step=1035100) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-19 17:31:51] (step=1035200) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 17:32:31] (step=1035300) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 17:33:12] (step=1035400) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 17:33:52] (step=1035500) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 17:34:33] (step=1035600) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 17:35:13] (step=1035700) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 17:35:54] (step=1035800) Train Loss: 0.0743, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 17:36:34] (step=1035900) Train Loss: 0.0717, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 17:37:15] (step=1036000) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 17:37:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 17:38:08] (step=1036100) Train Loss: 0.0623, Train Steps/Sec: 1.87, Samples/Sec: 67.26
+[2025-05-19 17:38:49] (step=1036200) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 17:39:29] (step=1036300) Train Loss: 0.0609, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 17:40:10] (step=1036400) Train Loss: 0.0690, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 17:40:51] (step=1036500) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 17:41:31] (step=1036600) Train Loss: 0.0685, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 17:42:12] (step=1036700) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 17:42:52] (step=1036800) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 17:43:33] (step=1036900) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 17:44:13] (step=1037000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 17:44:54] (step=1037100) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 17:45:34] (step=1037200) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 17:46:15] (step=1037300) Train Loss: 0.0678, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 17:46:55] (step=1037400) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 17:47:36] (step=1037500) Train Loss: 0.0663, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 17:48:16] (step=1037600) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 17:48:57] (step=1037700) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 17:49:37] (step=1037800) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 17:50:18] (step=1037900) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 17:50:59] (step=1038000) Train Loss: 0.0699, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 17:51:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 17:51:53] (step=1038100) Train Loss: 0.0744, Train Steps/Sec: 1.84, Samples/Sec: 66.22
+[2025-05-19 17:52:33] (step=1038200) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 17:53:06] Beginning epoch 257...
+[2025-05-19 17:53:14] (step=1038300) Train Loss: 0.0643, Train Steps/Sec: 2.46, Samples/Sec: 88.41
+[2025-05-19 17:53:55] (step=1038400) Train Loss: 0.0674, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 17:54:35] (step=1038500) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 17:55:16] (step=1038600) Train Loss: 0.0661, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 17:55:56] (step=1038700) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 17:56:37] (step=1038800) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 17:57:17] (step=1038900) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 17:57:58] (step=1039000) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 17:58:38] (step=1039100) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 17:59:19] (step=1039200) Train Loss: 0.0611, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 18:00:00] (step=1039300) Train Loss: 0.0641, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 18:00:40] (step=1039400) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 18:01:21] (step=1039500) Train Loss: 0.0623, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 18:02:01] (step=1039600) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 18:02:42] (step=1039700) Train Loss: 0.0692, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 18:03:22] (step=1039800) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 18:04:03] (step=1039900) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 18:04:43] (step=1040000) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 18:05:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1040000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 18:05:14] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 18:07:14] (step=1040000) Perceptual Loss: 0.3026, Eval Time: 123.33
+[2025-05-19 18:07:55] (step=1040100) Train Loss: 0.0663, Train Steps/Sec: 0.52, Samples/Sec: 18.80
+[2025-05-19 18:08:35] (step=1040200) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 18:09:16] (step=1040300) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 18:09:56] (step=1040400) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 18:10:37] (step=1040500) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 18:11:17] (step=1040600) Train Loss: 0.0665, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-19 18:11:58] (step=1040700) Train Loss: 0.0665, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 18:12:38] (step=1040800) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 18:13:19] (step=1040900) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 18:14:00] (step=1041000) Train Loss: 0.0649, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 18:14:40] (step=1041100) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 18:15:21] (step=1041200) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 18:16:01] (step=1041300) Train Loss: 0.0694, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 18:16:42] (step=1041400) Train Loss: 0.0656, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 18:17:22] (step=1041500) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 18:18:03] (step=1041600) Train Loss: 0.0688, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-19 18:18:43] (step=1041700) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 18:19:24] (step=1041800) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 18:20:04] (step=1041900) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 18:20:45] (step=1042000) Train Loss: 0.0700, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-19 18:20:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 18:21:38] (step=1042100) Train Loss: 0.0617, Train Steps/Sec: 1.88, Samples/Sec: 67.56
+[2025-05-19 18:22:19] (step=1042200) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 18:22:59] (step=1042300) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 18:23:08] Beginning epoch 258...
+[2025-05-19 18:23:40] (step=1042400) Train Loss: 0.0681, Train Steps/Sec: 2.45, Samples/Sec: 88.20
+[2025-05-19 18:24:21] (step=1042500) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 18:25:01] (step=1042600) Train Loss: 0.0663, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-19 18:25:42] (step=1042700) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 18:26:22] (step=1042800) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 18:27:03] (step=1042900) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 18:27:43] (step=1043000) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 18:28:24] (step=1043100) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 18:29:04] (step=1043200) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 18:29:45] (step=1043300) Train Loss: 0.0684, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 18:30:26] (step=1043400) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 18:31:06] (step=1043500) Train Loss: 0.0711, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 18:31:47] (step=1043600) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 18:32:27] (step=1043700) Train Loss: 0.0673, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 18:33:08] (step=1043800) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 18:33:48] (step=1043900) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 18:34:29] (step=1044000) Train Loss: 0.0640, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 18:34:42] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 18:35:23] (step=1044100) Train Loss: 0.0646, Train Steps/Sec: 1.86, Samples/Sec: 66.82
+[2025-05-19 18:36:03] (step=1044200) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 18:36:44] (step=1044300) Train Loss: 0.0637, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 18:37:24] (step=1044400) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 18:38:05] (step=1044500) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 18:38:45] (step=1044600) Train Loss: 0.0676, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 18:39:26] (step=1044700) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 18:40:06] (step=1044800) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 18:40:47] (step=1044900) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 18:41:27] (step=1045000) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 18:42:08] (step=1045100) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 18:42:48] (step=1045200) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 18:43:29] (step=1045300) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 18:44:10] (step=1045400) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 18:44:50] (step=1045500) Train Loss: 0.0662, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 18:45:31] (step=1045600) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 18:46:11] (step=1045700) Train Loss: 0.0643, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 18:46:52] (step=1045800) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 18:47:32] (step=1045900) Train Loss: 0.0719, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 18:48:13] (step=1046000) Train Loss: 0.0678, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 18:48:26] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 18:49:06] (step=1046100) Train Loss: 0.0667, Train Steps/Sec: 1.86, Samples/Sec: 67.07
+[2025-05-19 18:49:47] (step=1046200) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 18:50:27] (step=1046300) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 18:50:52] Beginning epoch 259...
+[2025-05-19 18:51:08] (step=1046400) Train Loss: 0.0686, Train Steps/Sec: 2.45, Samples/Sec: 88.11
+[2025-05-19 18:51:49] (step=1046500) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-19 18:52:29] (step=1046600) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-19 18:53:10] (step=1046700) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 18:53:51] (step=1046800) Train Loss: 0.0662, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 18:54:31] (step=1046900) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 18:55:12] (step=1047000) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 18:55:52] (step=1047100) Train Loss: 0.0679, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 18:56:33] (step=1047200) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 18:57:13] (step=1047300) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 18:57:54] (step=1047400) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 18:58:34] (step=1047500) Train Loss: 0.0714, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 18:59:15] (step=1047600) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 18:59:55] (step=1047700) Train Loss: 0.0673, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 19:00:36] (step=1047800) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 19:01:16] (step=1047900) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 19:01:57] (step=1048000) Train Loss: 0.0676, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 19:02:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 19:02:51] (step=1048100) Train Loss: 0.0651, Train Steps/Sec: 1.85, Samples/Sec: 66.66
+[2025-05-19 19:03:32] (step=1048200) Train Loss: 0.0680, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 19:04:12] (step=1048300) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 19:04:53] (step=1048400) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 19:05:33] (step=1048500) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 19:06:14] (step=1048600) Train Loss: 0.0694, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 19:06:54] (step=1048700) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 19:07:35] (step=1048800) Train Loss: 0.0661, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-19 19:08:15] (step=1048900) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 19:08:56] (step=1049000) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 19:09:37] (step=1049100) Train Loss: 0.0656, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-19 19:10:17] (step=1049200) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 19:10:58] (step=1049300) Train Loss: 0.0665, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-19 19:11:38] (step=1049400) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 19:12:19] (step=1049500) Train Loss: 0.0647, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 19:12:59] (step=1049600) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 19:13:40] (step=1049700) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 19:14:20] (step=1049800) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 19:15:01] (step=1049900) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 19:15:41] (step=1050000) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 19:15:55] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 19:15:57] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 19:17:57] (step=1050000) Perceptual Loss: 0.3156, Eval Time: 122.43
+[2025-05-19 19:18:38] (step=1050100) Train Loss: 0.0634, Train Steps/Sec: 0.57, Samples/Sec: 20.43
+[2025-05-19 19:19:18] (step=1050200) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 19:19:59] (step=1050300) Train Loss: 0.0644, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 19:20:39] (step=1050400) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 19:20:39] Beginning epoch 260...
+[2025-05-19 19:21:20] (step=1050500) Train Loss: 0.0706, Train Steps/Sec: 2.45, Samples/Sec: 88.29
+[2025-05-19 19:22:00] (step=1050600) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 19:22:41] (step=1050700) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.44
+[2025-05-19 19:23:22] (step=1050800) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 19:24:02] (step=1050900) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 19:24:43] (step=1051000) Train Loss: 0.0704, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 19:25:23] (step=1051100) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 19:26:04] (step=1051200) Train Loss: 0.0604, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 19:26:44] (step=1051300) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 19:27:25] (step=1051400) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.42
+[2025-05-19 19:28:05] (step=1051500) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 19:28:46] (step=1051600) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 19:29:27] (step=1051700) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 19:30:07] (step=1051800) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 19:30:48] (step=1051900) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 19:31:28] (step=1052000) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 19:31:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 19:32:22] (step=1052100) Train Loss: 0.0658, Train Steps/Sec: 1.86, Samples/Sec: 66.85
+[2025-05-19 19:33:02] (step=1052200) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 19:33:43] (step=1052300) Train Loss: 0.0705, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 19:34:23] (step=1052400) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 19:35:04] (step=1052500) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 19:35:44] (step=1052600) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 19:36:25] (step=1052700) Train Loss: 0.0624, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-19 19:37:06] (step=1052800) Train Loss: 0.0675, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 19:37:46] (step=1052900) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 19:38:27] (step=1053000) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 19:39:07] (step=1053100) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 19:39:48] (step=1053200) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 19:40:28] (step=1053300) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 19:41:09] (step=1053400) Train Loss: 0.0697, Train Steps/Sec: 2.46, Samples/Sec: 88.48
+[2025-05-19 19:41:49] (step=1053500) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 19:42:30] (step=1053600) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 19:43:11] (step=1053700) Train Loss: 0.0647, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 19:43:51] (step=1053800) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 19:44:32] (step=1053900) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 19:45:12] (step=1054000) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 19:45:25] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 19:46:06] (step=1054100) Train Loss: 0.0694, Train Steps/Sec: 1.86, Samples/Sec: 66.98
+[2025-05-19 19:46:46] (step=1054200) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 19:47:27] (step=1054300) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 19:48:07] (step=1054400) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 19:48:24] Beginning epoch 261...
+[2025-05-19 19:48:48] (step=1054500) Train Loss: 0.0692, Train Steps/Sec: 2.45, Samples/Sec: 88.30
+[2025-05-19 19:49:29] (step=1054600) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 19:50:09] (step=1054700) Train Loss: 0.0635, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 19:50:50] (step=1054800) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-19 19:51:30] (step=1054900) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 19:52:11] (step=1055000) Train Loss: 0.0670, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 19:52:52] (step=1055100) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 19:53:32] (step=1055200) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 19:54:13] (step=1055300) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 19:54:53] (step=1055400) Train Loss: 0.0638, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 19:55:34] (step=1055500) Train Loss: 0.0644, Train Steps/Sec: 2.46, Samples/Sec: 88.63
+[2025-05-19 19:56:14] (step=1055600) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 19:56:55] (step=1055700) Train Loss: 0.0699, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 19:57:35] (step=1055800) Train Loss: 0.0723, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 19:58:16] (step=1055900) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 19:58:56] (step=1056000) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 19:59:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 19:59:51] (step=1056100) Train Loss: 0.0642, Train Steps/Sec: 1.85, Samples/Sec: 66.42
+[2025-05-19 20:00:31] (step=1056200) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 20:01:12] (step=1056300) Train Loss: 0.0721, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:01:52] (step=1056400) Train Loss: 0.0687, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 20:02:33] (step=1056500) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 20:03:13] (step=1056600) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 20:03:54] (step=1056700) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 20:04:34] (step=1056800) Train Loss: 0.0649, Train Steps/Sec: 2.46, Samples/Sec: 88.39
+[2025-05-19 20:05:15] (step=1056900) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 20:05:55] (step=1057000) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 20:06:36] (step=1057100) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 20:07:16] (step=1057200) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 20:07:57] (step=1057300) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 20:08:37] (step=1057400) Train Loss: 0.0612, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 20:09:18] (step=1057500) Train Loss: 0.0635, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-19 20:09:59] (step=1057600) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 20:10:39] (step=1057700) Train Loss: 0.0619, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 20:11:20] (step=1057800) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 20:12:00] (step=1057900) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:12:41] (step=1058000) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 20:12:54] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 20:13:34] (step=1058100) Train Loss: 0.0601, Train Steps/Sec: 1.87, Samples/Sec: 67.25
+[2025-05-19 20:14:15] (step=1058200) Train Loss: 0.0699, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 20:14:55] (step=1058300) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 20:15:36] (step=1058400) Train Loss: 0.0668, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 20:16:08] Beginning epoch 262...
+[2025-05-19 20:16:17] (step=1058500) Train Loss: 0.0691, Train Steps/Sec: 2.45, Samples/Sec: 88.36
+[2025-05-19 20:16:57] (step=1058600) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:17:38] (step=1058700) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 20:18:18] (step=1058800) Train Loss: 0.0687, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-19 20:18:59] (step=1058900) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 20:19:39] (step=1059000) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 20:20:20] (step=1059100) Train Loss: 0.0694, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 20:21:00] (step=1059200) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 20:21:41] (step=1059300) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 20:22:21] (step=1059400) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 20:23:02] (step=1059500) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-19 20:23:42] (step=1059600) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 20:24:23] (step=1059700) Train Loss: 0.0651, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 20:25:04] (step=1059800) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 20:25:44] (step=1059900) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 20:26:25] (step=1060000) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 20:26:52] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1060000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 20:26:55] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 20:28:54] (step=1060000) Perceptual Loss: 0.2969, Eval Time: 122.40
+[2025-05-19 20:29:35] (step=1060100) Train Loss: 0.0658, Train Steps/Sec: 0.53, Samples/Sec: 18.90
+[2025-05-19 20:30:15] (step=1060200) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 20:30:56] (step=1060300) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-19 20:31:37] (step=1060400) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 20:32:17] (step=1060500) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 20:32:58] (step=1060600) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:33:38] (step=1060700) Train Loss: 0.0610, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 20:34:19] (step=1060800) Train Loss: 0.0664, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 20:34:59] (step=1060900) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 20:35:40] (step=1061000) Train Loss: 0.0666, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 20:36:20] (step=1061100) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 20:37:01] (step=1061200) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 20:37:41] (step=1061300) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 20:38:22] (step=1061400) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 20:39:02] (step=1061500) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 20:39:43] (step=1061600) Train Loss: 0.0703, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 20:40:24] (step=1061700) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 20:41:04] (step=1061800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 20:41:44] (step=1061900) Train Loss: 0.0619, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 20:42:25] (step=1062000) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:42:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 20:43:19] (step=1062100) Train Loss: 0.0694, Train Steps/Sec: 1.86, Samples/Sec: 66.78
+[2025-05-19 20:43:59] (step=1062200) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:44:40] (step=1062300) Train Loss: 0.0663, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 20:45:20] (step=1062400) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 20:46:01] (step=1062500) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 20:46:09] Beginning epoch 263...
+[2025-05-19 20:46:42] (step=1062600) Train Loss: 0.0649, Train Steps/Sec: 2.45, Samples/Sec: 88.28
+[2025-05-19 20:47:22] (step=1062700) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:48:03] (step=1062800) Train Loss: 0.0708, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 20:48:43] (step=1062900) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 20:49:24] (step=1063000) Train Loss: 0.0706, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 20:50:04] (step=1063100) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:50:45] (step=1063200) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 20:51:25] (step=1063300) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 20:52:06] (step=1063400) Train Loss: 0.0706, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 20:52:47] (step=1063500) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 20:53:27] (step=1063600) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 20:54:08] (step=1063700) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 20:54:48] (step=1063800) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 20:55:29] (step=1063900) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 20:56:09] (step=1064000) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 20:56:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 20:57:03] (step=1064100) Train Loss: 0.0653, Train Steps/Sec: 1.87, Samples/Sec: 67.43
+[2025-05-19 20:57:43] (step=1064200) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 20:58:24] (step=1064300) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 20:59:04] (step=1064400) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 20:59:45] (step=1064500) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:00:25] (step=1064600) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:01:06] (step=1064700) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 21:01:46] (step=1064800) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.46
+[2025-05-19 21:02:27] (step=1064900) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 21:03:07] (step=1065000) Train Loss: 0.0633, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 21:03:48] (step=1065100) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:04:28] (step=1065200) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 21:05:09] (step=1065300) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 21:05:49] (step=1065400) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 21:06:30] (step=1065500) Train Loss: 0.0651, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 21:07:11] (step=1065600) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:07:51] (step=1065700) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 21:08:32] (step=1065800) Train Loss: 0.0612, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:09:12] (step=1065900) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 21:09:53] (step=1066000) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 21:10:06] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 21:10:47] (step=1066100) Train Loss: 0.0661, Train Steps/Sec: 1.85, Samples/Sec: 66.62
+[2025-05-19 21:11:27] (step=1066200) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:12:08] (step=1066300) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:12:48] (step=1066400) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 21:13:29] (step=1066500) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:13:53] Beginning epoch 264...
+[2025-05-19 21:14:10] (step=1066600) Train Loss: 0.0660, Train Steps/Sec: 2.45, Samples/Sec: 88.20
+[2025-05-19 21:14:50] (step=1066700) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 21:15:31] (step=1066800) Train Loss: 0.0637, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-19 21:16:11] (step=1066900) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-19 21:16:52] (step=1067000) Train Loss: 0.0680, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 21:17:32] (step=1067100) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:18:13] (step=1067200) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 21:18:53] (step=1067300) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 21:19:34] (step=1067400) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 21:20:14] (step=1067500) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 21:20:55] (step=1067600) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 21:21:35] (step=1067700) Train Loss: 0.0702, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 21:22:16] (step=1067800) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 21:22:56] (step=1067900) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 21:23:37] (step=1068000) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 21:23:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 21:24:30] (step=1068100) Train Loss: 0.0671, Train Steps/Sec: 1.88, Samples/Sec: 67.52
+[2025-05-19 21:25:11] (step=1068200) Train Loss: 0.0716, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 21:25:51] (step=1068300) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 21:26:32] (step=1068400) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 21:27:12] (step=1068500) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 21:27:53] (step=1068600) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 21:28:33] (step=1068700) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 21:29:14] (step=1068800) Train Loss: 0.0743, Train Steps/Sec: 2.46, Samples/Sec: 88.44
+[2025-05-19 21:29:55] (step=1068900) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 21:30:35] (step=1069000) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 21:31:16] (step=1069100) Train Loss: 0.0664, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 21:31:56] (step=1069200) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 21:32:37] (step=1069300) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 21:33:17] (step=1069400) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:33:58] (step=1069500) Train Loss: 0.0664, Train Steps/Sec: 2.46, Samples/Sec: 88.42
+[2025-05-19 21:34:38] (step=1069600) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:35:19] (step=1069700) Train Loss: 0.0605, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 21:36:00] (step=1069800) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 21:36:40] (step=1069900) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:37:21] (step=1070000) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 21:37:33] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 21:37:36] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 21:39:36] (step=1070000) Perceptual Loss: 0.3113, Eval Time: 122.52
+[2025-05-19 21:40:16] (step=1070100) Train Loss: 0.0683, Train Steps/Sec: 0.57, Samples/Sec: 20.49
+[2025-05-19 21:40:57] (step=1070200) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 21:41:37] (step=1070300) Train Loss: 0.0653, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 21:42:18] (step=1070400) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:42:58] (step=1070500) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 21:43:39] (step=1070600) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 21:43:39] Beginning epoch 265...
+[2025-05-19 21:44:20] (step=1070700) Train Loss: 0.0667, Train Steps/Sec: 2.45, Samples/Sec: 88.03
+[2025-05-19 21:45:00] (step=1070800) Train Loss: 0.0668, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 21:45:41] (step=1070900) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 21:46:21] (step=1071000) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 21:47:02] (step=1071100) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 21:47:42] (step=1071200) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 21:48:23] (step=1071300) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 21:49:03] (step=1071400) Train Loss: 0.0676, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 21:49:44] (step=1071500) Train Loss: 0.0707, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 21:50:25] (step=1071600) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 21:51:05] (step=1071700) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 21:51:46] (step=1071800) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 21:52:26] (step=1071900) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 21:53:07] (step=1072000) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 21:53:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 21:54:00] (step=1072100) Train Loss: 0.0661, Train Steps/Sec: 1.89, Samples/Sec: 67.99
+[2025-05-19 21:54:40] (step=1072200) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 21:55:21] (step=1072300) Train Loss: 0.0663, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-19 21:56:01] (step=1072400) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 21:56:42] (step=1072500) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 21:57:22] (step=1072600) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 21:58:03] (step=1072700) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 21:58:43] (step=1072800) Train Loss: 0.0664, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-19 21:59:24] (step=1072900) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 22:00:04] (step=1073000) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 22:00:45] (step=1073100) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 22:01:25] (step=1073200) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 22:02:06] (step=1073300) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 22:02:46] (step=1073400) Train Loss: 0.0701, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 22:03:27] (step=1073500) Train Loss: 0.0653, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 22:04:08] (step=1073600) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 22:04:48] (step=1073700) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 22:05:29] (step=1073800) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 22:06:09] (step=1073900) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 22:06:50] (step=1074000) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 22:07:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 22:07:43] (step=1074100) Train Loss: 0.0654, Train Steps/Sec: 1.88, Samples/Sec: 67.70
+[2025-05-19 22:08:23] (step=1074200) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 22:09:04] (step=1074300) Train Loss: 0.0652, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 22:09:44] (step=1074400) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 22:10:25] (step=1074500) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 22:11:05] (step=1074600) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 22:11:22] Beginning epoch 266...
+[2025-05-19 22:11:46] (step=1074700) Train Loss: 0.0660, Train Steps/Sec: 2.45, Samples/Sec: 88.26
+[2025-05-19 22:12:27] (step=1074800) Train Loss: 0.0656, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 22:13:07] (step=1074900) Train Loss: 0.0721, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 22:13:48] (step=1075000) Train Loss: 0.0597, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 22:14:29] (step=1075100) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 22:15:09] (step=1075200) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 22:15:50] (step=1075300) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 22:16:30] (step=1075400) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 22:17:11] (step=1075500) Train Loss: 0.0723, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 22:17:51] (step=1075600) Train Loss: 0.0641, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 22:18:32] (step=1075700) Train Loss: 0.0675, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 22:19:12] (step=1075800) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 22:19:53] (step=1075900) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 22:20:33] (step=1076000) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 22:20:46] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 22:21:27] (step=1076100) Train Loss: 0.0693, Train Steps/Sec: 1.87, Samples/Sec: 67.40
+[2025-05-19 22:22:07] (step=1076200) Train Loss: 0.0688, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 22:22:48] (step=1076300) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 22:23:28] (step=1076400) Train Loss: 0.0674, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 22:24:09] (step=1076500) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 22:24:49] (step=1076600) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 22:25:30] (step=1076700) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 22:26:10] (step=1076800) Train Loss: 0.0720, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 22:26:51] (step=1076900) Train Loss: 0.0629, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 22:27:32] (step=1077000) Train Loss: 0.0717, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 22:28:12] (step=1077100) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 22:28:53] (step=1077200) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 22:29:33] (step=1077300) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 22:30:14] (step=1077400) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 22:30:54] (step=1077500) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 22:31:35] (step=1077600) Train Loss: 0.0647, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-19 22:32:15] (step=1077700) Train Loss: 0.0682, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-19 22:32:56] (step=1077800) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 22:33:36] (step=1077900) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 22:34:17] (step=1078000) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 22:34:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 22:35:10] (step=1078100) Train Loss: 0.0664, Train Steps/Sec: 1.88, Samples/Sec: 67.83
+[2025-05-19 22:35:51] (step=1078200) Train Loss: 0.0593, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-19 22:36:31] (step=1078300) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 22:37:12] (step=1078400) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 22:37:52] (step=1078500) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 22:38:33] (step=1078600) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 22:39:05] Beginning epoch 267...
+[2025-05-19 22:39:13] (step=1078700) Train Loss: 0.0660, Train Steps/Sec: 2.45, Samples/Sec: 88.24
+[2025-05-19 22:39:54] (step=1078800) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 22:40:35] (step=1078900) Train Loss: 0.0672, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-19 22:41:15] (step=1079000) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-19 22:41:56] (step=1079100) Train Loss: 0.0635, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 22:42:36] (step=1079200) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 22:43:17] (step=1079300) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 22:43:57] (step=1079400) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 22:44:38] (step=1079500) Train Loss: 0.0719, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-19 22:45:18] (step=1079600) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-19 22:45:59] (step=1079700) Train Loss: 0.0721, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 22:46:39] (step=1079800) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 22:47:20] (step=1079900) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 22:48:00] (step=1080000) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 22:48:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1080000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 22:48:30] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-19 22:50:30] (step=1080000) Perceptual Loss: 0.3239, Eval Time: 123.09
+[2025-05-19 22:51:10] (step=1080100) Train Loss: 0.0629, Train Steps/Sec: 0.53, Samples/Sec: 18.97
+[2025-05-19 22:51:51] (step=1080200) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-19 22:52:31] (step=1080300) Train Loss: 0.0654, Train Steps/Sec: 2.46, Samples/Sec: 88.58
+[2025-05-19 22:53:12] (step=1080400) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 22:53:52] (step=1080500) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 22:54:33] (step=1080600) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 22:55:14] (step=1080700) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 22:55:54] (step=1080800) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 22:56:35] (step=1080900) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 22:57:15] (step=1081000) Train Loss: 0.0596, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 22:57:56] (step=1081100) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 22:58:36] (step=1081200) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 22:59:17] (step=1081300) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 22:59:57] (step=1081400) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-19 23:00:38] (step=1081500) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 23:01:19] (step=1081600) Train Loss: 0.0670, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-19 23:01:59] (step=1081700) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-19 23:02:40] (step=1081800) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-19 23:03:20] (step=1081900) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-19 23:04:01] (step=1082000) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 23:04:15] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 23:04:55] (step=1082100) Train Loss: 0.0721, Train Steps/Sec: 1.83, Samples/Sec: 65.71
+[2025-05-19 23:05:36] (step=1082200) Train Loss: 0.0690, Train Steps/Sec: 2.46, Samples/Sec: 88.53
+[2025-05-19 23:06:17] (step=1082300) Train Loss: 0.0691, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-19 23:06:57] (step=1082400) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 23:07:38] (step=1082500) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-19 23:08:18] (step=1082600) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 23:08:59] (step=1082700) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 23:09:07] Beginning epoch 268...
+[2025-05-19 23:09:40] (step=1082800) Train Loss: 0.0689, Train Steps/Sec: 2.43, Samples/Sec: 87.59
+[2025-05-19 23:10:21] (step=1082900) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 23:11:01] (step=1083000) Train Loss: 0.0668, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-19 23:11:42] (step=1083100) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-19 23:12:22] (step=1083200) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-19 23:13:03] (step=1083300) Train Loss: 0.0669, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 23:13:43] (step=1083400) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 23:14:24] (step=1083500) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-19 23:15:05] (step=1083600) Train Loss: 0.0676, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 23:15:45] (step=1083700) Train Loss: 0.0642, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-19 23:16:26] (step=1083800) Train Loss: 0.0624, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 23:17:06] (step=1083900) Train Loss: 0.0660, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 23:17:47] (step=1084000) Train Loss: 0.0668, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 23:18:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 23:18:41] (step=1084100) Train Loss: 0.0617, Train Steps/Sec: 1.84, Samples/Sec: 66.30
+[2025-05-19 23:19:22] (step=1084200) Train Loss: 0.0697, Train Steps/Sec: 2.46, Samples/Sec: 88.52
+[2025-05-19 23:20:02] (step=1084300) Train Loss: 0.0622, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 23:20:43] (step=1084400) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 23:21:24] (step=1084500) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-19 23:22:04] (step=1084600) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 23:22:45] (step=1084700) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 23:23:25] (step=1084800) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-19 23:24:06] (step=1084900) Train Loss: 0.0699, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 23:24:46] (step=1085000) Train Loss: 0.0676, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 23:25:27] (step=1085100) Train Loss: 0.0670, Train Steps/Sec: 2.46, Samples/Sec: 88.46
+[2025-05-19 23:26:07] (step=1085200) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 23:26:48] (step=1085300) Train Loss: 0.0730, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-19 23:27:29] (step=1085400) Train Loss: 0.0669, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-19 23:28:09] (step=1085500) Train Loss: 0.0653, Train Steps/Sec: 2.46, Samples/Sec: 88.39
+[2025-05-19 23:28:50] (step=1085600) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-19 23:29:31] (step=1085700) Train Loss: 0.0665, Train Steps/Sec: 2.46, Samples/Sec: 88.42
+[2025-05-19 23:30:11] (step=1085800) Train Loss: 0.0655, Train Steps/Sec: 2.46, Samples/Sec: 88.51
+[2025-05-19 23:30:52] (step=1085900) Train Loss: 0.0714, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-19 23:31:33] (step=1086000) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-19 23:31:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 23:32:26] (step=1086100) Train Loss: 0.0654, Train Steps/Sec: 1.88, Samples/Sec: 67.82
+[2025-05-19 23:33:06] (step=1086200) Train Loss: 0.0660, Train Steps/Sec: 2.45, Samples/Sec: 88.31
+[2025-05-19 23:33:47] (step=1086300) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 23:34:28] (step=1086400) Train Loss: 0.0695, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-19 23:35:08] (step=1086500) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 23:35:49] (step=1086600) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 23:36:29] (step=1086700) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 23:36:53] Beginning epoch 269...
+[2025-05-19 23:37:10] (step=1086800) Train Loss: 0.0732, Train Steps/Sec: 2.44, Samples/Sec: 87.91
+[2025-05-19 23:37:50] (step=1086900) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-19 23:38:31] (step=1087000) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 23:39:12] (step=1087100) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 23:39:52] (step=1087200) Train Loss: 0.0727, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 23:40:32] (step=1087300) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 23:41:13] (step=1087400) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-19 23:41:54] (step=1087500) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-19 23:42:34] (step=1087600) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-19 23:43:15] (step=1087700) Train Loss: 0.0704, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 23:43:55] (step=1087800) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 23:44:36] (step=1087900) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 23:45:16] (step=1088000) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-19 23:45:29] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-19 23:46:10] (step=1088100) Train Loss: 0.0651, Train Steps/Sec: 1.86, Samples/Sec: 66.81
+[2025-05-19 23:46:51] (step=1088200) Train Loss: 0.0661, Train Steps/Sec: 2.46, Samples/Sec: 88.51
+[2025-05-19 23:47:31] (step=1088300) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-19 23:48:12] (step=1088400) Train Loss: 0.0733, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-19 23:48:52] (step=1088500) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 23:49:33] (step=1088600) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 23:50:13] (step=1088700) Train Loss: 0.0712, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-19 23:50:54] (step=1088800) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 23:51:34] (step=1088900) Train Loss: 0.0652, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-19 23:52:15] (step=1089000) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 23:52:55] (step=1089100) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-19 23:53:36] (step=1089200) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 23:54:16] (step=1089300) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-19 23:54:57] (step=1089400) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-19 23:55:37] (step=1089500) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-19 23:56:18] (step=1089600) Train Loss: 0.0673, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-19 23:56:58] (step=1089700) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-19 23:57:39] (step=1089800) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-19 23:58:19] (step=1089900) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-19 23:59:00] (step=1090000) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 23:59:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-19 23:59:15] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 00:01:15] (step=1090000) Perceptual Loss: 0.3264, Eval Time: 122.37
+[2025-05-20 00:01:55] (step=1090100) Train Loss: 0.0674, Train Steps/Sec: 0.57, Samples/Sec: 20.50
+[2025-05-20 00:02:36] (step=1090200) Train Loss: 0.0713, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 00:03:16] (step=1090300) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 00:03:57] (step=1090400) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:04:37] (step=1090500) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 00:05:18] (step=1090600) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 00:05:58] (step=1090700) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 00:06:39] (step=1090800) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 00:06:39] Beginning epoch 270...
+[2025-05-20 00:07:20] (step=1090900) Train Loss: 0.0677, Train Steps/Sec: 2.45, Samples/Sec: 88.13
+[2025-05-20 00:08:00] (step=1091000) Train Loss: 0.0684, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-20 00:08:41] (step=1091100) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:09:21] (step=1091200) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 00:10:02] (step=1091300) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 00:10:42] (step=1091400) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 00:11:23] (step=1091500) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 00:12:04] (step=1091600) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-20 00:12:44] (step=1091700) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 00:13:24] (step=1091800) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 00:14:05] (step=1091900) Train Loss: 0.0733, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 00:14:45] (step=1092000) Train Loss: 0.0597, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 00:14:59] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 00:15:40] (step=1092100) Train Loss: 0.0668, Train Steps/Sec: 1.85, Samples/Sec: 66.44
+[2025-05-20 00:16:20] (step=1092200) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-20 00:17:01] (step=1092300) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-20 00:17:41] (step=1092400) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 00:18:22] (step=1092500) Train Loss: 0.0614, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 00:19:02] (step=1092600) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 00:19:43] (step=1092700) Train Loss: 0.0631, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 00:20:23] (step=1092800) Train Loss: 0.0630, Train Steps/Sec: 2.46, Samples/Sec: 88.54
+[2025-05-20 00:21:04] (step=1092900) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 00:21:44] (step=1093000) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-20 00:22:25] (step=1093100) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:23:05] (step=1093200) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:23:46] (step=1093300) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 00:24:26] (step=1093400) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 00:25:07] (step=1093500) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 00:25:47] (step=1093600) Train Loss: 0.0622, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-20 00:26:28] (step=1093700) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-20 00:27:08] (step=1093800) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 00:27:49] (step=1093900) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 00:28:29] (step=1094000) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:28:43] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 00:29:24] (step=1094100) Train Loss: 0.0700, Train Steps/Sec: 1.84, Samples/Sec: 66.39
+[2025-05-20 00:30:04] (step=1094200) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-20 00:30:45] (step=1094300) Train Loss: 0.0670, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-20 00:31:25] (step=1094400) Train Loss: 0.0751, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 00:32:06] (step=1094500) Train Loss: 0.0591, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:32:46] (step=1094600) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:33:27] (step=1094700) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 00:34:07] (step=1094800) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 00:34:24] Beginning epoch 271...
+[2025-05-20 00:34:48] (step=1094900) Train Loss: 0.0714, Train Steps/Sec: 2.45, Samples/Sec: 88.21
+[2025-05-20 00:35:29] (step=1095000) Train Loss: 0.0654, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-20 00:36:09] (step=1095100) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 00:36:50] (step=1095200) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 00:37:30] (step=1095300) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:38:11] (step=1095400) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 00:38:51] (step=1095500) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 00:39:32] (step=1095600) Train Loss: 0.0636, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-20 00:40:12] (step=1095700) Train Loss: 0.0632, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-20 00:40:53] (step=1095800) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 00:41:33] (step=1095900) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 00:42:14] (step=1096000) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 00:42:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 00:43:08] (step=1096100) Train Loss: 0.0689, Train Steps/Sec: 1.83, Samples/Sec: 65.97
+[2025-05-20 00:43:49] (step=1096200) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-20 00:44:29] (step=1096300) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 00:45:10] (step=1096400) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-20 00:45:51] (step=1096500) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 00:46:31] (step=1096600) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:47:11] (step=1096700) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 00:47:52] (step=1096800) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-20 00:48:33] (step=1096900) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-20 00:49:13] (step=1097000) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 00:49:54] (step=1097100) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 00:50:34] (step=1097200) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 00:51:15] (step=1097300) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 00:51:55] (step=1097400) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 00:52:36] (step=1097500) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-20 00:53:16] (step=1097600) Train Loss: 0.0677, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-20 00:53:57] (step=1097700) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 00:54:37] (step=1097800) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 00:55:18] (step=1097900) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 00:55:58] (step=1098000) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 00:56:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 00:56:53] (step=1098100) Train Loss: 0.0663, Train Steps/Sec: 1.83, Samples/Sec: 65.78
+[2025-05-20 00:57:33] (step=1098200) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-20 00:58:14] (step=1098300) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 00:58:55] (step=1098400) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 00:59:35] (step=1098500) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 01:00:15] (step=1098600) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:00:56] (step=1098700) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 01:01:36] (step=1098800) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-20 01:02:09] Beginning epoch 272...
+[2025-05-20 01:02:17] (step=1098900) Train Loss: 0.0688, Train Steps/Sec: 2.45, Samples/Sec: 88.05
+[2025-05-20 01:02:58] (step=1099000) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:03:38] (step=1099100) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-20 01:04:19] (step=1099200) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 01:04:59] (step=1099300) Train Loss: 0.0725, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 01:05:40] (step=1099400) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:06:20] (step=1099500) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-20 01:07:01] (step=1099600) Train Loss: 0.0652, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-20 01:07:42] (step=1099700) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 01:08:22] (step=1099800) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 01:09:02] (step=1099900) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 01:09:43] (step=1100000) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 01:10:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1100000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 01:10:12] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 01:12:11] (step=1100000) Perceptual Loss: 0.3011, Eval Time: 122.45
+[2025-05-20 01:12:52] (step=1100100) Train Loss: 0.0694, Train Steps/Sec: 0.53, Samples/Sec: 19.04
+[2025-05-20 01:13:33] (step=1100200) Train Loss: 0.0706, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-20 01:14:13] (step=1100300) Train Loss: 0.0722, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-20 01:14:54] (step=1100400) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 01:15:34] (step=1100500) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 01:16:15] (step=1100600) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 01:16:55] (step=1100700) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-20 01:17:36] (step=1100800) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:18:16] (step=1100900) Train Loss: 0.0652, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-20 01:18:57] (step=1101000) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-20 01:19:37] (step=1101100) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 01:20:18] (step=1101200) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:20:58] (step=1101300) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 01:21:39] (step=1101400) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 01:22:19] (step=1101500) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:23:00] (step=1101600) Train Loss: 0.0664, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-20 01:23:40] (step=1101700) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 01:24:21] (step=1101800) Train Loss: 0.0731, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 01:25:01] (step=1101900) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 01:25:42] (step=1102000) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 01:25:55] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 01:26:35] (step=1102100) Train Loss: 0.0636, Train Steps/Sec: 1.87, Samples/Sec: 67.15
+[2025-05-20 01:27:16] (step=1102200) Train Loss: 0.0659, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-20 01:27:56] (step=1102300) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-20 01:28:37] (step=1102400) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 01:29:17] (step=1102500) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 01:29:58] (step=1102600) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 01:30:38] (step=1102700) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 01:31:19] (step=1102800) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 01:31:59] (step=1102900) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-20 01:32:07] Beginning epoch 273...
+[2025-05-20 01:32:40] (step=1103000) Train Loss: 0.0688, Train Steps/Sec: 2.45, Samples/Sec: 88.22
+[2025-05-20 01:33:21] (step=1103100) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 01:34:01] (step=1103200) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 01:34:42] (step=1103300) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 01:35:22] (step=1103400) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 01:36:03] (step=1103500) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 01:36:43] (step=1103600) Train Loss: 0.0667, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-20 01:37:24] (step=1103700) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-20 01:38:04] (step=1103800) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:38:45] (step=1103900) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 01:39:25] (step=1104000) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 01:39:39] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 01:40:20] (step=1104100) Train Loss: 0.0639, Train Steps/Sec: 1.84, Samples/Sec: 66.18
+[2025-05-20 01:41:00] (step=1104200) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 01:41:41] (step=1104300) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-20 01:42:21] (step=1104400) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 01:43:02] (step=1104500) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 01:43:42] (step=1104600) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 01:44:23] (step=1104700) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:45:03] (step=1104800) Train Loss: 0.0711, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 01:45:44] (step=1104900) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.74
+[2025-05-20 01:46:24] (step=1105000) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 01:47:05] (step=1105100) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 01:47:45] (step=1105200) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 01:48:26] (step=1105300) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 01:49:06] (step=1105400) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:49:47] (step=1105500) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-20 01:50:27] (step=1105600) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 01:51:08] (step=1105700) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-20 01:51:48] (step=1105800) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 01:52:29] (step=1105900) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 01:53:09] (step=1106000) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 01:53:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 01:54:04] (step=1106100) Train Loss: 0.0659, Train Steps/Sec: 1.84, Samples/Sec: 66.08
+[2025-05-20 01:54:44] (step=1106200) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 01:55:25] (step=1106300) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 01:56:05] (step=1106400) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 01:56:46] (step=1106500) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 01:57:26] (step=1106600) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 01:58:07] (step=1106700) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 01:58:47] (step=1106800) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 01:59:28] (step=1106900) Train Loss: 0.0646, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-20 01:59:52] Beginning epoch 274...
+[2025-05-20 02:00:09] (step=1107000) Train Loss: 0.0706, Train Steps/Sec: 2.45, Samples/Sec: 88.27
+[2025-05-20 02:00:49] (step=1107100) Train Loss: 0.0725, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 02:01:30] (step=1107200) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 02:02:10] (step=1107300) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 02:02:51] (step=1107400) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 02:03:31] (step=1107500) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 02:04:12] (step=1107600) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-20 02:04:52] (step=1107700) Train Loss: 0.0635, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 02:05:33] (step=1107800) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 02:06:13] (step=1107900) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 02:06:54] (step=1108000) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 02:07:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 02:07:48] (step=1108100) Train Loss: 0.0675, Train Steps/Sec: 1.83, Samples/Sec: 65.95
+[2025-05-20 02:08:29] (step=1108200) Train Loss: 0.0748, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-20 02:09:10] (step=1108300) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 02:09:50] (step=1108400) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 02:10:30] (step=1108500) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:11:11] (step=1108600) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 02:11:51] (step=1108700) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 02:12:32] (step=1108800) Train Loss: 0.0720, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 02:13:12] (step=1108900) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 02:13:53] (step=1109000) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 02:14:33] (step=1109100) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-20 02:15:14] (step=1109200) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 02:15:54] (step=1109300) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 02:16:35] (step=1109400) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 02:17:15] (step=1109500) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 02:17:56] (step=1109600) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-20 02:18:36] (step=1109700) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 02:19:17] (step=1109800) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 02:19:57] (step=1109900) Train Loss: 0.0602, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 02:20:38] (step=1110000) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 02:20:51] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 02:20:54] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 02:22:54] (step=1110000) Perceptual Loss: 0.3003, Eval Time: 122.64
+[2025-05-20 02:23:35] (step=1110100) Train Loss: 0.0692, Train Steps/Sec: 0.56, Samples/Sec: 20.34
+[2025-05-20 02:24:15] (step=1110200) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:24:56] (step=1110300) Train Loss: 0.0666, Train Steps/Sec: 2.46, Samples/Sec: 88.48
+[2025-05-20 02:25:36] (step=1110400) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 02:26:17] (step=1110500) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:26:57] (step=1110600) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 02:27:38] (step=1110700) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 02:28:18] (step=1110800) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 02:28:58] (step=1110900) Train Loss: 0.0734, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 02:29:39] (step=1111000) Train Loss: 0.0619, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-20 02:29:39] Beginning epoch 275...
+[2025-05-20 02:30:20] (step=1111100) Train Loss: 0.0707, Train Steps/Sec: 2.45, Samples/Sec: 88.37
+[2025-05-20 02:31:00] (step=1111200) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 02:31:41] (step=1111300) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 02:32:21] (step=1111400) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 02:33:02] (step=1111500) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 02:33:42] (step=1111600) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 02:34:23] (step=1111700) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-20 02:35:03] (step=1111800) Train Loss: 0.0723, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 02:35:44] (step=1111900) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:36:24] (step=1112000) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 02:36:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 02:37:18] (step=1112100) Train Loss: 0.0698, Train Steps/Sec: 1.85, Samples/Sec: 66.55
+[2025-05-20 02:37:58] (step=1112200) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:38:39] (step=1112300) Train Loss: 0.0636, Train Steps/Sec: 2.46, Samples/Sec: 88.65
+[2025-05-20 02:39:20] (step=1112400) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 02:40:00] (step=1112500) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 02:40:40] (step=1112600) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:41:21] (step=1112700) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 02:42:01] (step=1112800) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 02:42:42] (step=1112900) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:43:22] (step=1113000) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 02:44:03] (step=1113100) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 02:44:43] (step=1113200) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 02:45:24] (step=1113300) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 02:46:04] (step=1113400) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 02:46:45] (step=1113500) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 02:47:25] (step=1113600) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:48:06] (step=1113700) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-20 02:48:46] (step=1113800) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 02:49:26] (step=1113900) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:50:07] (step=1114000) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 02:50:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 02:51:01] (step=1114100) Train Loss: 0.0625, Train Steps/Sec: 1.86, Samples/Sec: 67.05
+[2025-05-20 02:51:41] (step=1114200) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 02:52:22] (step=1114300) Train Loss: 0.0661, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-20 02:53:02] (step=1114400) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 02:53:42] (step=1114500) Train Loss: 0.0730, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 02:54:23] (step=1114600) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 02:55:03] (step=1114700) Train Loss: 0.0615, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 02:55:44] (step=1114800) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 02:56:24] (step=1114900) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 02:57:05] (step=1115000) Train Loss: 0.0658, Train Steps/Sec: 2.46, Samples/Sec: 88.66
+[2025-05-20 02:57:21] Beginning epoch 276...
+[2025-05-20 02:57:46] (step=1115100) Train Loss: 0.0694, Train Steps/Sec: 2.46, Samples/Sec: 88.41
+[2025-05-20 02:58:26] (step=1115200) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 02:59:06] (step=1115300) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 02:59:47] (step=1115400) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 03:00:27] (step=1115500) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-20 03:01:08] (step=1115600) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 03:01:48] (step=1115700) Train Loss: 0.0628, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-20 03:02:29] (step=1115800) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 03:03:09] (step=1115900) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:03:50] (step=1116000) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 03:04:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 03:04:43] (step=1116100) Train Loss: 0.0641, Train Steps/Sec: 1.89, Samples/Sec: 67.92
+[2025-05-20 03:05:23] (step=1116200) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:06:04] (step=1116300) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 03:06:44] (step=1116400) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 03:07:25] (step=1116500) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 03:08:05] (step=1116600) Train Loss: 0.0739, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 03:08:46] (step=1116700) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:09:26] (step=1116800) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 03:10:07] (step=1116900) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 03:10:47] (step=1117000) Train Loss: 0.0735, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-20 03:11:28] (step=1117100) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 03:12:08] (step=1117200) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 03:12:48] (step=1117300) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 03:13:29] (step=1117400) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:14:09] (step=1117500) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 03:14:50] (step=1117600) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 03:15:30] (step=1117700) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 03:16:11] (step=1117800) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:16:51] (step=1117900) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 03:17:32] (step=1118000) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 03:17:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 03:18:25] (step=1118100) Train Loss: 0.0736, Train Steps/Sec: 1.88, Samples/Sec: 67.71
+[2025-05-20 03:19:05] (step=1118200) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 03:19:46] (step=1118300) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 03:20:26] (step=1118400) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 03:21:07] (step=1118500) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 03:21:47] (step=1118600) Train Loss: 0.0615, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 03:22:28] (step=1118700) Train Loss: 0.0612, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 03:23:08] (step=1118800) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 03:23:48] (step=1118900) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 03:24:29] (step=1119000) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 03:25:01] Beginning epoch 277...
+[2025-05-20 03:25:10] (step=1119100) Train Loss: 0.0718, Train Steps/Sec: 2.45, Samples/Sec: 88.22
+[2025-05-20 03:25:50] (step=1119200) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:26:31] (step=1119300) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:27:11] (step=1119400) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:27:52] (step=1119500) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 03:28:32] (step=1119600) Train Loss: 0.0717, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 03:29:13] (step=1119700) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-20 03:29:53] (step=1119800) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 03:30:34] (step=1119900) Train Loss: 0.0618, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 03:31:14] (step=1120000) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 03:31:41] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1120000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 03:31:44] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 03:33:44] (step=1120000) Perceptual Loss: 0.3212, Eval Time: 122.65
+[2025-05-20 03:34:24] (step=1120100) Train Loss: 0.0660, Train Steps/Sec: 0.53, Samples/Sec: 18.93
+[2025-05-20 03:35:05] (step=1120200) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 03:35:45] (step=1120300) Train Loss: 0.0727, Train Steps/Sec: 2.46, Samples/Sec: 88.54
+[2025-05-20 03:36:26] (step=1120400) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 03:37:06] (step=1120500) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 03:37:47] (step=1120600) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 03:38:27] (step=1120700) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 03:39:08] (step=1120800) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 03:39:48] (step=1120900) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 03:40:29] (step=1121000) Train Loss: 0.0691, Train Steps/Sec: 2.46, Samples/Sec: 88.64
+[2025-05-20 03:41:09] (step=1121100) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 03:41:50] (step=1121200) Train Loss: 0.0707, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 03:42:30] (step=1121300) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 03:43:11] (step=1121400) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 03:43:51] (step=1121500) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 03:44:31] (step=1121600) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 03:45:12] (step=1121700) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.78
+[2025-05-20 03:45:53] (step=1121800) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 03:46:33] (step=1121900) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 03:47:13] (step=1122000) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 03:47:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 03:48:07] (step=1122100) Train Loss: 0.0699, Train Steps/Sec: 1.86, Samples/Sec: 66.81
+[2025-05-20 03:48:48] (step=1122200) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 03:49:28] (step=1122300) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 03:50:09] (step=1122400) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 03:50:49] (step=1122500) Train Loss: 0.0690, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 03:51:29] (step=1122600) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-20 03:52:10] (step=1122700) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 03:52:50] (step=1122800) Train Loss: 0.0720, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 03:53:30] (step=1122900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 03:54:11] (step=1123000) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 03:54:51] (step=1123100) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 03:54:59] Beginning epoch 278...
+[2025-05-20 03:55:32] (step=1123200) Train Loss: 0.0661, Train Steps/Sec: 2.46, Samples/Sec: 88.59
+[2025-05-20 03:56:12] (step=1123300) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 03:56:53] (step=1123400) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 03:57:33] (step=1123500) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 03:58:13] (step=1123600) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 03:58:54] (step=1123700) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 03:59:34] (step=1123800) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 04:00:14] (step=1123900) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 04:00:55] (step=1124000) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 04:01:08] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 04:01:49] (step=1124100) Train Loss: 0.0721, Train Steps/Sec: 1.85, Samples/Sec: 66.63
+[2025-05-20 04:02:29] (step=1124200) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 04:03:09] (step=1124300) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 04:03:50] (step=1124400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 04:04:30] (step=1124500) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 04:05:10] (step=1124600) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 04:05:51] (step=1124700) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:06:31] (step=1124800) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 04:07:11] (step=1124900) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:07:52] (step=1125000) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 04:08:32] (step=1125100) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 04:09:13] (step=1125200) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:09:53] (step=1125300) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 04:10:33] (step=1125400) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:11:14] (step=1125500) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 04:11:54] (step=1125600) Train Loss: 0.0731, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:12:34] (step=1125700) Train Loss: 0.0735, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 04:13:15] (step=1125800) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 04:13:55] (step=1125900) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 04:14:35] (step=1126000) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 04:14:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 04:15:28] (step=1126100) Train Loss: 0.0667, Train Steps/Sec: 1.91, Samples/Sec: 68.73
+[2025-05-20 04:16:08] (step=1126200) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 04:16:49] (step=1126300) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 04:17:29] (step=1126400) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 04:18:09] (step=1126500) Train Loss: 0.0739, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:18:50] (step=1126600) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:19:30] (step=1126700) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:20:10] (step=1126800) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 04:20:51] (step=1126900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-20 04:21:31] (step=1127000) Train Loss: 0.0727, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 04:22:11] (step=1127100) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:22:36] Beginning epoch 279...
+[2025-05-20 04:22:52] (step=1127200) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-20 04:23:32] (step=1127300) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:24:13] (step=1127400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:24:53] (step=1127500) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 04:25:33] (step=1127600) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 04:26:14] (step=1127700) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 04:26:54] (step=1127800) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:27:34] (step=1127900) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:28:15] (step=1128000) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:28:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 04:29:07] (step=1128100) Train Loss: 0.0647, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-20 04:29:48] (step=1128200) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 04:30:28] (step=1128300) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 04:31:08] (step=1128400) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 04:31:49] (step=1128500) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-20 04:32:29] (step=1128600) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 04:33:09] (step=1128700) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 04:33:50] (step=1128800) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 04:34:30] (step=1128900) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 04:35:11] (step=1129000) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 04:35:51] (step=1129100) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 04:36:31] (step=1129200) Train Loss: 0.0608, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 04:37:12] (step=1129300) Train Loss: 0.0725, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 04:37:52] (step=1129400) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:38:32] (step=1129500) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 04:39:13] (step=1129600) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 04:39:53] (step=1129700) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 04:40:33] (step=1129800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 04:41:14] (step=1129900) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 04:41:54] (step=1130000) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 04:42:06] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 04:42:09] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 04:44:08] (step=1130000) Perceptual Loss: 0.3002, Eval Time: 122.20
+[2025-05-20 04:44:49] (step=1130100) Train Loss: 0.0604, Train Steps/Sec: 0.57, Samples/Sec: 20.61
+[2025-05-20 04:45:29] (step=1130200) Train Loss: 0.0633, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-20 04:46:10] (step=1130300) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 04:46:50] (step=1130400) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-20 04:47:30] (step=1130500) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.34
+[2025-05-20 04:48:10] (step=1130600) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:48:51] (step=1130700) Train Loss: 0.0695, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-20 04:49:31] (step=1130800) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 04:50:11] (step=1130900) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 04:50:52] (step=1131000) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 04:51:32] (step=1131100) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 04:52:13] (step=1131200) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.35
+[2025-05-20 04:52:13] Beginning epoch 280...
+[2025-05-20 04:52:53] (step=1131300) Train Loss: 0.0648, Train Steps/Sec: 2.46, Samples/Sec: 88.60
+[2025-05-20 04:53:34] (step=1131400) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-20 04:54:14] (step=1131500) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 04:54:54] (step=1131600) Train Loss: 0.0629, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-20 04:55:35] (step=1131700) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 04:56:15] (step=1131800) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 04:56:55] (step=1131900) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 04:57:36] (step=1132000) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 04:57:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 04:58:28] (step=1132100) Train Loss: 0.0663, Train Steps/Sec: 1.90, Samples/Sec: 68.56
+[2025-05-20 04:59:08] (step=1132200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 04:59:49] (step=1132300) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 05:00:29] (step=1132400) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 05:01:10] (step=1132500) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 05:01:50] (step=1132600) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 05:02:30] (step=1132700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 05:03:11] (step=1132800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 05:03:51] (step=1132900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 05:04:31] (step=1133000) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 05:05:12] (step=1133100) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 05:05:52] (step=1133200) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 05:06:32] (step=1133300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 05:07:13] (step=1133400) Train Loss: 0.0682, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 05:07:53] (step=1133500) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 05:08:33] (step=1133600) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 05:09:14] (step=1133700) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 05:09:54] (step=1133800) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 05:10:35] (step=1133900) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 05:11:15] (step=1134000) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 05:11:27] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 05:12:08] (step=1134100) Train Loss: 0.0687, Train Steps/Sec: 1.89, Samples/Sec: 68.16
+[2025-05-20 05:12:48] (step=1134200) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 05:13:28] (step=1134300) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 05:14:09] (step=1134400) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 05:14:49] (step=1134500) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 05:15:30] (step=1134600) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 05:16:10] (step=1134700) Train Loss: 0.0608, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 05:16:50] (step=1134800) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 05:17:31] (step=1134900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 05:18:11] (step=1135000) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 05:18:51] (step=1135100) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 05:19:32] (step=1135200) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 05:19:48] Beginning epoch 281...
+[2025-05-20 05:20:12] (step=1135300) Train Loss: 0.0702, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-20 05:20:53] (step=1135400) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 05:21:33] (step=1135500) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 05:22:13] (step=1135600) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 05:22:54] (step=1135700) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 05:23:34] (step=1135800) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 05:24:14] (step=1135900) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 05:24:55] (step=1136000) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 05:25:07] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 05:25:47] (step=1136100) Train Loss: 0.0646, Train Steps/Sec: 1.91, Samples/Sec: 68.61
+[2025-05-20 05:26:28] (step=1136200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-20 05:27:08] (step=1136300) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-20 05:27:48] (step=1136400) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-20 05:28:29] (step=1136500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 05:29:09] (step=1136600) Train Loss: 0.0617, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 05:29:49] (step=1136700) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 05:30:30] (step=1136800) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 05:31:10] (step=1136900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 05:31:51] (step=1137000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 05:32:31] (step=1137100) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 05:33:11] (step=1137200) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 05:33:52] (step=1137300) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 05:34:32] (step=1137400) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 05:35:12] (step=1137500) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 05:35:53] (step=1137600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 05:36:33] (step=1137700) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 05:37:13] (step=1137800) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 05:37:54] (step=1137900) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 05:38:34] (step=1138000) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 05:38:47] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 05:39:27] (step=1138100) Train Loss: 0.0699, Train Steps/Sec: 1.89, Samples/Sec: 68.18
+[2025-05-20 05:40:07] (step=1138200) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 05:40:48] (step=1138300) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 05:41:28] (step=1138400) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 05:42:08] (step=1138500) Train Loss: 0.0631, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 05:42:49] (step=1138600) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 05:43:29] (step=1138700) Train Loss: 0.0726, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 05:44:10] (step=1138800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 05:44:50] (step=1138900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 05:45:30] (step=1139000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 05:46:11] (step=1139100) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 05:46:51] (step=1139200) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 05:47:23] Beginning epoch 282...
+[2025-05-20 05:47:32] (step=1139300) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-20 05:48:12] (step=1139400) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 05:48:52] (step=1139500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 05:49:33] (step=1139600) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 05:50:13] (step=1139700) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 05:50:53] (step=1139800) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 05:51:34] (step=1139900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 05:52:14] (step=1140000) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 05:52:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1140000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 05:52:40] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 05:54:40] (step=1140000) Perceptual Loss: 0.2950, Eval Time: 122.37
+[2025-05-20 05:55:20] (step=1140100) Train Loss: 0.0663, Train Steps/Sec: 0.54, Samples/Sec: 19.31
+[2025-05-20 05:56:01] (step=1140200) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 05:56:41] (step=1140300) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 05:57:22] (step=1140400) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 05:58:02] (step=1140500) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 05:58:42] (step=1140600) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 05:59:23] (step=1140700) Train Loss: 0.0620, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 06:00:03] (step=1140800) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 06:00:43] (step=1140900) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 06:01:24] (step=1141000) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 06:02:05] (step=1141100) Train Loss: 0.0688, Train Steps/Sec: 2.45, Samples/Sec: 88.38
+[2025-05-20 06:02:45] (step=1141200) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 06:03:25] (step=1141300) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 06:04:06] (step=1141400) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 06:04:46] (step=1141500) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 06:05:26] (step=1141600) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 06:06:07] (step=1141700) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-20 06:06:47] (step=1141800) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 06:07:28] (step=1141900) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 06:08:08] (step=1142000) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 06:08:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 06:09:00] (step=1142100) Train Loss: 0.0671, Train Steps/Sec: 1.91, Samples/Sec: 68.83
+[2025-05-20 06:09:41] (step=1142200) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 06:10:21] (step=1142300) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 06:11:01] (step=1142400) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 06:11:42] (step=1142500) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 06:12:22] (step=1142600) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 06:13:02] (step=1142700) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 06:13:43] (step=1142800) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 06:14:23] (step=1142900) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-20 06:15:04] (step=1143000) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 06:15:44] (step=1143100) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 06:16:24] (step=1143200) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-20 06:17:05] (step=1143300) Train Loss: 0.0713, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 06:17:13] Beginning epoch 283...
+[2025-05-20 06:17:46] (step=1143400) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.40
+[2025-05-20 06:18:26] (step=1143500) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 06:19:06] (step=1143600) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 06:19:47] (step=1143700) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 06:20:27] (step=1143800) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 06:21:07] (step=1143900) Train Loss: 0.0712, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 06:21:48] (step=1144000) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 06:22:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 06:22:41] (step=1144100) Train Loss: 0.0672, Train Steps/Sec: 1.88, Samples/Sec: 67.55
+[2025-05-20 06:23:21] (step=1144200) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 06:24:02] (step=1144300) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 06:24:42] (step=1144400) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 06:25:23] (step=1144500) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 06:26:03] (step=1144600) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 06:26:43] (step=1144700) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 06:27:24] (step=1144800) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 06:28:04] (step=1144900) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 06:28:44] (step=1145000) Train Loss: 0.0732, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 06:29:25] (step=1145100) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 06:30:05] (step=1145200) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 06:30:45] (step=1145300) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 06:31:26] (step=1145400) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 06:32:06] (step=1145500) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 06:32:46] (step=1145600) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 06:33:27] (step=1145700) Train Loss: 0.0717, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 06:34:07] (step=1145800) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 06:34:48] (step=1145900) Train Loss: 0.0733, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 06:35:28] (step=1146000) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 06:35:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 06:36:20] (step=1146100) Train Loss: 0.0674, Train Steps/Sec: 1.91, Samples/Sec: 68.76
+[2025-05-20 06:37:01] (step=1146200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 06:37:41] (step=1146300) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 06:38:21] (step=1146400) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 06:39:02] (step=1146500) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 06:39:42] (step=1146600) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 06:40:22] (step=1146700) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 06:41:03] (step=1146800) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 06:41:43] (step=1146900) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 06:42:23] (step=1147000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 06:43:04] (step=1147100) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 06:43:44] (step=1147200) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 06:44:24] (step=1147300) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 06:44:49] Beginning epoch 284...
+[2025-05-20 06:45:05] (step=1147400) Train Loss: 0.0637, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-20 06:45:45] (step=1147500) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-20 06:46:26] (step=1147600) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 06:47:06] (step=1147700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 06:47:46] (step=1147800) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 06:48:27] (step=1147900) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 06:49:07] (step=1148000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 06:49:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 06:50:00] (step=1148100) Train Loss: 0.0673, Train Steps/Sec: 1.91, Samples/Sec: 68.79
+[2025-05-20 06:50:40] (step=1148200) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 06:51:20] (step=1148300) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 06:52:01] (step=1148400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 06:52:41] (step=1148500) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 06:53:21] (step=1148600) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 06:54:02] (step=1148700) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 06:54:42] (step=1148800) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 06:55:22] (step=1148900) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 06:56:03] (step=1149000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 06:56:43] (step=1149100) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 06:57:24] (step=1149200) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 06:58:04] (step=1149300) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 06:58:44] (step=1149400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 06:59:25] (step=1149500) Train Loss: 0.0636, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-20 07:00:05] (step=1149600) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 07:00:45] (step=1149700) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:01:26] (step=1149800) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 07:02:06] (step=1149900) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 07:02:46] (step=1150000) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 07:02:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 07:03:01] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 07:05:01] (step=1150000) Perceptual Loss: 0.3025, Eval Time: 122.29
+[2025-05-20 07:05:41] (step=1150100) Train Loss: 0.0708, Train Steps/Sec: 0.57, Samples/Sec: 20.61
+[2025-05-20 07:06:21] (step=1150200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 07:07:02] (step=1150300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:07:42] (step=1150400) Train Loss: 0.0616, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:08:22] (step=1150500) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 07:09:03] (step=1150600) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 07:09:43] (step=1150700) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 07:10:23] (step=1150800) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:11:04] (step=1150900) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:11:44] (step=1151000) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 07:12:24] (step=1151100) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:13:05] (step=1151200) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 07:13:45] (step=1151300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:14:26] (step=1151400) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 07:14:26] Beginning epoch 285...
+[2025-05-20 07:15:06] (step=1151500) Train Loss: 0.0706, Train Steps/Sec: 2.46, Samples/Sec: 88.57
+[2025-05-20 07:15:47] (step=1151600) Train Loss: 0.0697, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 07:16:27] (step=1151700) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:17:07] (step=1151800) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 07:17:48] (step=1151900) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:18:28] (step=1152000) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:18:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 07:19:20] (step=1152100) Train Loss: 0.0687, Train Steps/Sec: 1.91, Samples/Sec: 68.94
+[2025-05-20 07:20:00] (step=1152200) Train Loss: 0.0701, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 07:20:41] (step=1152300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:21:21] (step=1152400) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:22:02] (step=1152500) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:22:42] (step=1152600) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 07:23:22] (step=1152700) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:24:03] (step=1152800) Train Loss: 0.0680, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 07:24:43] (step=1152900) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 07:25:23] (step=1153000) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 07:26:04] (step=1153100) Train Loss: 0.0709, Train Steps/Sec: 2.48, Samples/Sec: 89.32
+[2025-05-20 07:26:44] (step=1153200) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:27:24] (step=1153300) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:28:05] (step=1153400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 07:28:45] (step=1153500) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:29:25] (step=1153600) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 07:30:06] (step=1153700) Train Loss: 0.0687, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 07:30:46] (step=1153800) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 07:31:26] (step=1153900) Train Loss: 0.0657, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 07:32:07] (step=1154000) Train Loss: 0.0642, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:32:19] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 07:32:59] (step=1154100) Train Loss: 0.0692, Train Steps/Sec: 1.92, Samples/Sec: 69.03
+[2025-05-20 07:33:39] (step=1154200) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:34:20] (step=1154300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 07:35:00] (step=1154400) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:35:40] (step=1154500) Train Loss: 0.0656, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 07:36:21] (step=1154600) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 07:37:01] (step=1154700) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 07:37:41] (step=1154800) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 07:38:22] (step=1154900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:39:02] (step=1155000) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:39:42] (step=1155100) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:40:23] (step=1155200) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:41:03] (step=1155300) Train Loss: 0.0643, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 07:41:43] (step=1155400) Train Loss: 0.0735, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 07:42:00] Beginning epoch 286...
+[2025-05-20 07:42:24] (step=1155500) Train Loss: 0.0647, Train Steps/Sec: 2.46, Samples/Sec: 88.62
+[2025-05-20 07:43:04] (step=1155600) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-20 07:43:45] (step=1155700) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-20 07:44:25] (step=1155800) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:45:05] (step=1155900) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:45:46] (step=1156000) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 07:45:58] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 07:46:38] (step=1156100) Train Loss: 0.0619, Train Steps/Sec: 1.91, Samples/Sec: 68.63
+[2025-05-20 07:47:19] (step=1156200) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:47:59] (step=1156300) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:48:39] (step=1156400) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 07:49:20] (step=1156500) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:50:00] (step=1156600) Train Loss: 0.0637, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 07:50:40] (step=1156700) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 07:51:21] (step=1156800) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:52:01] (step=1156900) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 07:52:41] (step=1157000) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 07:53:22] (step=1157100) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-20 07:54:02] (step=1157200) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.33
+[2025-05-20 07:54:42] (step=1157300) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 07:55:23] (step=1157400) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 07:56:03] (step=1157500) Train Loss: 0.0626, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-20 07:56:43] (step=1157600) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 07:57:24] (step=1157700) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 07:58:04] (step=1157800) Train Loss: 0.0627, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 07:58:44] (step=1157900) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 07:59:25] (step=1158000) Train Loss: 0.0639, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 07:59:37] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 08:00:17] (step=1158100) Train Loss: 0.0716, Train Steps/Sec: 1.90, Samples/Sec: 68.49
+[2025-05-20 08:00:58] (step=1158200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 08:01:38] (step=1158300) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 08:02:18] (step=1158400) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 08:02:59] (step=1158500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 08:03:39] (step=1158600) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 08:04:20] (step=1158700) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 08:05:00] (step=1158800) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-20 08:05:40] (step=1158900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 08:06:21] (step=1159000) Train Loss: 0.0660, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 08:07:01] (step=1159100) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 08:07:41] (step=1159200) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 08:08:22] (step=1159300) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 08:09:02] (step=1159400) Train Loss: 0.0675, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 08:09:34] Beginning epoch 287...
+[2025-05-20 08:09:43] (step=1159500) Train Loss: 0.0630, Train Steps/Sec: 2.46, Samples/Sec: 88.56
+[2025-05-20 08:10:23] (step=1159600) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.31
+[2025-05-20 08:11:03] (step=1159700) Train Loss: 0.0700, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 08:11:44] (step=1159800) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 08:12:24] (step=1159900) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 08:13:04] (step=1160000) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 08:13:28] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1160000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 08:13:32] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 08:15:31] (step=1160000) Perceptual Loss: 0.3103, Eval Time: 122.75
+[2025-05-20 08:16:12] (step=1160100) Train Loss: 0.0748, Train Steps/Sec: 0.53, Samples/Sec: 19.23
+[2025-05-20 08:16:52] (step=1160200) Train Loss: 0.0658, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 08:17:32] (step=1160300) Train Loss: 0.0633, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 08:18:13] (step=1160400) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 08:18:53] (step=1160500) Train Loss: 0.0707, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 08:19:33] (step=1160600) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 08:20:14] (step=1160700) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 08:20:54] (step=1160800) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 08:21:34] (step=1160900) Train Loss: 0.0644, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 08:22:15] (step=1161000) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 08:22:55] (step=1161100) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 08:23:36] (step=1161200) Train Loss: 0.0691, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 08:24:16] (step=1161300) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 08:24:56] (step=1161400) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 08:25:37] (step=1161500) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 08:26:17] (step=1161600) Train Loss: 0.0705, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 08:26:57] (step=1161700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-20 08:27:38] (step=1161800) Train Loss: 0.0730, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 08:28:18] (step=1161900) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 08:28:58] (step=1162000) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 08:29:11] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 08:29:51] (step=1162100) Train Loss: 0.0711, Train Steps/Sec: 1.90, Samples/Sec: 68.50
+[2025-05-20 08:30:31] (step=1162200) Train Loss: 0.0651, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 08:31:12] (step=1162300) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 08:31:52] (step=1162400) Train Loss: 0.0673, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 08:32:32] (step=1162500) Train Loss: 0.0623, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 08:33:13] (step=1162600) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 08:33:53] (step=1162700) Train Loss: 0.0638, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 08:34:34] (step=1162800) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 08:35:14] (step=1162900) Train Loss: 0.0671, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 08:35:54] (step=1163000) Train Loss: 0.0643, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 08:36:35] (step=1163100) Train Loss: 0.0711, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 08:37:15] (step=1163200) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.30
+[2025-05-20 08:37:55] (step=1163300) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 08:38:36] (step=1163400) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 08:39:16] (step=1163500) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 08:39:24] Beginning epoch 288...
+[2025-05-20 08:39:57] (step=1163600) Train Loss: 0.0641, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-20 08:40:37] (step=1163700) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 08:41:17] (step=1163800) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 08:41:58] (step=1163900) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 08:42:38] (step=1164000) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 08:42:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 08:43:30] (step=1164100) Train Loss: 0.0640, Train Steps/Sec: 1.91, Samples/Sec: 68.88
+[2025-05-20 08:44:11] (step=1164200) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 08:44:51] (step=1164300) Train Loss: 0.0668, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 08:45:31] (step=1164400) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.18
+[2025-05-20 08:46:12] (step=1164500) Train Loss: 0.0622, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 08:46:52] (step=1164600) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 08:47:33] (step=1164700) Train Loss: 0.0736, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 08:48:13] (step=1164800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 08:48:53] (step=1164900) Train Loss: 0.0628, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 08:49:34] (step=1165000) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 08:50:14] (step=1165100) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 08:50:54] (step=1165200) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 08:51:35] (step=1165300) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 08:52:15] (step=1165400) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 08:52:56] (step=1165500) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 08:53:36] (step=1165600) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-20 08:54:16] (step=1165700) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 08:54:57] (step=1165800) Train Loss: 0.0703, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 08:55:37] (step=1165900) Train Loss: 0.0659, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-20 08:56:18] (step=1166000) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 08:56:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 08:57:10] (step=1166100) Train Loss: 0.0681, Train Steps/Sec: 1.90, Samples/Sec: 68.56
+[2025-05-20 08:57:50] (step=1166200) Train Loss: 0.0654, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 08:58:31] (step=1166300) Train Loss: 0.0679, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 08:59:11] (step=1166400) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 08:59:51] (step=1166500) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 09:00:32] (step=1166600) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 09:01:12] (step=1166700) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.21
+[2025-05-20 09:01:53] (step=1166800) Train Loss: 0.0721, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 09:02:33] (step=1166900) Train Loss: 0.0698, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 09:03:13] (step=1167000) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 09:03:54] (step=1167100) Train Loss: 0.0724, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 09:04:34] (step=1167200) Train Loss: 0.0640, Train Steps/Sec: 2.48, Samples/Sec: 89.24
+[2025-05-20 09:05:15] (step=1167300) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 09:05:55] (step=1167400) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 09:06:35] (step=1167500) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 09:06:59] Beginning epoch 289...
+[2025-05-20 09:07:16] (step=1167600) Train Loss: 0.0657, Train Steps/Sec: 2.46, Samples/Sec: 88.67
+[2025-05-20 09:07:56] (step=1167700) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 09:08:37] (step=1167800) Train Loss: 0.0699, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 09:09:17] (step=1167900) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.17
+[2025-05-20 09:09:57] (step=1168000) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 09:10:09] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 09:10:50] (step=1168100) Train Loss: 0.0724, Train Steps/Sec: 1.91, Samples/Sec: 68.92
+[2025-05-20 09:11:30] (step=1168200) Train Loss: 0.0622, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 09:12:10] (step=1168300) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 09:12:51] (step=1168400) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 09:13:31] (step=1168500) Train Loss: 0.0665, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 09:14:12] (step=1168600) Train Loss: 0.0617, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 09:14:52] (step=1168700) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 09:15:32] (step=1168800) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 09:16:13] (step=1168900) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 09:16:53] (step=1169000) Train Loss: 0.0653, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-20 09:17:33] (step=1169100) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 09:18:14] (step=1169200) Train Loss: 0.0630, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 09:18:54] (step=1169300) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 09:19:35] (step=1169400) Train Loss: 0.0648, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-20 09:20:15] (step=1169500) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 09:20:55] (step=1169600) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-20 09:21:36] (step=1169700) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 09:22:16] (step=1169800) Train Loss: 0.0609, Train Steps/Sec: 2.48, Samples/Sec: 89.25
+[2025-05-20 09:22:56] (step=1169900) Train Loss: 0.0704, Train Steps/Sec: 2.48, Samples/Sec: 89.23
+[2025-05-20 09:23:37] (step=1170000) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 09:23:50] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 09:23:53] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 09:25:52] (step=1170000) Perceptual Loss: 0.3120, Eval Time: 122.57
+[2025-05-20 09:26:33] (step=1170100) Train Loss: 0.0713, Train Steps/Sec: 0.57, Samples/Sec: 20.45
+[2025-05-20 09:27:13] (step=1170200) Train Loss: 0.0627, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 09:27:54] (step=1170300) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 09:28:34] (step=1170400) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 09:29:15] (step=1170500) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 09:29:55] (step=1170600) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 09:30:36] (step=1170700) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.61
+[2025-05-20 09:31:16] (step=1170800) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 09:31:57] (step=1170900) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 09:32:37] (step=1171000) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 09:33:18] (step=1171100) Train Loss: 0.0740, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 09:33:58] (step=1171200) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 09:34:39] (step=1171300) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 09:35:19] (step=1171400) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.79
+[2025-05-20 09:36:00] (step=1171500) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 09:36:40] (step=1171600) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 09:36:40] Beginning epoch 290...
+[2025-05-20 09:37:21] (step=1171700) Train Loss: 0.0680, Train Steps/Sec: 2.45, Samples/Sec: 88.18
+[2025-05-20 09:38:01] (step=1171800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 09:38:42] (step=1171900) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 09:39:22] (step=1172000) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 09:39:36] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 09:40:16] (step=1172100) Train Loss: 0.0607, Train Steps/Sec: 1.86, Samples/Sec: 66.79
+[2025-05-20 09:40:57] (step=1172200) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 09:41:37] (step=1172300) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 09:42:18] (step=1172400) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 09:42:58] (step=1172500) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 09:43:38] (step=1172600) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 09:44:19] (step=1172700) Train Loss: 0.0620, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-20 09:44:59] (step=1172800) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 09:45:40] (step=1172900) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 09:46:20] (step=1173000) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 09:47:01] (step=1173100) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 09:47:41] (step=1173200) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 09:48:22] (step=1173300) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 09:49:02] (step=1173400) Train Loss: 0.0666, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-20 09:49:43] (step=1173500) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 09:50:23] (step=1173600) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 09:51:04] (step=1173700) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-20 09:51:44] (step=1173800) Train Loss: 0.0722, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 09:52:25] (step=1173900) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 09:53:05] (step=1174000) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 09:53:18] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 09:53:59] (step=1174100) Train Loss: 0.0691, Train Steps/Sec: 1.85, Samples/Sec: 66.70
+[2025-05-20 09:54:39] (step=1174200) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 09:55:20] (step=1174300) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 09:56:00] (step=1174400) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 09:56:41] (step=1174500) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 09:57:21] (step=1174600) Train Loss: 0.0689, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 09:58:02] (step=1174700) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 09:58:42] (step=1174800) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-20 09:59:23] (step=1174900) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:00:03] (step=1175000) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 10:00:44] (step=1175100) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 10:01:24] (step=1175200) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 10:02:05] (step=1175300) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 10:02:45] (step=1175400) Train Loss: 0.0720, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:03:26] (step=1175500) Train Loss: 0.0653, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-20 10:04:06] (step=1175600) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 10:04:22] Beginning epoch 291...
+[2025-05-20 10:04:47] (step=1175700) Train Loss: 0.0676, Train Steps/Sec: 2.45, Samples/Sec: 88.34
+[2025-05-20 10:05:27] (step=1175800) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 10:06:08] (step=1175900) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 10:06:48] (step=1176000) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 10:07:02] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 10:07:42] (step=1176100) Train Loss: 0.0692, Train Steps/Sec: 1.84, Samples/Sec: 66.33
+[2025-05-20 10:08:23] (step=1176200) Train Loss: 0.0688, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 10:09:03] (step=1176300) Train Loss: 0.0725, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 10:09:44] (step=1176400) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 10:10:24] (step=1176500) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 10:11:05] (step=1176600) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 10:11:45] (step=1176700) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 10:12:26] (step=1176800) Train Loss: 0.0653, Train Steps/Sec: 2.46, Samples/Sec: 88.72
+[2025-05-20 10:13:06] (step=1176900) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 10:13:47] (step=1177000) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 10:14:27] (step=1177100) Train Loss: 0.0684, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 10:15:07] (step=1177200) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:15:48] (step=1177300) Train Loss: 0.0733, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 10:16:28] (step=1177400) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 10:17:09] (step=1177500) Train Loss: 0.0681, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-20 10:17:49] (step=1177600) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 10:18:30] (step=1177700) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 10:19:10] (step=1177800) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:19:51] (step=1177900) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 10:20:31] (step=1178000) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 10:20:44] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 10:21:25] (step=1178100) Train Loss: 0.0664, Train Steps/Sec: 1.87, Samples/Sec: 67.21
+[2025-05-20 10:22:05] (step=1178200) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 10:22:46] (step=1178300) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 10:23:26] (step=1178400) Train Loss: 0.0718, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 10:24:07] (step=1178500) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 10:24:47] (step=1178600) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 10:25:27] (step=1178700) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 10:26:08] (step=1178800) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-20 10:26:48] (step=1178900) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 10:27:29] (step=1179000) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 10:28:09] (step=1179100) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 10:28:50] (step=1179200) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 10:29:30] (step=1179300) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 10:30:11] (step=1179400) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 10:30:51] (step=1179500) Train Loss: 0.0715, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-20 10:31:32] (step=1179600) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:32:04] Beginning epoch 292...
+[2025-05-20 10:32:12] (step=1179700) Train Loss: 0.0687, Train Steps/Sec: 2.46, Samples/Sec: 88.40
+[2025-05-20 10:32:53] (step=1179800) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 10:33:33] (step=1179900) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 10:34:14] (step=1180000) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 10:34:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1180000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 10:34:41] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 10:36:40] (step=1180000) Perceptual Loss: 0.3220, Eval Time: 122.55
+[2025-05-20 10:37:21] (step=1180100) Train Loss: 0.0690, Train Steps/Sec: 0.53, Samples/Sec: 19.20
+[2025-05-20 10:38:02] (step=1180200) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 10:38:42] (step=1180300) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 10:39:23] (step=1180400) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 10:40:03] (step=1180500) Train Loss: 0.0665, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 10:40:43] (step=1180600) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 10:41:24] (step=1180700) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 10:42:04] (step=1180800) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 10:42:45] (step=1180900) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 10:43:25] (step=1181000) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-20 10:44:06] (step=1181100) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 10:44:46] (step=1181200) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 10:45:27] (step=1181300) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 10:46:07] (step=1181400) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.82
+[2025-05-20 10:46:48] (step=1181500) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-20 10:47:28] (step=1181600) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:48:09] (step=1181700) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 10:48:49] (step=1181800) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 10:49:30] (step=1181900) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:50:10] (step=1182000) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 10:50:23] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 10:51:03] (step=1182100) Train Loss: 0.0655, Train Steps/Sec: 1.89, Samples/Sec: 67.94
+[2025-05-20 10:51:44] (step=1182200) Train Loss: 0.0778, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 10:52:24] (step=1182300) Train Loss: 0.0632, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 10:53:05] (step=1182400) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:53:45] (step=1182500) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 10:54:25] (step=1182600) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 10:55:06] (step=1182700) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 10:55:47] (step=1182800) Train Loss: 0.0663, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-20 10:56:27] (step=1182900) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 10:57:08] (step=1183000) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 10:57:48] (step=1183100) Train Loss: 0.0742, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:58:28] (step=1183200) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 10:59:09] (step=1183300) Train Loss: 0.0616, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 10:59:49] (step=1183400) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 11:00:30] (step=1183500) Train Loss: 0.0674, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 11:01:10] (step=1183600) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 11:01:51] (step=1183700) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 11:01:59] Beginning epoch 293...
+[2025-05-20 11:02:32] (step=1183800) Train Loss: 0.0714, Train Steps/Sec: 2.45, Samples/Sec: 88.32
+[2025-05-20 11:03:12] (step=1183900) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 11:03:53] (step=1184000) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 11:04:05] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 11:04:46] (step=1184100) Train Loss: 0.0680, Train Steps/Sec: 1.89, Samples/Sec: 67.95
+[2025-05-20 11:05:26] (step=1184200) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 11:06:06] (step=1184300) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 11:06:47] (step=1184400) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 11:07:27] (step=1184500) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 11:08:08] (step=1184600) Train Loss: 0.0615, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 11:08:48] (step=1184700) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 11:09:29] (step=1184800) Train Loss: 0.0693, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-20 11:10:09] (step=1184900) Train Loss: 0.0745, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 11:10:50] (step=1185000) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 88.81
+[2025-05-20 11:11:30] (step=1185100) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 11:12:11] (step=1185200) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 11:12:51] (step=1185300) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 11:13:32] (step=1185400) Train Loss: 0.0714, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 11:14:12] (step=1185500) Train Loss: 0.0694, Train Steps/Sec: 2.46, Samples/Sec: 88.55
+[2025-05-20 11:14:53] (step=1185600) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 11:15:33] (step=1185700) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 11:16:14] (step=1185800) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 11:16:54] (step=1185900) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 11:17:35] (step=1186000) Train Loss: 0.0696, Train Steps/Sec: 2.47, Samples/Sec: 89.03
+[2025-05-20 11:17:48] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 11:18:28] (step=1186100) Train Loss: 0.0687, Train Steps/Sec: 1.86, Samples/Sec: 67.00
+[2025-05-20 11:19:09] (step=1186200) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 11:19:49] (step=1186300) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 11:20:30] (step=1186400) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 11:21:10] (step=1186500) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 11:21:51] (step=1186600) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 11:22:31] (step=1186700) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 11:23:12] (step=1186800) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 11:23:52] (step=1186900) Train Loss: 0.0649, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 11:24:33] (step=1187000) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.98
+[2025-05-20 11:25:13] (step=1187100) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 11:25:54] (step=1187200) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 11:26:34] (step=1187300) Train Loss: 0.0699, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 11:27:14] (step=1187400) Train Loss: 0.0636, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 11:27:55] (step=1187500) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 11:28:35] (step=1187600) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 11:29:16] (step=1187700) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 11:29:40] Beginning epoch 294...
+[2025-05-20 11:29:57] (step=1187800) Train Loss: 0.0668, Train Steps/Sec: 2.45, Samples/Sec: 88.36
+[2025-05-20 11:30:37] (step=1187900) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 11:31:18] (step=1188000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 11:31:30] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 11:32:11] (step=1188100) Train Loss: 0.0670, Train Steps/Sec: 1.88, Samples/Sec: 67.55
+[2025-05-20 11:32:51] (step=1188200) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 11:33:32] (step=1188300) Train Loss: 0.0719, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 11:34:12] (step=1188400) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 11:34:52] (step=1188500) Train Loss: 0.0684, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 11:35:33] (step=1188600) Train Loss: 0.0611, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 11:36:13] (step=1188700) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 11:36:54] (step=1188800) Train Loss: 0.0626, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 11:37:34] (step=1188900) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 11:38:14] (step=1189000) Train Loss: 0.0650, Train Steps/Sec: 2.48, Samples/Sec: 89.22
+[2025-05-20 11:38:55] (step=1189100) Train Loss: 0.0703, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 11:39:35] (step=1189200) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.27
+[2025-05-20 11:40:15] (step=1189300) Train Loss: 0.0652, Train Steps/Sec: 2.48, Samples/Sec: 89.26
+[2025-05-20 11:40:56] (step=1189400) Train Loss: 0.0619, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 11:41:36] (step=1189500) Train Loss: 0.0689, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 11:42:17] (step=1189600) Train Loss: 0.0664, Train Steps/Sec: 2.47, Samples/Sec: 88.75
+[2025-05-20 11:42:57] (step=1189700) Train Loss: 0.0641, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 11:43:38] (step=1189800) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 11:44:18] (step=1189900) Train Loss: 0.0690, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 11:44:58] (step=1190000) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 11:45:12] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 11:45:15] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 11:47:15] (step=1190000) Perceptual Loss: 0.2937, Eval Time: 122.89
+[2025-05-20 11:47:56] (step=1190100) Train Loss: 0.0740, Train Steps/Sec: 0.56, Samples/Sec: 20.31
+[2025-05-20 11:48:36] (step=1190200) Train Loss: 0.0671, Train Steps/Sec: 2.46, Samples/Sec: 88.68
+[2025-05-20 11:49:17] (step=1190300) Train Loss: 0.0638, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 11:49:57] (step=1190400) Train Loss: 0.0720, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 11:50:38] (step=1190500) Train Loss: 0.0634, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 11:51:18] (step=1190600) Train Loss: 0.0669, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 11:51:58] (step=1190700) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 11:52:39] (step=1190800) Train Loss: 0.0604, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 11:53:19] (step=1190900) Train Loss: 0.0598, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 11:54:00] (step=1191000) Train Loss: 0.0678, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 11:54:40] (step=1191100) Train Loss: 0.0706, Train Steps/Sec: 2.48, Samples/Sec: 89.28
+[2025-05-20 11:55:20] (step=1191200) Train Loss: 0.0670, Train Steps/Sec: 2.48, Samples/Sec: 89.20
+[2025-05-20 11:56:01] (step=1191300) Train Loss: 0.0634, Train Steps/Sec: 2.48, Samples/Sec: 89.19
+[2025-05-20 11:56:41] (step=1191400) Train Loss: 0.0605, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 11:57:22] (step=1191500) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.29
+[2025-05-20 11:58:02] (step=1191600) Train Loss: 0.0710, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 11:58:42] (step=1191700) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 11:59:23] (step=1191800) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.16
+[2025-05-20 11:59:23] Beginning epoch 295...
+[2025-05-20 12:00:04] (step=1191900) Train Loss: 0.0655, Train Steps/Sec: 2.46, Samples/Sec: 88.41
+[2025-05-20 12:00:44] (step=1192000) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 12:00:57] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 12:01:37] (step=1192100) Train Loss: 0.0631, Train Steps/Sec: 1.88, Samples/Sec: 67.56
+[2025-05-20 12:02:18] (step=1192200) Train Loss: 0.0716, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 12:02:58] (step=1192300) Train Loss: 0.0667, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 12:03:39] (step=1192400) Train Loss: 0.0669, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 12:04:19] (step=1192500) Train Loss: 0.0685, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 12:04:59] (step=1192600) Train Loss: 0.0635, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 12:05:40] (step=1192700) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 12:06:20] (step=1192800) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 12:07:01] (step=1192900) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 12:07:41] (step=1193000) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 12:08:22] (step=1193100) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 12:09:02] (step=1193200) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 12:09:43] (step=1193300) Train Loss: 0.0677, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 12:10:23] (step=1193400) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 12:11:04] (step=1193500) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 12:11:44] (step=1193600) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 12:12:25] (step=1193700) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 12:13:05] (step=1193800) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 12:13:45] (step=1193900) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 12:14:26] (step=1194000) Train Loss: 0.0651, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 12:14:38] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 12:15:18] (step=1194100) Train Loss: 0.0660, Train Steps/Sec: 1.91, Samples/Sec: 68.61
+[2025-05-20 12:15:59] (step=1194200) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 12:16:39] (step=1194300) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.00
+[2025-05-20 12:17:20] (step=1194400) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.80
+[2025-05-20 12:18:00] (step=1194500) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 12:18:41] (step=1194600) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 12:19:21] (step=1194700) Train Loss: 0.0625, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 12:20:02] (step=1194800) Train Loss: 0.0638, Train Steps/Sec: 2.46, Samples/Sec: 88.71
+[2025-05-20 12:20:42] (step=1194900) Train Loss: 0.0648, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-20 12:21:23] (step=1195000) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 88.86
+[2025-05-20 12:22:03] (step=1195100) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 12:22:44] (step=1195200) Train Loss: 0.0694, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 12:23:24] (step=1195300) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 88.99
+[2025-05-20 12:24:05] (step=1195400) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 12:24:45] (step=1195500) Train Loss: 0.0652, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 12:25:26] (step=1195600) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 12:26:06] (step=1195700) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 12:26:47] (step=1195800) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 12:27:03] Beginning epoch 296...
+[2025-05-20 12:27:27] (step=1195900) Train Loss: 0.0660, Train Steps/Sec: 2.45, Samples/Sec: 88.37
+[2025-05-20 12:28:08] (step=1196000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 12:28:20] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 12:29:00] (step=1196100) Train Loss: 0.0698, Train Steps/Sec: 1.91, Samples/Sec: 68.61
+[2025-05-20 12:29:41] (step=1196200) Train Loss: 0.0708, Train Steps/Sec: 2.47, Samples/Sec: 88.83
+[2025-05-20 12:30:21] (step=1196300) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 12:31:02] (step=1196400) Train Loss: 0.0701, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 12:31:42] (step=1196500) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 12:32:23] (step=1196600) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 12:33:03] (step=1196700) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 12:33:44] (step=1196800) Train Loss: 0.0699, Train Steps/Sec: 2.46, Samples/Sec: 88.69
+[2025-05-20 12:34:24] (step=1196900) Train Loss: 0.0670, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 12:35:04] (step=1197000) Train Loss: 0.0621, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 12:35:45] (step=1197100) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 12:36:25] (step=1197200) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 12:37:06] (step=1197300) Train Loss: 0.0659, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 12:37:46] (step=1197400) Train Loss: 0.0628, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 12:38:27] (step=1197500) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 12:39:07] (step=1197600) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 12:39:48] (step=1197700) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 12:40:28] (step=1197800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 12:41:08] (step=1197900) Train Loss: 0.0681, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 12:41:49] (step=1198000) Train Loss: 0.0663, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 12:42:01] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 12:42:42] (step=1198100) Train Loss: 0.0687, Train Steps/Sec: 1.89, Samples/Sec: 67.93
+[2025-05-20 12:43:22] (step=1198200) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 12:44:03] (step=1198300) Train Loss: 0.0655, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 12:44:43] (step=1198400) Train Loss: 0.0637, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 12:45:24] (step=1198500) Train Loss: 0.0624, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 12:46:04] (step=1198600) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 12:46:44] (step=1198700) Train Loss: 0.0691, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 12:47:25] (step=1198800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 12:48:05] (step=1198900) Train Loss: 0.0672, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 12:48:46] (step=1199000) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 12:49:26] (step=1199100) Train Loss: 0.0642, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 12:50:07] (step=1199200) Train Loss: 0.0702, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 12:50:47] (step=1199300) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 12:51:28] (step=1199400) Train Loss: 0.0717, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 12:52:08] (step=1199500) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 12:52:49] (step=1199600) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 12:53:29] (step=1199700) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 12:54:10] (step=1199800) Train Loss: 0.0687, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 12:54:42] Beginning epoch 297...
+[2025-05-20 12:54:50] (step=1199900) Train Loss: 0.0675, Train Steps/Sec: 2.45, Samples/Sec: 88.36
+[2025-05-20 12:55:31] (step=1200000) Train Loss: 0.0645, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 12:55:56] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/1200000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 12:55:59] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 12:57:58] (step=1200000) Perceptual Loss: 0.3084, Eval Time: 122.31
+[2025-05-20 12:58:39] (step=1200100) Train Loss: 0.0648, Train Steps/Sec: 0.53, Samples/Sec: 19.15
+[2025-05-20 12:59:19] (step=1200200) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 13:00:00] (step=1200300) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.84
+[2025-05-20 13:00:40] (step=1200400) Train Loss: 0.0629, Train Steps/Sec: 2.47, Samples/Sec: 88.96
+[2025-05-20 13:01:21] (step=1200500) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 13:02:01] (step=1200600) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 13:02:42] (step=1200700) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 13:03:22] (step=1200800) Train Loss: 0.0686, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 13:04:02] (step=1200900) Train Loss: 0.0656, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 13:04:43] (step=1201000) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 13:05:23] (step=1201100) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 13:06:04] (step=1201200) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.05
+[2025-05-20 13:06:44] (step=1201300) Train Loss: 0.0610, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 13:07:25] (step=1201400) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.90
+[2025-05-20 13:08:05] (step=1201500) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 13:08:46] (step=1201600) Train Loss: 0.0657, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 13:09:26] (step=1201700) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 13:10:07] (step=1201800) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 13:10:47] (step=1201900) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 13:11:27] (step=1202000) Train Loss: 0.0693, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 13:11:40] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 13:12:21] (step=1202100) Train Loss: 0.0628, Train Steps/Sec: 1.88, Samples/Sec: 67.68
+[2025-05-20 13:13:01] (step=1202200) Train Loss: 0.0692, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 13:13:41] (step=1202300) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 88.97
+[2025-05-20 13:14:22] (step=1202400) Train Loss: 0.0661, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 13:15:02] (step=1202500) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 13:15:43] (step=1202600) Train Loss: 0.0672, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 13:16:23] (step=1202700) Train Loss: 0.0694, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 13:17:04] (step=1202800) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 88.74
+[2025-05-20 13:17:44] (step=1202900) Train Loss: 0.0710, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 13:18:25] (step=1203000) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 88.93
+[2025-05-20 13:19:05] (step=1203100) Train Loss: 0.0671, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 13:19:45] (step=1203200) Train Loss: 0.0685, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+[2025-05-20 13:20:26] (step=1203300) Train Loss: 0.0676, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 13:21:06] (step=1203400) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 13:21:47] (step=1203500) Train Loss: 0.0619, Train Steps/Sec: 2.47, Samples/Sec: 88.88
+[2025-05-20 13:22:27] (step=1203600) Train Loss: 0.0723, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 13:23:08] (step=1203700) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 13:23:48] (step=1203800) Train Loss: 0.0653, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 13:24:28] (step=1203900) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 13:24:37] Beginning epoch 298...
+[2025-05-20 13:25:09] (step=1204000) Train Loss: 0.0618, Train Steps/Sec: 2.46, Samples/Sec: 88.46
+[2025-05-20 13:25:22] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 13:26:03] (step=1204100) Train Loss: 0.0636, Train Steps/Sec: 1.86, Samples/Sec: 66.90
+[2025-05-20 13:26:43] (step=1204200) Train Loss: 0.0715, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 13:27:24] (step=1204300) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 13:28:04] (step=1204400) Train Loss: 0.0678, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 13:28:45] (step=1204500) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 13:29:25] (step=1204600) Train Loss: 0.0686, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 13:30:05] (step=1204700) Train Loss: 0.0621, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 13:30:46] (step=1204800) Train Loss: 0.0729, Train Steps/Sec: 2.46, Samples/Sec: 88.73
+[2025-05-20 13:31:26] (step=1204900) Train Loss: 0.0644, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 13:32:07] (step=1205000) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 13:32:47] (step=1205100) Train Loss: 0.0646, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 13:33:28] (step=1205200) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 13:34:08] (step=1205300) Train Loss: 0.0658, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 13:34:49] (step=1205400) Train Loss: 0.0683, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 13:35:29] (step=1205500) Train Loss: 0.0641, Train Steps/Sec: 2.46, Samples/Sec: 88.70
+[2025-05-20 13:36:10] (step=1205600) Train Loss: 0.0682, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 13:36:50] (step=1205700) Train Loss: 0.0666, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 13:37:31] (step=1205800) Train Loss: 0.0660, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 13:38:11] (step=1205900) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 89.04
+[2025-05-20 13:38:51] (step=1206000) Train Loss: 0.0711, Train Steps/Sec: 2.47, Samples/Sec: 89.06
+[2025-05-20 13:39:03] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 13:39:44] (step=1206100) Train Loss: 0.0679, Train Steps/Sec: 1.90, Samples/Sec: 68.40
+[2025-05-20 13:40:24] (step=1206200) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 13:41:05] (step=1206300) Train Loss: 0.0683, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 13:41:45] (step=1206400) Train Loss: 0.0647, Train Steps/Sec: 2.47, Samples/Sec: 88.89
+[2025-05-20 13:42:26] (step=1206500) Train Loss: 0.0720, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 13:43:06] (step=1206600) Train Loss: 0.0695, Train Steps/Sec: 2.47, Samples/Sec: 89.02
+[2025-05-20 13:43:47] (step=1206700) Train Loss: 0.0677, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 13:44:27] (step=1206800) Train Loss: 0.0680, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 13:45:08] (step=1206900) Train Loss: 0.0733, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 13:45:48] (step=1207000) Train Loss: 0.0706, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 13:46:28] (step=1207100) Train Loss: 0.0624, Train Steps/Sec: 2.47, Samples/Sec: 89.08
+[2025-05-20 13:47:09] (step=1207200) Train Loss: 0.0645, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 13:47:49] (step=1207300) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 13:48:30] (step=1207400) Train Loss: 0.0702, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 13:49:10] (step=1207500) Train Loss: 0.0681, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 13:49:51] (step=1207600) Train Loss: 0.0666, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 13:50:31] (step=1207700) Train Loss: 0.0698, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 13:51:11] (step=1207800) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 13:51:52] (step=1207900) Train Loss: 0.0667, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 13:52:16] Beginning epoch 299...
+[2025-05-20 13:52:32] (step=1208000) Train Loss: 0.0696, Train Steps/Sec: 2.46, Samples/Sec: 88.50
+[2025-05-20 13:52:45] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 13:53:25] (step=1208100) Train Loss: 0.0663, Train Steps/Sec: 1.90, Samples/Sec: 68.44
+[2025-05-20 13:54:06] (step=1208200) Train Loss: 0.0639, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 13:54:46] (step=1208300) Train Loss: 0.0709, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 13:55:27] (step=1208400) Train Loss: 0.0673, Train Steps/Sec: 2.47, Samples/Sec: 88.95
+[2025-05-20 13:56:07] (step=1208500) Train Loss: 0.0674, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 13:56:47] (step=1208600) Train Loss: 0.0649, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 13:57:28] (step=1208700) Train Loss: 0.0654, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 13:58:08] (step=1208800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 88.77
+[2025-05-20 13:58:49] (step=1208900) Train Loss: 0.0662, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 13:59:29] (step=1209000) Train Loss: 0.0675, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 14:00:10] (step=1209100) Train Loss: 0.0640, Train Steps/Sec: 2.47, Samples/Sec: 88.85
+[2025-05-20 14:00:50] (step=1209200) Train Loss: 0.0688, Train Steps/Sec: 2.47, Samples/Sec: 89.10
+[2025-05-20 14:01:30] (step=1209300) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 89.01
+[2025-05-20 14:02:11] (step=1209400) Train Loss: 0.0708, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 14:02:51] (step=1209500) Train Loss: 0.0607, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 14:03:32] (step=1209600) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.87
+[2025-05-20 14:04:12] (step=1209700) Train Loss: 0.0662, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 14:04:53] (step=1209800) Train Loss: 0.0676, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 14:05:33] (step=1209900) Train Loss: 0.0641, Train Steps/Sec: 2.48, Samples/Sec: 89.10
+[2025-05-20 14:06:14] (step=1210000) Train Loss: 0.0646, Train Steps/Sec: 2.47, Samples/Sec: 89.07
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 14:06:26] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-20 14:06:28] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-20 14:08:28] (step=1210000) Perceptual Loss: 0.3147, Eval Time: 122.15
+[2025-05-20 14:09:08] (step=1210100) Train Loss: 0.0700, Train Steps/Sec: 0.57, Samples/Sec: 20.58
+[2025-05-20 14:09:49] (step=1210200) Train Loss: 0.0704, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 14:10:29] (step=1210300) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.91
+[2025-05-20 14:11:10] (step=1210400) Train Loss: 0.0715, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 14:11:50] (step=1210500) Train Loss: 0.0661, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 14:12:31] (step=1210600) Train Loss: 0.0729, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 14:13:11] (step=1210700) Train Loss: 0.0697, Train Steps/Sec: 2.47, Samples/Sec: 88.94
+[2025-05-20 14:13:52] (step=1210800) Train Loss: 0.0664, Train Steps/Sec: 2.48, Samples/Sec: 89.13
+[2025-05-20 14:14:32] (step=1210900) Train Loss: 0.0679, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 14:15:12] (step=1211000) Train Loss: 0.0668, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 14:15:53] (step=1211100) Train Loss: 0.0700, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 14:16:33] (step=1211200) Train Loss: 0.0647, Train Steps/Sec: 2.48, Samples/Sec: 89.11
+[2025-05-20 14:17:14] (step=1211300) Train Loss: 0.0655, Train Steps/Sec: 2.48, Samples/Sec: 89.14
+[2025-05-20 14:17:54] (step=1211400) Train Loss: 0.0650, Train Steps/Sec: 2.47, Samples/Sec: 88.92
+[2025-05-20 14:18:35] (step=1211500) Train Loss: 0.0693, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 14:19:15] (step=1211600) Train Loss: 0.0692, Train Steps/Sec: 2.47, Samples/Sec: 88.76
+[2025-05-20 14:19:56] (step=1211700) Train Loss: 0.0696, Train Steps/Sec: 2.48, Samples/Sec: 89.12
+[2025-05-20 14:20:36] (step=1211800) Train Loss: 0.0630, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 14:21:16] (step=1211900) Train Loss: 0.0705, Train Steps/Sec: 2.47, Samples/Sec: 89.09
+[2025-05-20 14:21:57] (step=1212000) Train Loss: 0.0625, Train Steps/Sec: 2.48, Samples/Sec: 89.15
+[2025-05-20 14:22:10] Saved checkpoint to logs/nwm_cdit_wuhan_union/checkpoints/latest.pth.tar
+[2025-05-20 14:22:10] Done!
diff --git a/wuhan.log b/wuhan.log
new file mode 100644
index 0000000000000000000000000000000000000000..3cd112ce13e4b9a48df02ff7f8aad70daf8c5865
--- /dev/null
+++ b/wuhan.log
@@ -0,0 +1,1488 @@
+[2025-05-13 05:54:02,175] torch.distributed.run: [WARNING] 
+[2025-05-13 05:54:02,175] torch.distributed.run: [WARNING] *****************************************
+[2025-05-13 05:54:02,175] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+[2025-05-13 05:54:02,175] torch.distributed.run: [WARNING] *****************************************
+[2025-05-13 05:54:04] Experiment directory created at logs/nwm_cdit_wuhan
+[2025-05-13 05:54:18] CDiT Parameters: 1,011,959,456
+[2025-05-13 05:54:18] Dataset contains 18,087 images
+[2025-05-13 05:54:18] Training for 300 epochs...
+[2025-05-13 05:54:18] Beginning epoch 0...
+[2025-05-13 05:55:17] (step=0000100) Train Loss: 0.3423, Train Steps/Sec: 1.71, Samples/Sec: 81.94
+[2025-05-13 05:56:13] (step=0000200) Train Loss: 0.2075, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 05:57:08] (step=0000300) Train Loss: 0.2066, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 05:58:03] (step=0000400) Train Loss: 0.2031, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 05:58:59] (step=0000500) Train Loss: 0.1905, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 05:59:54] (step=0000600) Train Loss: 0.1960, Train Steps/Sec: 1.81, Samples/Sec: 86.69
+[2025-05-13 06:00:49] (step=0000700) Train Loss: 0.1888, Train Steps/Sec: 1.81, Samples/Sec: 86.69
+[2025-05-13 06:01:45] (step=0000800) Train Loss: 0.1828, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 06:02:40] (step=0000900) Train Loss: 0.1918, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 06:03:36] (step=0001000) Train Loss: 0.1865, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 06:04:31] (step=0001100) Train Loss: 0.1762, Train Steps/Sec: 1.81, Samples/Sec: 86.68
+[2025-05-13 06:05:27] (step=0001200) Train Loss: 0.1835, Train Steps/Sec: 1.81, Samples/Sec: 86.68
+[2025-05-13 06:06:22] (step=0001300) Train Loss: 0.1856, Train Steps/Sec: 1.81, Samples/Sec: 86.68
+[2025-05-13 06:07:17] (step=0001400) Train Loss: 0.1729, Train Steps/Sec: 1.81, Samples/Sec: 86.67
+[2025-05-13 06:08:13] (step=0001500) Train Loss: 0.1745, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 06:08:17] Beginning epoch 1...
+[2025-05-13 06:09:09] (step=0001600) Train Loss: 0.1763, Train Steps/Sec: 1.79, Samples/Sec: 86.07
+[2025-05-13 06:10:04] (step=0001700) Train Loss: 0.1751, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 06:10:59] (step=0001800) Train Loss: 0.1682, Train Steps/Sec: 1.81, Samples/Sec: 86.68
+[2025-05-13 06:11:55] (step=0001900) Train Loss: 0.1787, Train Steps/Sec: 1.81, Samples/Sec: 86.68
+[2025-05-13 06:12:50] (step=0002000) Train Loss: 0.1791, Train Steps/Sec: 1.81, Samples/Sec: 86.67
+[2025-05-13 06:13:09] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 06:14:04] (step=0002100) Train Loss: 0.1670, Train Steps/Sec: 1.35, Samples/Sec: 65.04
+[2025-05-13 06:14:59] (step=0002200) Train Loss: 0.1740, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 06:15:55] (step=0002300) Train Loss: 0.1668, Train Steps/Sec: 1.81, Samples/Sec: 86.68
+[2025-05-13 06:16:50] (step=0002400) Train Loss: 0.1725, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 06:17:46] (step=0002500) Train Loss: 0.1634, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 06:18:41] (step=0002600) Train Loss: 0.1724, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 06:19:36] (step=0002700) Train Loss: 0.1689, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 06:20:32] (step=0002800) Train Loss: 0.1624, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 06:21:27] (step=0002900) Train Loss: 0.1719, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 06:22:23] (step=0003000) Train Loss: 0.1587, Train Steps/Sec: 1.81, Samples/Sec: 86.68
+[2025-05-13 06:22:31] Beginning epoch 2...
+[2025-05-13 06:23:19] (step=0003100) Train Loss: 0.1628, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 06:24:14] (step=0003200) Train Loss: 0.1617, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 06:25:09] (step=0003300) Train Loss: 0.1537, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 06:26:05] (step=0003400) Train Loss: 0.1583, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 06:27:00] (step=0003500) Train Loss: 0.1598, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 06:27:56] (step=0003600) Train Loss: 0.1561, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 06:28:51] (step=0003700) Train Loss: 0.1588, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 06:29:47] (step=0003800) Train Loss: 0.1601, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 06:30:42] (step=0003900) Train Loss: 0.1571, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 06:31:37] (step=0004000) Train Loss: 0.1595, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 06:31:57] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 06:32:53] (step=0004100) Train Loss: 0.1559, Train Steps/Sec: 1.33, Samples/Sec: 63.71
+[2025-05-13 06:33:48] (step=0004200) Train Loss: 0.1526, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 06:34:44] (step=0004300) Train Loss: 0.1561, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 06:35:39] (step=0004400) Train Loss: 0.1562, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 06:36:34] (step=0004500) Train Loss: 0.1519, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 06:36:46] Beginning epoch 3...
+[2025-05-13 06:37:30] (step=0004600) Train Loss: 0.1551, Train Steps/Sec: 1.79, Samples/Sec: 86.03
+[2025-05-13 06:38:26] (step=0004700) Train Loss: 0.1505, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 06:39:21] (step=0004800) Train Loss: 0.1571, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 06:40:17] (step=0004900) Train Loss: 0.1551, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 06:41:12] (step=0005000) Train Loss: 0.1535, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 06:42:08] (step=0005100) Train Loss: 0.1536, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 06:43:03] (step=0005200) Train Loss: 0.1455, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 06:43:59] (step=0005300) Train Loss: 0.1484, Train Steps/Sec: 1.80, Samples/Sec: 86.47
+[2025-05-13 06:44:54] (step=0005400) Train Loss: 0.1534, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 06:45:50] (step=0005500) Train Loss: 0.1503, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 06:46:45] (step=0005600) Train Loss: 0.1527, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 06:47:40] (step=0005700) Train Loss: 0.1488, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 06:48:36] (step=0005800) Train Loss: 0.1547, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 06:49:31] (step=0005900) Train Loss: 0.1467, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 06:50:27] (step=0006000) Train Loss: 0.1450, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 06:50:43] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 06:50:58] Beginning epoch 4...
+[2025-05-13 06:51:39] (step=0006100) Train Loss: 0.1495, Train Steps/Sec: 1.39, Samples/Sec: 66.80
+[2025-05-13 06:52:34] (step=0006200) Train Loss: 0.1497, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 06:53:30] (step=0006300) Train Loss: 0.1489, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 06:54:25] (step=0006400) Train Loss: 0.1394, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 06:55:20] (step=0006500) Train Loss: 0.1478, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 06:56:16] (step=0006600) Train Loss: 0.1451, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 06:57:11] (step=0006700) Train Loss: 0.1444, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 06:58:07] (step=0006800) Train Loss: 0.1474, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 06:59:02] (step=0006900) Train Loss: 0.1453, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 06:59:58] (step=0007000) Train Loss: 0.1398, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 07:00:53] (step=0007100) Train Loss: 0.1453, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 07:01:49] (step=0007200) Train Loss: 0.1432, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 07:02:44] (step=0007300) Train Loss: 0.1433, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 07:03:40] (step=0007400) Train Loss: 0.1398, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 07:04:35] (step=0007500) Train Loss: 0.1452, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 07:04:55] Beginning epoch 5...
+[2025-05-13 07:05:31] (step=0007600) Train Loss: 0.1403, Train Steps/Sec: 1.79, Samples/Sec: 86.03
+[2025-05-13 07:06:26] (step=0007700) Train Loss: 0.1461, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 07:07:22] (step=0007800) Train Loss: 0.1452, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 07:08:17] (step=0007900) Train Loss: 0.1367, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 07:09:13] (step=0008000) Train Loss: 0.1430, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 07:09:28] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 07:10:24] (step=0008100) Train Loss: 0.1416, Train Steps/Sec: 1.41, Samples/Sec: 67.71
+[2025-05-13 07:11:19] (step=0008200) Train Loss: 0.1358, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 07:12:15] (step=0008300) Train Loss: 0.1418, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 07:13:10] (step=0008400) Train Loss: 0.1399, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 07:14:06] (step=0008500) Train Loss: 0.1444, Train Steps/Sec: 1.80, Samples/Sec: 86.47
+[2025-05-13 07:15:01] (step=0008600) Train Loss: 0.1387, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 07:15:56] (step=0008700) Train Loss: 0.1368, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 07:16:52] (step=0008800) Train Loss: 0.1365, Train Steps/Sec: 1.80, Samples/Sec: 86.47
+[2025-05-13 07:17:47] (step=0008900) Train Loss: 0.1446, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 07:18:43] (step=0009000) Train Loss: 0.1334, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 07:19:06] Beginning epoch 6...
+[2025-05-13 07:19:39] (step=0009100) Train Loss: 0.1331, Train Steps/Sec: 1.79, Samples/Sec: 85.94
+[2025-05-13 07:20:34] (step=0009200) Train Loss: 0.1356, Train Steps/Sec: 1.81, Samples/Sec: 86.71
+[2025-05-13 07:21:29] (step=0009300) Train Loss: 0.1347, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 07:22:25] (step=0009400) Train Loss: 0.1382, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 07:23:20] (step=0009500) Train Loss: 0.1373, Train Steps/Sec: 1.81, Samples/Sec: 86.67
+[2025-05-13 07:24:16] (step=0009600) Train Loss: 0.1407, Train Steps/Sec: 1.81, Samples/Sec: 86.67
+[2025-05-13 07:25:11] (step=0009700) Train Loss: 0.1360, Train Steps/Sec: 1.81, Samples/Sec: 86.70
+[2025-05-13 07:26:06] (step=0009800) Train Loss: 0.1327, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 07:27:02] (step=0009900) Train Loss: 0.1364, Train Steps/Sec: 1.81, Samples/Sec: 86.70
+[2025-05-13 07:27:57] (step=0010000) Train Loss: 0.1352, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+[2025-05-13 07:28:16] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
+  return self.fget.__get__(instance, owner)()
+/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+[2025-05-13 07:28:19] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 07:30:39] (step=0010000) Perceptual Loss: 0.8698, Eval Time: 142.88
+[2025-05-13 07:31:34] (step=0010100) Train Loss: 0.1326, Train Steps/Sec: 0.46, Samples/Sec: 22.11
+[2025-05-13 07:32:30] (step=0010200) Train Loss: 0.1319, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 07:33:25] (step=0010300) Train Loss: 0.1290, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 07:34:21] (step=0010400) Train Loss: 0.1340, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 07:35:16] (step=0010500) Train Loss: 0.1377, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 07:35:43] Beginning epoch 7...
+[2025-05-13 07:36:12] (step=0010600) Train Loss: 0.1359, Train Steps/Sec: 1.79, Samples/Sec: 85.73
+[2025-05-13 07:37:08] (step=0010700) Train Loss: 0.1393, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 07:38:03] (step=0010800) Train Loss: 0.1318, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 07:38:59] (step=0010900) Train Loss: 0.1265, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 07:39:54] (step=0011000) Train Loss: 0.1317, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 07:40:50] (step=0011100) Train Loss: 0.1289, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 07:41:45] (step=0011200) Train Loss: 0.1278, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 07:42:41] (step=0011300) Train Loss: 0.1305, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 07:43:36] (step=0011400) Train Loss: 0.1365, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 07:44:31] (step=0011500) Train Loss: 0.1352, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 07:45:27] (step=0011600) Train Loss: 0.1309, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 07:46:22] (step=0011700) Train Loss: 0.1333, Train Steps/Sec: 1.80, Samples/Sec: 86.47
+[2025-05-13 07:47:18] (step=0011800) Train Loss: 0.1295, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 07:48:13] (step=0011900) Train Loss: 0.1308, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 07:49:09] (step=0012000) Train Loss: 0.1290, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 07:49:23] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 07:49:54] Beginning epoch 8...
+[2025-05-13 07:50:19] (step=0012100) Train Loss: 0.1296, Train Steps/Sec: 1.44, Samples/Sec: 68.91
+[2025-05-13 07:51:14] (step=0012200) Train Loss: 0.1299, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 07:52:10] (step=0012300) Train Loss: 0.1296, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 07:53:05] (step=0012400) Train Loss: 0.1255, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 07:54:00] (step=0012500) Train Loss: 0.1269, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 07:54:56] (step=0012600) Train Loss: 0.1294, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 07:55:52] (step=0012700) Train Loss: 0.1310, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 07:56:47] (step=0012800) Train Loss: 0.1293, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 07:57:42] (step=0012900) Train Loss: 0.1259, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 07:58:38] (step=0013000) Train Loss: 0.1327, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 07:59:33] (step=0013100) Train Loss: 0.1314, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 08:00:29] (step=0013200) Train Loss: 0.1287, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 08:01:24] (step=0013300) Train Loss: 0.1335, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 08:02:20] (step=0013400) Train Loss: 0.1277, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 08:03:15] (step=0013500) Train Loss: 0.1271, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 08:03:50] Beginning epoch 9...
+[2025-05-13 08:04:11] (step=0013600) Train Loss: 0.1257, Train Steps/Sec: 1.79, Samples/Sec: 85.85
+[2025-05-13 08:05:07] (step=0013700) Train Loss: 0.1265, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 08:06:02] (step=0013800) Train Loss: 0.1261, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 08:06:58] (step=0013900) Train Loss: 0.1240, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 08:07:53] (step=0014000) Train Loss: 0.1286, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 08:08:07] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 08:09:02] (step=0014100) Train Loss: 0.1335, Train Steps/Sec: 1.44, Samples/Sec: 69.29
+[2025-05-13 08:09:58] (step=0014200) Train Loss: 0.1241, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 08:10:53] (step=0014300) Train Loss: 0.1183, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 08:11:49] (step=0014400) Train Loss: 0.1243, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 08:12:44] (step=0014500) Train Loss: 0.1255, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 08:13:40] (step=0014600) Train Loss: 0.1255, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 08:14:35] (step=0014700) Train Loss: 0.1266, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 08:15:31] (step=0014800) Train Loss: 0.1237, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 08:16:26] (step=0014900) Train Loss: 0.1210, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 08:17:22] (step=0015000) Train Loss: 0.1265, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 08:18:00] Beginning epoch 10...
+[2025-05-13 08:18:17] (step=0015100) Train Loss: 0.1246, Train Steps/Sec: 1.79, Samples/Sec: 86.07
+[2025-05-13 08:19:13] (step=0015200) Train Loss: 0.1254, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 08:20:08] (step=0015300) Train Loss: 0.1248, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 08:21:04] (step=0015400) Train Loss: 0.1251, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 08:21:59] (step=0015500) Train Loss: 0.1271, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 08:22:55] (step=0015600) Train Loss: 0.1212, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 08:23:50] (step=0015700) Train Loss: 0.1219, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 08:24:46] (step=0015800) Train Loss: 0.1247, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 08:25:41] (step=0015900) Train Loss: 0.1197, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 08:26:37] (step=0016000) Train Loss: 0.1208, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 08:26:51] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 08:27:46] (step=0016100) Train Loss: 0.1279, Train Steps/Sec: 1.44, Samples/Sec: 69.19
+[2025-05-13 08:28:42] (step=0016200) Train Loss: 0.1211, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 08:29:37] (step=0016300) Train Loss: 0.1213, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 08:30:33] (step=0016400) Train Loss: 0.1197, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 08:31:28] (step=0016500) Train Loss: 0.1275, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 08:32:11] Beginning epoch 11...
+[2025-05-13 08:32:24] (step=0016600) Train Loss: 0.1247, Train Steps/Sec: 1.79, Samples/Sec: 86.03
+[2025-05-13 08:33:19] (step=0016700) Train Loss: 0.1202, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 08:34:15] (step=0016800) Train Loss: 0.1163, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 08:35:10] (step=0016900) Train Loss: 0.1194, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 08:36:06] (step=0017000) Train Loss: 0.1214, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 08:37:01] (step=0017100) Train Loss: 0.1241, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 08:37:57] (step=0017200) Train Loss: 0.1201, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 08:38:52] (step=0017300) Train Loss: 0.1215, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 08:39:48] (step=0017400) Train Loss: 0.1184, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 08:40:43] (step=0017500) Train Loss: 0.1161, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 08:41:39] (step=0017600) Train Loss: 0.1309, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 08:42:34] (step=0017700) Train Loss: 0.1216, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 08:43:30] (step=0017800) Train Loss: 0.1184, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 08:44:25] (step=0017900) Train Loss: 0.1210, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 08:45:21] (step=0018000) Train Loss: 0.1237, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 08:45:34] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 08:46:21] Beginning epoch 12...
+[2025-05-13 08:46:30] (step=0018100) Train Loss: 0.1226, Train Steps/Sec: 1.44, Samples/Sec: 68.97
+[2025-05-13 08:47:26] (step=0018200) Train Loss: 0.1181, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 08:48:21] (step=0018300) Train Loss: 0.1218, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 08:49:17] (step=0018400) Train Loss: 0.1236, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 08:50:12] (step=0018500) Train Loss: 0.1257, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 08:51:07] (step=0018600) Train Loss: 0.1151, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 08:52:03] (step=0018700) Train Loss: 0.1143, Train Steps/Sec: 1.80, Samples/Sec: 86.42
+[2025-05-13 08:52:58] (step=0018800) Train Loss: 0.1159, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 08:53:54] (step=0018900) Train Loss: 0.1203, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 08:54:49] (step=0019000) Train Loss: 0.1206, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 08:55:45] (step=0019100) Train Loss: 0.1206, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 08:56:40] (step=0019200) Train Loss: 0.1189, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 08:57:36] (step=0019300) Train Loss: 0.1210, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 08:58:31] (step=0019400) Train Loss: 0.1226, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 08:59:27] (step=0019500) Train Loss: 0.1198, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 09:00:17] Beginning epoch 13...
+[2025-05-13 09:00:23] (step=0019600) Train Loss: 0.1179, Train Steps/Sec: 1.79, Samples/Sec: 86.08
+[2025-05-13 09:01:18] (step=0019700) Train Loss: 0.1249, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 09:02:13] (step=0019800) Train Loss: 0.1151, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 09:03:09] (step=0019900) Train Loss: 0.1148, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 09:04:04] (step=0020000) Train Loss: 0.1131, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 09:04:31] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/0020000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 09:04:34] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 09:06:53] (step=0020000) Perceptual Loss: 0.6528, Eval Time: 142.38
+[2025-05-13 09:07:49] (step=0020100) Train Loss: 0.1198, Train Steps/Sec: 0.45, Samples/Sec: 21.40
+[2025-05-13 09:08:44] (step=0020200) Train Loss: 0.1192, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 09:09:40] (step=0020300) Train Loss: 0.1166, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 09:10:35] (step=0020400) Train Loss: 0.1138, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 09:11:31] (step=0020500) Train Loss: 0.1143, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 09:12:26] (step=0020600) Train Loss: 0.1176, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 09:13:22] (step=0020700) Train Loss: 0.1121, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 09:14:17] (step=0020800) Train Loss: 0.1174, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 09:15:13] (step=0020900) Train Loss: 0.1197, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 09:16:08] (step=0021000) Train Loss: 0.1166, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 09:17:02] Beginning epoch 14...
+[2025-05-13 09:17:04] (step=0021100) Train Loss: 0.1199, Train Steps/Sec: 1.79, Samples/Sec: 86.09
+[2025-05-13 09:17:59] (step=0021200) Train Loss: 0.1205, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 09:18:55] (step=0021300) Train Loss: 0.1201, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 09:19:51] (step=0021400) Train Loss: 0.1196, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 09:20:46] (step=0021500) Train Loss: 0.1165, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 09:21:42] (step=0021600) Train Loss: 0.1191, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 09:22:37] (step=0021700) Train Loss: 0.1133, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 09:23:32] (step=0021800) Train Loss: 0.1188, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 09:24:28] (step=0021900) Train Loss: 0.1197, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 09:25:24] (step=0022000) Train Loss: 0.1182, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 09:25:38] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 09:26:34] (step=0022100) Train Loss: 0.1171, Train Steps/Sec: 1.43, Samples/Sec: 68.43
+[2025-05-13 09:27:29] (step=0022200) Train Loss: 0.1150, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 09:28:25] (step=0022300) Train Loss: 0.1146, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 09:29:20] (step=0022400) Train Loss: 0.1114, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 09:30:16] (step=0022500) Train Loss: 0.1153, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 09:31:11] (step=0022600) Train Loss: 0.1170, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 09:31:14] Beginning epoch 15...
+[2025-05-13 09:32:07] (step=0022700) Train Loss: 0.1181, Train Steps/Sec: 1.79, Samples/Sec: 85.78
+[2025-05-13 09:33:03] (step=0022800) Train Loss: 0.1133, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 09:33:58] (step=0022900) Train Loss: 0.1132, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 09:34:54] (step=0023000) Train Loss: 0.1127, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 09:35:49] (step=0023100) Train Loss: 0.1182, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 09:36:44] (step=0023200) Train Loss: 0.1114, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 09:37:40] (step=0023300) Train Loss: 0.1101, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 09:38:36] (step=0023400) Train Loss: 0.1135, Train Steps/Sec: 1.80, Samples/Sec: 86.42
+[2025-05-13 09:39:31] (step=0023500) Train Loss: 0.1099, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 09:40:27] (step=0023600) Train Loss: 0.1072, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 09:41:22] (step=0023700) Train Loss: 0.1113, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 09:42:17] (step=0023800) Train Loss: 0.1112, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 09:43:13] (step=0023900) Train Loss: 0.1185, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 09:44:08] (step=0024000) Train Loss: 0.1094, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 09:44:22] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 09:45:18] (step=0024100) Train Loss: 0.1128, Train Steps/Sec: 1.44, Samples/Sec: 69.30
+[2025-05-13 09:45:24] Beginning epoch 16...
+[2025-05-13 09:46:14] (step=0024200) Train Loss: 0.1140, Train Steps/Sec: 1.79, Samples/Sec: 86.07
+[2025-05-13 09:47:09] (step=0024300) Train Loss: 0.1079, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 09:48:04] (step=0024400) Train Loss: 0.1168, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 09:49:00] (step=0024500) Train Loss: 0.1167, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 09:49:55] (step=0024600) Train Loss: 0.1112, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 09:50:51] (step=0024700) Train Loss: 0.1121, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 09:51:46] (step=0024800) Train Loss: 0.1156, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 09:52:42] (step=0024900) Train Loss: 0.1175, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 09:53:37] (step=0025000) Train Loss: 0.1148, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 09:54:33] (step=0025100) Train Loss: 0.1115, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 09:55:28] (step=0025200) Train Loss: 0.1102, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 09:56:24] (step=0025300) Train Loss: 0.1105, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 09:57:19] (step=0025400) Train Loss: 0.1112, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 09:58:15] (step=0025500) Train Loss: 0.1101, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 09:59:10] (step=0025600) Train Loss: 0.1107, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 09:59:21] Beginning epoch 17...
+[2025-05-13 10:00:06] (step=0025700) Train Loss: 0.1100, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 10:01:01] (step=0025800) Train Loss: 0.1187, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 10:01:57] (step=0025900) Train Loss: 0.1155, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 10:02:53] (step=0026000) Train Loss: 0.1136, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 10:03:06] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 10:04:01] (step=0026100) Train Loss: 0.1106, Train Steps/Sec: 1.46, Samples/Sec: 70.16
+[2025-05-13 10:04:56] (step=0026200) Train Loss: 0.1074, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 10:05:52] (step=0026300) Train Loss: 0.1104, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 10:06:47] (step=0026400) Train Loss: 0.1131, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 10:07:43] (step=0026500) Train Loss: 0.1105, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 10:08:38] (step=0026600) Train Loss: 0.1078, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 10:09:34] (step=0026700) Train Loss: 0.1083, Train Steps/Sec: 1.78, Samples/Sec: 85.44
+[2025-05-13 10:10:31] (step=0026800) Train Loss: 0.1108, Train Steps/Sec: 1.77, Samples/Sec: 85.13
+[2025-05-13 10:11:27] (step=0026900) Train Loss: 0.1090, Train Steps/Sec: 1.79, Samples/Sec: 85.97
+[2025-05-13 10:12:22] (step=0027000) Train Loss: 0.1062, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 10:13:18] (step=0027100) Train Loss: 0.1089, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 10:13:32] Beginning epoch 18...
+[2025-05-13 10:14:14] (step=0027200) Train Loss: 0.1054, Train Steps/Sec: 1.78, Samples/Sec: 85.23
+[2025-05-13 10:15:10] (step=0027300) Train Loss: 0.1159, Train Steps/Sec: 1.77, Samples/Sec: 85.18
+[2025-05-13 10:16:07] (step=0027400) Train Loss: 0.1039, Train Steps/Sec: 1.77, Samples/Sec: 85.09
+[2025-05-13 10:17:03] (step=0027500) Train Loss: 0.1141, Train Steps/Sec: 1.77, Samples/Sec: 84.84
+[2025-05-13 10:17:59] (step=0027600) Train Loss: 0.1079, Train Steps/Sec: 1.78, Samples/Sec: 85.65
+[2025-05-13 10:18:55] (step=0027700) Train Loss: 0.1086, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 10:19:50] (step=0027800) Train Loss: 0.1104, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 10:20:46] (step=0027900) Train Loss: 0.1151, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 10:21:42] (step=0028000) Train Loss: 0.1118, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 10:21:54] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 10:22:50] (step=0028100) Train Loss: 0.1100, Train Steps/Sec: 1.46, Samples/Sec: 70.32
+[2025-05-13 10:23:45] (step=0028200) Train Loss: 0.1103, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 10:24:41] (step=0028300) Train Loss: 0.1107, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 10:25:36] (step=0028400) Train Loss: 0.1088, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 10:26:32] (step=0028500) Train Loss: 0.1099, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 10:27:27] (step=0028600) Train Loss: 0.1122, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 10:27:46] Beginning epoch 19...
+[2025-05-13 10:28:23] (step=0028700) Train Loss: 0.1079, Train Steps/Sec: 1.78, Samples/Sec: 85.61
+[2025-05-13 10:29:19] (step=0028800) Train Loss: 0.1054, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 10:30:14] (step=0028900) Train Loss: 0.1083, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 10:31:10] (step=0029000) Train Loss: 0.1081, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 10:32:05] (step=0029100) Train Loss: 0.1031, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 10:33:01] (step=0029200) Train Loss: 0.1084, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 10:33:56] (step=0029300) Train Loss: 0.1092, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 10:34:52] (step=0029400) Train Loss: 0.1110, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 10:35:48] (step=0029500) Train Loss: 0.1080, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 10:36:43] (step=0029600) Train Loss: 0.1094, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 10:37:39] (step=0029700) Train Loss: 0.1073, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 10:38:34] (step=0029800) Train Loss: 0.1098, Train Steps/Sec: 1.80, Samples/Sec: 86.47
+[2025-05-13 10:39:30] (step=0029900) Train Loss: 0.1057, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 10:40:25] (step=0030000) Train Loss: 0.1110, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 10:40:38] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 10:40:41] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 10:43:01] (step=0030000) Perceptual Loss: 0.4381, Eval Time: 142.79
+[2025-05-13 10:43:56] (step=0030100) Train Loss: 0.1080, Train Steps/Sec: 0.47, Samples/Sec: 22.71
+[2025-05-13 10:44:19] Beginning epoch 20...
+[2025-05-13 10:44:52] (step=0030200) Train Loss: 0.1052, Train Steps/Sec: 1.79, Samples/Sec: 85.87
+[2025-05-13 10:45:48] (step=0030300) Train Loss: 0.1031, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 10:46:43] (step=0030400) Train Loss: 0.1123, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 10:47:39] (step=0030500) Train Loss: 0.1108, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 10:48:35] (step=0030600) Train Loss: 0.1108, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 10:49:30] (step=0030700) Train Loss: 0.1072, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 10:50:26] (step=0030800) Train Loss: 0.1079, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 10:51:21] (step=0030900) Train Loss: 0.1015, Train Steps/Sec: 1.80, Samples/Sec: 86.44
+[2025-05-13 10:52:17] (step=0031000) Train Loss: 0.1026, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 10:53:12] (step=0031100) Train Loss: 0.1092, Train Steps/Sec: 1.80, Samples/Sec: 86.44
+[2025-05-13 10:54:08] (step=0031200) Train Loss: 0.1112, Train Steps/Sec: 1.80, Samples/Sec: 86.44
+[2025-05-13 10:55:04] (step=0031300) Train Loss: 0.1112, Train Steps/Sec: 1.80, Samples/Sec: 86.23
+[2025-05-13 10:55:59] (step=0031400) Train Loss: 0.1026, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 10:56:55] (step=0031500) Train Loss: 0.0985, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 10:57:50] (step=0031600) Train Loss: 0.1062, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 10:58:16] Beginning epoch 21...
+[2025-05-13 10:58:46] (step=0031700) Train Loss: 0.1071, Train Steps/Sec: 1.79, Samples/Sec: 85.97
+[2025-05-13 10:59:42] (step=0031800) Train Loss: 0.1080, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 11:00:37] (step=0031900) Train Loss: 0.1085, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 11:01:33] (step=0032000) Train Loss: 0.1050, Train Steps/Sec: 1.79, Samples/Sec: 86.07
+[2025-05-13 11:01:46] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 11:02:41] (step=0032100) Train Loss: 0.1043, Train Steps/Sec: 1.47, Samples/Sec: 70.35
+[2025-05-13 11:03:37] (step=0032200) Train Loss: 0.1020, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 11:04:32] (step=0032300) Train Loss: 0.1016, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 11:05:28] (step=0032400) Train Loss: 0.1122, Train Steps/Sec: 1.80, Samples/Sec: 86.44
+[2025-05-13 11:06:23] (step=0032500) Train Loss: 0.1104, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 11:07:19] (step=0032600) Train Loss: 0.1016, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 11:08:14] (step=0032700) Train Loss: 0.1061, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 11:09:10] (step=0032800) Train Loss: 0.1069, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 11:10:06] (step=0032900) Train Loss: 0.1071, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 11:11:01] (step=0033000) Train Loss: 0.1041, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 11:11:57] (step=0033100) Train Loss: 0.1091, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 11:12:27] Beginning epoch 22...
+[2025-05-13 11:12:53] (step=0033200) Train Loss: 0.1075, Train Steps/Sec: 1.79, Samples/Sec: 85.80
+[2025-05-13 11:13:48] (step=0033300) Train Loss: 0.1126, Train Steps/Sec: 1.79, Samples/Sec: 86.13
+[2025-05-13 11:14:44] (step=0033400) Train Loss: 0.1013, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 11:15:40] (step=0033500) Train Loss: 0.1032, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 11:16:35] (step=0033600) Train Loss: 0.1101, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 11:17:31] (step=0033700) Train Loss: 0.1042, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 11:18:26] (step=0033800) Train Loss: 0.0978, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 11:19:22] (step=0033900) Train Loss: 0.1067, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 11:20:18] (step=0034000) Train Loss: 0.1099, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 11:20:30] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 11:21:26] (step=0034100) Train Loss: 0.1052, Train Steps/Sec: 1.46, Samples/Sec: 70.16
+[2025-05-13 11:22:22] (step=0034200) Train Loss: 0.1034, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 11:23:17] (step=0034300) Train Loss: 0.1035, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 11:24:13] (step=0034400) Train Loss: 0.1035, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 11:25:09] (step=0034500) Train Loss: 0.0997, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 11:26:04] (step=0034600) Train Loss: 0.1038, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 11:26:38] Beginning epoch 23...
+[2025-05-13 11:27:00] (step=0034700) Train Loss: 0.1017, Train Steps/Sec: 1.78, Samples/Sec: 85.60
+[2025-05-13 11:27:56] (step=0034800) Train Loss: 0.1023, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 11:28:51] (step=0034900) Train Loss: 0.1072, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 11:29:47] (step=0035000) Train Loss: 0.1070, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 11:30:43] (step=0035100) Train Loss: 0.1049, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 11:31:38] (step=0035200) Train Loss: 0.1065, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 11:32:34] (step=0035300) Train Loss: 0.1055, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 11:33:29] (step=0035400) Train Loss: 0.1020, Train Steps/Sec: 1.80, Samples/Sec: 86.16
+[2025-05-13 11:34:25] (step=0035500) Train Loss: 0.1091, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 11:35:21] (step=0035600) Train Loss: 0.1054, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 11:36:16] (step=0035700) Train Loss: 0.0990, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 11:37:12] (step=0035800) Train Loss: 0.1027, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 11:38:07] (step=0035900) Train Loss: 0.1038, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 11:39:03] (step=0036000) Train Loss: 0.1028, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 11:39:16] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 11:40:11] (step=0036100) Train Loss: 0.1091, Train Steps/Sec: 1.46, Samples/Sec: 70.27
+[2025-05-13 11:40:49] Beginning epoch 24...
+[2025-05-13 11:41:07] (step=0036200) Train Loss: 0.1029, Train Steps/Sec: 1.79, Samples/Sec: 85.81
+[2025-05-13 11:42:03] (step=0036300) Train Loss: 0.1090, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 11:42:58] (step=0036400) Train Loss: 0.1035, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 11:43:54] (step=0036500) Train Loss: 0.0995, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 11:44:50] (step=0036600) Train Loss: 0.1063, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 11:45:45] (step=0036700) Train Loss: 0.1042, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 11:46:41] (step=0036800) Train Loss: 0.1047, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 11:47:36] (step=0036900) Train Loss: 0.1048, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 11:48:32] (step=0037000) Train Loss: 0.0977, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 11:49:28] (step=0037100) Train Loss: 0.1009, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 11:50:23] (step=0037200) Train Loss: 0.1050, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 11:51:19] (step=0037300) Train Loss: 0.1039, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 11:52:15] (step=0037400) Train Loss: 0.1053, Train Steps/Sec: 1.79, Samples/Sec: 86.16
+[2025-05-13 11:53:10] (step=0037500) Train Loss: 0.0938, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 11:54:06] (step=0037600) Train Loss: 0.1021, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 11:54:47] Beginning epoch 25...
+[2025-05-13 11:55:02] (step=0037700) Train Loss: 0.1037, Train Steps/Sec: 1.79, Samples/Sec: 85.82
+[2025-05-13 11:55:57] (step=0037800) Train Loss: 0.0999, Train Steps/Sec: 1.80, Samples/Sec: 86.23
+[2025-05-13 11:56:53] (step=0037900) Train Loss: 0.1054, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 11:57:48] (step=0038000) Train Loss: 0.0967, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 11:58:01] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 11:58:57] (step=0038100) Train Loss: 0.1052, Train Steps/Sec: 1.46, Samples/Sec: 70.15
+[2025-05-13 11:59:52] (step=0038200) Train Loss: 0.0989, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 12:00:48] (step=0038300) Train Loss: 0.1045, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 12:01:44] (step=0038400) Train Loss: 0.1022, Train Steps/Sec: 1.80, Samples/Sec: 86.23
+[2025-05-13 12:02:39] (step=0038500) Train Loss: 0.1022, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 12:03:35] (step=0038600) Train Loss: 0.1037, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 12:04:31] (step=0038700) Train Loss: 0.1012, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 12:05:26] (step=0038800) Train Loss: 0.0987, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 12:06:22] (step=0038900) Train Loss: 0.0991, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:07:17] (step=0039000) Train Loss: 0.1068, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 12:08:13] (step=0039100) Train Loss: 0.0988, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 12:08:58] Beginning epoch 26...
+[2025-05-13 12:09:09] (step=0039200) Train Loss: 0.0998, Train Steps/Sec: 1.79, Samples/Sec: 85.70
+[2025-05-13 12:10:05] (step=0039300) Train Loss: 0.1018, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 12:11:00] (step=0039400) Train Loss: 0.1005, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 12:11:56] (step=0039500) Train Loss: 0.0997, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 12:12:51] (step=0039600) Train Loss: 0.0997, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:13:47] (step=0039700) Train Loss: 0.0998, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:14:43] (step=0039800) Train Loss: 0.1037, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 12:15:38] (step=0039900) Train Loss: 0.0992, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 12:16:34] (step=0040000) Train Loss: 0.1055, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 12:16:58] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/0040000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 12:17:01] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 12:19:21] (step=0040000) Perceptual Loss: 0.3476, Eval Time: 142.70
+[2025-05-13 12:20:17] (step=0040100) Train Loss: 0.1034, Train Steps/Sec: 0.45, Samples/Sec: 21.52
+[2025-05-13 12:21:12] (step=0040200) Train Loss: 0.0979, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 12:22:08] (step=0040300) Train Loss: 0.1018, Train Steps/Sec: 1.80, Samples/Sec: 86.23
+[2025-05-13 12:23:04] (step=0040400) Train Loss: 0.1012, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 12:23:59] (step=0040500) Train Loss: 0.1008, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 12:24:55] (step=0040600) Train Loss: 0.0994, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 12:25:44] Beginning epoch 27...
+[2025-05-13 12:25:51] (step=0040700) Train Loss: 0.0973, Train Steps/Sec: 1.79, Samples/Sec: 85.77
+[2025-05-13 12:26:46] (step=0040800) Train Loss: 0.0975, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 12:27:42] (step=0040900) Train Loss: 0.0955, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 12:28:38] (step=0041000) Train Loss: 0.1034, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 12:29:33] (step=0041100) Train Loss: 0.0993, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 12:30:29] (step=0041200) Train Loss: 0.1021, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:31:24] (step=0041300) Train Loss: 0.0986, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 12:32:20] (step=0041400) Train Loss: 0.0972, Train Steps/Sec: 1.80, Samples/Sec: 86.23
+[2025-05-13 12:33:16] (step=0041500) Train Loss: 0.1008, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 12:34:11] (step=0041600) Train Loss: 0.0997, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 12:35:07] (step=0041700) Train Loss: 0.1025, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 12:36:02] (step=0041800) Train Loss: 0.0963, Train Steps/Sec: 1.80, Samples/Sec: 86.42
+[2025-05-13 12:36:58] (step=0041900) Train Loss: 0.0993, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 12:37:54] (step=0042000) Train Loss: 0.0964, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 12:38:07] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 12:39:02] (step=0042100) Train Loss: 0.0985, Train Steps/Sec: 1.46, Samples/Sec: 69.96
+[2025-05-13 12:39:56] Beginning epoch 28...
+[2025-05-13 12:39:58] (step=0042200) Train Loss: 0.0966, Train Steps/Sec: 1.78, Samples/Sec: 85.66
+[2025-05-13 12:40:54] (step=0042300) Train Loss: 0.1010, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 12:41:49] (step=0042400) Train Loss: 0.0961, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:42:45] (step=0042500) Train Loss: 0.1068, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 12:43:41] (step=0042600) Train Loss: 0.0983, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 12:44:36] (step=0042700) Train Loss: 0.1016, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 12:45:32] (step=0042800) Train Loss: 0.0957, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 12:46:28] (step=0042900) Train Loss: 0.0952, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 12:47:23] (step=0043000) Train Loss: 0.0914, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:48:19] (step=0043100) Train Loss: 0.1020, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:49:14] (step=0043200) Train Loss: 0.0972, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:50:10] (step=0043300) Train Loss: 0.0966, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 12:51:05] (step=0043400) Train Loss: 0.0965, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 12:52:01] (step=0043500) Train Loss: 0.0967, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:52:57] (step=0043600) Train Loss: 0.0983, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 12:53:52] (step=0043700) Train Loss: 0.0991, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 12:53:54] Beginning epoch 29...
+[2025-05-13 12:54:48] (step=0043800) Train Loss: 0.0966, Train Steps/Sec: 1.79, Samples/Sec: 85.83
+[2025-05-13 12:55:44] (step=0043900) Train Loss: 0.0941, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 12:56:39] (step=0044000) Train Loss: 0.1009, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 12:56:52] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 12:57:48] (step=0044100) Train Loss: 0.0997, Train Steps/Sec: 1.47, Samples/Sec: 70.51
+[2025-05-13 12:58:43] (step=0044200) Train Loss: 0.0958, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 12:59:39] (step=0044300) Train Loss: 0.0972, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:00:34] (step=0044400) Train Loss: 0.0993, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 13:01:30] (step=0044500) Train Loss: 0.0979, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 13:02:26] (step=0044600) Train Loss: 0.0924, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 13:03:21] (step=0044700) Train Loss: 0.0957, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 13:04:17] (step=0044800) Train Loss: 0.1031, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 13:05:12] (step=0044900) Train Loss: 0.1011, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 13:06:08] (step=0045000) Train Loss: 0.0971, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:07:04] (step=0045100) Train Loss: 0.0987, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 13:07:59] (step=0045200) Train Loss: 0.1011, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:08:05] Beginning epoch 30...
+[2025-05-13 13:08:55] (step=0045300) Train Loss: 0.0978, Train Steps/Sec: 1.79, Samples/Sec: 85.68
+[2025-05-13 13:09:51] (step=0045400) Train Loss: 0.0975, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 13:10:46] (step=0045500) Train Loss: 0.0987, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:11:42] (step=0045600) Train Loss: 0.0926, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 13:12:38] (step=0045700) Train Loss: 0.0912, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 13:13:33] (step=0045800) Train Loss: 0.0937, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 13:14:29] (step=0045900) Train Loss: 0.0949, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:15:24] (step=0046000) Train Loss: 0.0997, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 13:15:37] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 13:16:32] (step=0046100) Train Loss: 0.0977, Train Steps/Sec: 1.47, Samples/Sec: 70.51
+[2025-05-13 13:17:28] (step=0046200) Train Loss: 0.0947, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:18:24] (step=0046300) Train Loss: 0.0964, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 13:19:19] (step=0046400) Train Loss: 0.0989, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:20:15] (step=0046500) Train Loss: 0.0975, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 13:21:10] (step=0046600) Train Loss: 0.0983, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 13:22:06] (step=0046700) Train Loss: 0.0926, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 13:22:15] Beginning epoch 31...
+[2025-05-13 13:23:02] (step=0046800) Train Loss: 0.0951, Train Steps/Sec: 1.78, Samples/Sec: 85.45
+[2025-05-13 13:23:58] (step=0046900) Train Loss: 0.0990, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 13:24:53] (step=0047000) Train Loss: 0.0968, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 13:25:49] (step=0047100) Train Loss: 0.0926, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:26:45] (step=0047200) Train Loss: 0.0962, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 13:27:40] (step=0047300) Train Loss: 0.1001, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:28:36] (step=0047400) Train Loss: 0.0962, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 13:29:31] (step=0047500) Train Loss: 0.0917, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 13:30:27] (step=0047600) Train Loss: 0.0944, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 13:31:23] (step=0047700) Train Loss: 0.0980, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:32:18] (step=0047800) Train Loss: 0.0997, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:33:14] (step=0047900) Train Loss: 0.0926, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:34:09] (step=0048000) Train Loss: 0.0904, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 13:34:22] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 13:35:18] (step=0048100) Train Loss: 0.0954, Train Steps/Sec: 1.46, Samples/Sec: 70.22
+[2025-05-13 13:36:13] (step=0048200) Train Loss: 0.0972, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 13:36:27] Beginning epoch 32...
+[2025-05-13 13:37:09] (step=0048300) Train Loss: 0.0932, Train Steps/Sec: 1.79, Samples/Sec: 85.86
+[2025-05-13 13:38:05] (step=0048400) Train Loss: 0.0993, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 13:39:00] (step=0048500) Train Loss: 0.0947, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 13:39:56] (step=0048600) Train Loss: 0.0945, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:40:52] (step=0048700) Train Loss: 0.0982, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 13:41:47] (step=0048800) Train Loss: 0.0990, Train Steps/Sec: 1.80, Samples/Sec: 86.20
+[2025-05-13 13:42:43] (step=0048900) Train Loss: 0.1002, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:43:38] (step=0049000) Train Loss: 0.0962, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:44:34] (step=0049100) Train Loss: 0.0952, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:45:29] (step=0049200) Train Loss: 0.0940, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 13:46:25] (step=0049300) Train Loss: 0.0958, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:47:21] (step=0049400) Train Loss: 0.0932, Train Steps/Sec: 1.80, Samples/Sec: 86.17
+[2025-05-13 13:48:16] (step=0049500) Train Loss: 0.0990, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 13:49:12] (step=0049600) Train Loss: 0.0939, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 13:50:08] (step=0049700) Train Loss: 0.0914, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:50:25] Beginning epoch 33...
+[2025-05-13 13:51:03] (step=0049800) Train Loss: 0.0895, Train Steps/Sec: 1.79, Samples/Sec: 85.84
+[2025-05-13 13:51:59] (step=0049900) Train Loss: 0.0949, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 13:52:55] (step=0050000) Train Loss: 0.0963, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 13:53:08] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 13:53:10] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 13:55:30] (step=0050000) Perceptual Loss: 0.3673, Eval Time: 142.75
+[2025-05-13 13:56:26] (step=0050100) Train Loss: 0.0948, Train Steps/Sec: 0.47, Samples/Sec: 22.68
+[2025-05-13 13:57:22] (step=0050200) Train Loss: 0.0914, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 13:58:17] (step=0050300) Train Loss: 0.0940, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 13:59:13] (step=0050400) Train Loss: 0.0951, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 14:00:09] (step=0050500) Train Loss: 0.0974, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 14:01:04] (step=0050600) Train Loss: 0.0955, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 14:02:00] (step=0050700) Train Loss: 0.0950, Train Steps/Sec: 1.79, Samples/Sec: 85.93
+[2025-05-13 14:02:56] (step=0050800) Train Loss: 0.0961, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 14:03:51] (step=0050900) Train Loss: 0.0936, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 14:04:47] (step=0051000) Train Loss: 0.0936, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 14:05:42] (step=0051100) Train Loss: 0.0935, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 14:06:38] (step=0051200) Train Loss: 0.0957, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 14:06:59] Beginning epoch 34...
+[2025-05-13 14:07:34] (step=0051300) Train Loss: 0.0922, Train Steps/Sec: 1.78, Samples/Sec: 85.62
+[2025-05-13 14:08:30] (step=0051400) Train Loss: 0.0948, Train Steps/Sec: 1.79, Samples/Sec: 85.95
+[2025-05-13 14:09:26] (step=0051500) Train Loss: 0.0982, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 14:10:21] (step=0051600) Train Loss: 0.0945, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 14:11:17] (step=0051700) Train Loss: 0.0937, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 14:12:12] (step=0051800) Train Loss: 0.0957, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 14:13:08] (step=0051900) Train Loss: 0.0956, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 14:14:04] (step=0052000) Train Loss: 0.0956, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 14:14:16] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 14:15:12] (step=0052100) Train Loss: 0.0955, Train Steps/Sec: 1.47, Samples/Sec: 70.36
+[2025-05-13 14:16:07] (step=0052200) Train Loss: 0.0960, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 14:17:03] (step=0052300) Train Loss: 0.0906, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 14:17:59] (step=0052400) Train Loss: 0.0951, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 14:18:54] (step=0052500) Train Loss: 0.0934, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 14:19:50] (step=0052600) Train Loss: 0.0945, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 14:20:45] (step=0052700) Train Loss: 0.0955, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 14:21:11] Beginning epoch 35...
+[2025-05-13 14:21:42] (step=0052800) Train Loss: 0.0996, Train Steps/Sec: 1.78, Samples/Sec: 85.56
+[2025-05-13 14:22:37] (step=0052900) Train Loss: 0.0929, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 14:23:33] (step=0053000) Train Loss: 0.0942, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 14:24:28] (step=0053100) Train Loss: 0.0902, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 14:25:24] (step=0053200) Train Loss: 0.0962, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 14:26:20] (step=0053300) Train Loss: 0.0947, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 14:27:15] (step=0053400) Train Loss: 0.0914, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 14:28:11] (step=0053500) Train Loss: 0.0981, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 14:29:06] (step=0053600) Train Loss: 0.0958, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 14:30:02] (step=0053700) Train Loss: 0.0900, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 14:30:58] (step=0053800) Train Loss: 0.0964, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 14:31:53] (step=0053900) Train Loss: 0.0848, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 14:32:49] (step=0054000) Train Loss: 0.0916, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 14:33:01] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 14:33:57] (step=0054100) Train Loss: 0.0902, Train Steps/Sec: 1.46, Samples/Sec: 70.31
+[2025-05-13 14:34:53] (step=0054200) Train Loss: 0.0924, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 14:35:21] Beginning epoch 36...
+[2025-05-13 14:35:49] (step=0054300) Train Loss: 0.0970, Train Steps/Sec: 1.79, Samples/Sec: 85.81
+[2025-05-13 14:36:44] (step=0054400) Train Loss: 0.0914, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 14:37:40] (step=0054500) Train Loss: 0.0886, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 14:38:35] (step=0054600) Train Loss: 0.0944, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 14:39:31] (step=0054700) Train Loss: 0.0951, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 14:40:27] (step=0054800) Train Loss: 0.0944, Train Steps/Sec: 1.79, Samples/Sec: 86.16
+[2025-05-13 14:41:22] (step=0054900) Train Loss: 0.0901, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 14:42:18] (step=0055000) Train Loss: 0.0905, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 14:43:13] (step=0055100) Train Loss: 0.0898, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 14:44:09] (step=0055200) Train Loss: 0.0910, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 14:45:04] (step=0055300) Train Loss: 0.0929, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 14:46:00] (step=0055400) Train Loss: 0.0911, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 14:46:56] (step=0055500) Train Loss: 0.0923, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 14:47:51] (step=0055600) Train Loss: 0.0909, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 14:48:47] (step=0055700) Train Loss: 0.0906, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 14:49:20] Beginning epoch 37...
+[2025-05-13 14:49:43] (step=0055800) Train Loss: 0.0910, Train Steps/Sec: 1.79, Samples/Sec: 85.84
+[2025-05-13 14:50:38] (step=0055900) Train Loss: 0.0895, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 14:51:34] (step=0056000) Train Loss: 0.0896, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 14:51:46] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 14:52:42] (step=0056100) Train Loss: 0.0925, Train Steps/Sec: 1.47, Samples/Sec: 70.47
+[2025-05-13 14:53:38] (step=0056200) Train Loss: 0.0918, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 14:54:33] (step=0056300) Train Loss: 0.0875, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 14:55:29] (step=0056400) Train Loss: 0.0905, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 14:56:24] (step=0056500) Train Loss: 0.0927, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 14:57:20] (step=0056600) Train Loss: 0.0926, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 14:58:16] (step=0056700) Train Loss: 0.0930, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 14:59:11] (step=0056800) Train Loss: 0.0914, Train Steps/Sec: 1.80, Samples/Sec: 86.20
+[2025-05-13 15:00:07] (step=0056900) Train Loss: 0.0915, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:01:02] (step=0057000) Train Loss: 0.0896, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:01:58] (step=0057100) Train Loss: 0.0898, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:02:54] (step=0057200) Train Loss: 0.0874, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 15:03:30] Beginning epoch 38...
+[2025-05-13 15:03:49] (step=0057300) Train Loss: 0.0919, Train Steps/Sec: 1.79, Samples/Sec: 85.90
+[2025-05-13 15:04:45] (step=0057400) Train Loss: 0.0913, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 15:05:41] (step=0057500) Train Loss: 0.0909, Train Steps/Sec: 1.80, Samples/Sec: 86.18
+[2025-05-13 15:06:36] (step=0057600) Train Loss: 0.0891, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:07:32] (step=0057700) Train Loss: 0.0918, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 15:08:27] (step=0057800) Train Loss: 0.0946, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 15:09:23] (step=0057900) Train Loss: 0.0898, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 15:10:19] (step=0058000) Train Loss: 0.0880, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:10:31] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 15:11:27] (step=0058100) Train Loss: 0.0918, Train Steps/Sec: 1.46, Samples/Sec: 70.24
+[2025-05-13 15:12:23] (step=0058200) Train Loss: 0.0905, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 15:13:18] (step=0058300) Train Loss: 0.0897, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 15:14:14] (step=0058400) Train Loss: 0.0878, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:15:09] (step=0058500) Train Loss: 0.0929, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 15:16:05] (step=0058600) Train Loss: 0.0901, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 15:17:00] (step=0058700) Train Loss: 0.0902, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 15:17:41] Beginning epoch 39...
+[2025-05-13 15:17:57] (step=0058800) Train Loss: 0.0897, Train Steps/Sec: 1.78, Samples/Sec: 85.66
+[2025-05-13 15:18:52] (step=0058900) Train Loss: 0.0890, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:19:48] (step=0059000) Train Loss: 0.0894, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 15:20:43] (step=0059100) Train Loss: 0.0913, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:21:39] (step=0059200) Train Loss: 0.0914, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 15:22:34] (step=0059300) Train Loss: 0.0873, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 15:23:30] (step=0059400) Train Loss: 0.0890, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:24:26] (step=0059500) Train Loss: 0.0918, Train Steps/Sec: 1.80, Samples/Sec: 86.18
+[2025-05-13 15:25:21] (step=0059600) Train Loss: 0.0892, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 15:26:17] (step=0059700) Train Loss: 0.0914, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 15:27:13] (step=0059800) Train Loss: 0.0925, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 15:28:08] (step=0059900) Train Loss: 0.0894, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:29:04] (step=0060000) Train Loss: 0.0913, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 15:29:28] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/0060000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 15:29:31] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 15:31:51] (step=0060000) Perceptual Loss: 0.3477, Eval Time: 142.94
+[2025-05-13 15:32:47] (step=0060100) Train Loss: 0.0922, Train Steps/Sec: 0.45, Samples/Sec: 21.49
+[2025-05-13 15:33:43] (step=0060200) Train Loss: 0.0861, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 15:34:27] Beginning epoch 40...
+[2025-05-13 15:34:39] (step=0060300) Train Loss: 0.0928, Train Steps/Sec: 1.78, Samples/Sec: 85.68
+[2025-05-13 15:35:34] (step=0060400) Train Loss: 0.0880, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:36:30] (step=0060500) Train Loss: 0.0959, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:37:25] (step=0060600) Train Loss: 0.0859, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 15:38:21] (step=0060700) Train Loss: 0.0900, Train Steps/Sec: 1.79, Samples/Sec: 86.16
+[2025-05-13 15:39:17] (step=0060800) Train Loss: 0.0935, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 15:40:12] (step=0060900) Train Loss: 0.0833, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:41:08] (step=0061000) Train Loss: 0.0853, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 15:42:03] (step=0061100) Train Loss: 0.0862, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:42:59] (step=0061200) Train Loss: 0.0942, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:43:54] (step=0061300) Train Loss: 0.0902, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 15:44:50] (step=0061400) Train Loss: 0.0899, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 15:45:46] (step=0061500) Train Loss: 0.0893, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:46:41] (step=0061600) Train Loss: 0.0907, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 15:47:37] (step=0061700) Train Loss: 0.0873, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 15:48:25] Beginning epoch 41...
+[2025-05-13 15:48:33] (step=0061800) Train Loss: 0.0878, Train Steps/Sec: 1.78, Samples/Sec: 85.66
+[2025-05-13 15:49:29] (step=0061900) Train Loss: 0.0876, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 15:50:24] (step=0062000) Train Loss: 0.0882, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 15:50:37] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 15:51:33] (step=0062100) Train Loss: 0.0946, Train Steps/Sec: 1.45, Samples/Sec: 69.71
+[2025-05-13 15:52:29] (step=0062200) Train Loss: 0.0875, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:53:24] (step=0062300) Train Loss: 0.0837, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 15:54:20] (step=0062400) Train Loss: 0.0891, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:55:15] (step=0062500) Train Loss: 0.0873, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:56:11] (step=0062600) Train Loss: 0.0874, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:57:06] (step=0062700) Train Loss: 0.0870, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 15:58:02] (step=0062800) Train Loss: 0.0894, Train Steps/Sec: 1.80, Samples/Sec: 86.18
+[2025-05-13 15:58:58] (step=0062900) Train Loss: 0.0866, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 15:59:53] (step=0063000) Train Loss: 0.0881, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 16:00:49] (step=0063100) Train Loss: 0.0895, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 16:01:44] (step=0063200) Train Loss: 0.0917, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 16:02:37] Beginning epoch 42...
+[2025-05-13 16:02:40] (step=0063300) Train Loss: 0.0923, Train Steps/Sec: 1.79, Samples/Sec: 85.81
+[2025-05-13 16:03:36] (step=0063400) Train Loss: 0.0905, Train Steps/Sec: 1.80, Samples/Sec: 86.17
+[2025-05-13 16:04:32] (step=0063500) Train Loss: 0.0880, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 16:05:27] (step=0063600) Train Loss: 0.0915, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 16:06:23] (step=0063700) Train Loss: 0.0885, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 16:07:18] (step=0063800) Train Loss: 0.0900, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 16:08:14] (step=0063900) Train Loss: 0.0934, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 16:09:10] (step=0064000) Train Loss: 0.0898, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 16:09:22] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 16:10:18] (step=0064100) Train Loss: 0.0887, Train Steps/Sec: 1.47, Samples/Sec: 70.33
+[2025-05-13 16:11:13] (step=0064200) Train Loss: 0.0862, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 16:12:09] (step=0064300) Train Loss: 0.0824, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 16:13:05] (step=0064400) Train Loss: 0.0887, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 16:14:00] (step=0064500) Train Loss: 0.0873, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 16:14:56] (step=0064600) Train Loss: 0.0831, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 16:15:52] (step=0064700) Train Loss: 0.0867, Train Steps/Sec: 1.80, Samples/Sec: 86.19
+[2025-05-13 16:16:47] (step=0064800) Train Loss: 0.0823, Train Steps/Sec: 1.79, Samples/Sec: 85.99
+[2025-05-13 16:16:48] Beginning epoch 43...
+[2025-05-13 16:17:44] (step=0064900) Train Loss: 0.0929, Train Steps/Sec: 1.78, Samples/Sec: 85.35
+[2025-05-13 16:18:39] (step=0065000) Train Loss: 0.0834, Train Steps/Sec: 1.79, Samples/Sec: 86.06
+[2025-05-13 16:19:35] (step=0065100) Train Loss: 0.0840, Train Steps/Sec: 1.80, Samples/Sec: 86.23
+[2025-05-13 16:20:31] (step=0065200) Train Loss: 0.0874, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 16:21:26] (step=0065300) Train Loss: 0.0847, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 16:22:22] (step=0065400) Train Loss: 0.0873, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 16:23:18] (step=0065500) Train Loss: 0.0851, Train Steps/Sec: 1.79, Samples/Sec: 86.08
+[2025-05-13 16:24:13] (step=0065600) Train Loss: 0.0835, Train Steps/Sec: 1.80, Samples/Sec: 86.22
+[2025-05-13 16:25:09] (step=0065700) Train Loss: 0.0888, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 16:26:05] (step=0065800) Train Loss: 0.0851, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 16:27:00] (step=0065900) Train Loss: 0.0852, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 16:27:56] (step=0066000) Train Loss: 0.0904, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 16:28:09] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 16:29:05] (step=0066100) Train Loss: 0.0806, Train Steps/Sec: 1.45, Samples/Sec: 69.75
+[2025-05-13 16:30:01] (step=0066200) Train Loss: 0.0913, Train Steps/Sec: 1.79, Samples/Sec: 86.13
+[2025-05-13 16:30:56] (step=0066300) Train Loss: 0.0870, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 16:31:01] Beginning epoch 44...
+[2025-05-13 16:31:52] (step=0066400) Train Loss: 0.0786, Train Steps/Sec: 1.78, Samples/Sec: 85.63
+[2025-05-13 16:32:48] (step=0066500) Train Loss: 0.0868, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 16:33:44] (step=0066600) Train Loss: 0.0885, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 16:34:39] (step=0066700) Train Loss: 0.0889, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 16:35:35] (step=0066800) Train Loss: 0.0915, Train Steps/Sec: 1.79, Samples/Sec: 86.06
+[2025-05-13 16:36:31] (step=0066900) Train Loss: 0.0880, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 16:37:26] (step=0067000) Train Loss: 0.0877, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 16:38:22] (step=0067100) Train Loss: 0.0871, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 16:39:18] (step=0067200) Train Loss: 0.0822, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 16:40:13] (step=0067300) Train Loss: 0.0844, Train Steps/Sec: 1.80, Samples/Sec: 86.22
+[2025-05-13 16:41:09] (step=0067400) Train Loss: 0.0851, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 16:42:05] (step=0067500) Train Loss: 0.0868, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 16:43:00] (step=0067600) Train Loss: 0.0812, Train Steps/Sec: 1.80, Samples/Sec: 86.17
+[2025-05-13 16:43:56] (step=0067700) Train Loss: 0.0897, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 16:44:52] (step=0067800) Train Loss: 0.0797, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 16:45:00] Beginning epoch 45...
+[2025-05-13 16:45:47] (step=0067900) Train Loss: 0.0877, Train Steps/Sec: 1.79, Samples/Sec: 85.84
+[2025-05-13 16:46:43] (step=0068000) Train Loss: 0.0834, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 16:46:55] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 16:47:51] (step=0068100) Train Loss: 0.0806, Train Steps/Sec: 1.47, Samples/Sec: 70.62
+[2025-05-13 16:48:47] (step=0068200) Train Loss: 0.0828, Train Steps/Sec: 1.79, Samples/Sec: 86.01
+[2025-05-13 16:49:42] (step=0068300) Train Loss: 0.0825, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 16:50:38] (step=0068400) Train Loss: 0.0856, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 16:51:34] (step=0068500) Train Loss: 0.0831, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 16:52:29] (step=0068600) Train Loss: 0.0813, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 16:53:25] (step=0068700) Train Loss: 0.0822, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 16:54:21] (step=0068800) Train Loss: 0.0863, Train Steps/Sec: 1.79, Samples/Sec: 86.01
+[2025-05-13 16:55:16] (step=0068900) Train Loss: 0.0816, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 16:56:12] (step=0069000) Train Loss: 0.0862, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 16:57:07] (step=0069100) Train Loss: 0.0836, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 16:58:03] (step=0069200) Train Loss: 0.0880, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 16:58:59] (step=0069300) Train Loss: 0.0836, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 16:59:11] Beginning epoch 46...
+[2025-05-13 16:59:55] (step=0069400) Train Loss: 0.0857, Train Steps/Sec: 1.78, Samples/Sec: 85.61
+[2025-05-13 17:00:51] (step=0069500) Train Loss: 0.0877, Train Steps/Sec: 1.79, Samples/Sec: 85.94
+[2025-05-13 17:01:46] (step=0069600) Train Loss: 0.0876, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 17:02:42] (step=0069700) Train Loss: 0.0860, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 17:03:37] (step=0069800) Train Loss: 0.0897, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 17:04:33] (step=0069900) Train Loss: 0.0831, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 17:05:29] (step=0070000) Train Loss: 0.0822, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 17:05:41] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 17:05:44] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 17:08:04] (step=0070000) Perceptual Loss: 0.3544, Eval Time: 143.16
+[2025-05-13 17:09:00] (step=0070100) Train Loss: 0.0858, Train Steps/Sec: 0.47, Samples/Sec: 22.65
+[2025-05-13 17:09:56] (step=0070200) Train Loss: 0.0845, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 17:10:52] (step=0070300) Train Loss: 0.0816, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 17:11:47] (step=0070400) Train Loss: 0.0896, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 17:12:43] (step=0070500) Train Loss: 0.0854, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 17:13:38] (step=0070600) Train Loss: 0.0856, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 17:14:34] (step=0070700) Train Loss: 0.0867, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 17:15:30] (step=0070800) Train Loss: 0.0854, Train Steps/Sec: 1.80, Samples/Sec: 86.16
+[2025-05-13 17:15:46] Beginning epoch 47...
+[2025-05-13 17:16:26] (step=0070900) Train Loss: 0.0868, Train Steps/Sec: 1.79, Samples/Sec: 85.71
+[2025-05-13 17:17:21] (step=0071000) Train Loss: 0.0792, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 17:18:17] (step=0071100) Train Loss: 0.0851, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 17:19:13] (step=0071200) Train Loss: 0.0837, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 17:20:08] (step=0071300) Train Loss: 0.0874, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 17:21:04] (step=0071400) Train Loss: 0.0817, Train Steps/Sec: 1.79, Samples/Sec: 86.07
+[2025-05-13 17:22:00] (step=0071500) Train Loss: 0.0863, Train Steps/Sec: 1.80, Samples/Sec: 86.17
+[2025-05-13 17:22:55] (step=0071600) Train Loss: 0.0817, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 17:23:51] (step=0071700) Train Loss: 0.0818, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 17:24:46] (step=0071800) Train Loss: 0.0844, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 17:25:42] (step=0071900) Train Loss: 0.0868, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 17:26:38] (step=0072000) Train Loss: 0.0856, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 17:26:51] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 17:27:47] (step=0072100) Train Loss: 0.0844, Train Steps/Sec: 1.45, Samples/Sec: 69.53
+[2025-05-13 17:28:42] (step=0072200) Train Loss: 0.0832, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 17:29:38] (step=0072300) Train Loss: 0.0824, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 17:29:58] Beginning epoch 48...
+[2025-05-13 17:30:34] (step=0072400) Train Loss: 0.0800, Train Steps/Sec: 1.79, Samples/Sec: 85.76
+[2025-05-13 17:31:29] (step=0072500) Train Loss: 0.0813, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 17:32:25] (step=0072600) Train Loss: 0.0869, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 17:33:20] (step=0072700) Train Loss: 0.0871, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 17:34:16] (step=0072800) Train Loss: 0.0847, Train Steps/Sec: 1.79, Samples/Sec: 86.05
+[2025-05-13 17:35:12] (step=0072900) Train Loss: 0.0799, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 17:36:07] (step=0073000) Train Loss: 0.0889, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 17:37:03] (step=0073100) Train Loss: 0.0824, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 17:37:59] (step=0073200) Train Loss: 0.0837, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 17:38:54] (step=0073300) Train Loss: 0.0800, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 17:39:50] (step=0073400) Train Loss: 0.0774, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 17:40:46] (step=0073500) Train Loss: 0.0843, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 17:41:41] (step=0073600) Train Loss: 0.0823, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 17:42:37] (step=0073700) Train Loss: 0.0808, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 17:43:32] (step=0073800) Train Loss: 0.0860, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 17:43:56] Beginning epoch 49...
+[2025-05-13 17:44:28] (step=0073900) Train Loss: 0.0848, Train Steps/Sec: 1.78, Samples/Sec: 85.65
+[2025-05-13 17:45:24] (step=0074000) Train Loss: 0.0802, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 17:45:37] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 17:46:33] (step=0074100) Train Loss: 0.0796, Train Steps/Sec: 1.45, Samples/Sec: 69.78
+[2025-05-13 17:47:28] (step=0074200) Train Loss: 0.0817, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 17:48:24] (step=0074300) Train Loss: 0.0822, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 17:49:19] (step=0074400) Train Loss: 0.0834, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 17:50:15] (step=0074500) Train Loss: 0.0839, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 17:51:11] (step=0074600) Train Loss: 0.0858, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 17:52:06] (step=0074700) Train Loss: 0.0819, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 17:53:02] (step=0074800) Train Loss: 0.0856, Train Steps/Sec: 1.80, Samples/Sec: 86.17
+[2025-05-13 17:53:58] (step=0074900) Train Loss: 0.0840, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 17:54:53] (step=0075000) Train Loss: 0.0834, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 17:55:49] (step=0075100) Train Loss: 0.0845, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 17:56:44] (step=0075200) Train Loss: 0.0804, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 17:57:40] (step=0075300) Train Loss: 0.0805, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 17:58:08] Beginning epoch 50...
+[2025-05-13 17:58:36] (step=0075400) Train Loss: 0.0830, Train Steps/Sec: 1.78, Samples/Sec: 85.63
+[2025-05-13 17:59:32] (step=0075500) Train Loss: 0.0846, Train Steps/Sec: 1.79, Samples/Sec: 86.11
+[2025-05-13 18:00:27] (step=0075600) Train Loss: 0.0830, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 18:01:23] (step=0075700) Train Loss: 0.0830, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 18:02:18] (step=0075800) Train Loss: 0.0890, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 18:03:14] (step=0075900) Train Loss: 0.0834, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 18:04:10] (step=0076000) Train Loss: 0.0858, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 18:04:23] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 18:05:18] (step=0076100) Train Loss: 0.0800, Train Steps/Sec: 1.46, Samples/Sec: 69.90
+[2025-05-13 18:06:14] (step=0076200) Train Loss: 0.0815, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 18:07:10] (step=0076300) Train Loss: 0.0811, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 18:08:05] (step=0076400) Train Loss: 0.0814, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 18:09:01] (step=0076500) Train Loss: 0.0855, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 18:09:56] (step=0076600) Train Loss: 0.0849, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 18:10:52] (step=0076700) Train Loss: 0.0808, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 18:11:48] (step=0076800) Train Loss: 0.0841, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 18:12:19] Beginning epoch 51...
+[2025-05-13 18:12:44] (step=0076900) Train Loss: 0.0831, Train Steps/Sec: 1.79, Samples/Sec: 85.74
+[2025-05-13 18:13:39] (step=0077000) Train Loss: 0.0860, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 18:14:35] (step=0077100) Train Loss: 0.0847, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 18:15:30] (step=0077200) Train Loss: 0.0836, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 18:16:26] (step=0077300) Train Loss: 0.0813, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 18:17:22] (step=0077400) Train Loss: 0.0823, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 18:18:17] (step=0077500) Train Loss: 0.0821, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 18:19:13] (step=0077600) Train Loss: 0.0866, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 18:20:08] (step=0077700) Train Loss: 0.0804, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 18:21:04] (step=0077800) Train Loss: 0.0797, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 18:22:00] (step=0077900) Train Loss: 0.0817, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 18:22:55] (step=0078000) Train Loss: 0.0792, Train Steps/Sec: 1.80, Samples/Sec: 86.16
+[2025-05-13 18:23:08] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 18:24:04] (step=0078100) Train Loss: 0.0814, Train Steps/Sec: 1.46, Samples/Sec: 69.90
+[2025-05-13 18:25:00] (step=0078200) Train Loss: 0.0787, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 18:25:55] (step=0078300) Train Loss: 0.0826, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 18:26:31] Beginning epoch 52...
+[2025-05-13 18:26:51] (step=0078400) Train Loss: 0.0815, Train Steps/Sec: 1.79, Samples/Sec: 85.76
+[2025-05-13 18:27:47] (step=0078500) Train Loss: 0.0780, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 18:28:43] (step=0078600) Train Loss: 0.0810, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 18:29:38] (step=0078700) Train Loss: 0.0848, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 18:30:34] (step=0078800) Train Loss: 0.0825, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 18:31:30] (step=0078900) Train Loss: 0.0860, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 18:32:25] (step=0079000) Train Loss: 0.0818, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 18:33:21] (step=0079100) Train Loss: 0.0819, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 18:34:17] (step=0079200) Train Loss: 0.0803, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 18:35:12] (step=0079300) Train Loss: 0.0757, Train Steps/Sec: 1.79, Samples/Sec: 86.12
+[2025-05-13 18:36:08] (step=0079400) Train Loss: 0.0823, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 18:37:04] (step=0079500) Train Loss: 0.0812, Train Steps/Sec: 1.79, Samples/Sec: 85.99
+[2025-05-13 18:37:59] (step=0079600) Train Loss: 0.0782, Train Steps/Sec: 1.80, Samples/Sec: 86.20
+[2025-05-13 18:38:55] (step=0079700) Train Loss: 0.0770, Train Steps/Sec: 1.80, Samples/Sec: 86.22
+[2025-05-13 18:39:51] (step=0079800) Train Loss: 0.0803, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 18:40:30] Beginning epoch 53...
+[2025-05-13 18:40:47] (step=0079900) Train Loss: 0.0780, Train Steps/Sec: 1.78, Samples/Sec: 85.58
+[2025-05-13 18:41:42] (step=0080000) Train Loss: 0.0814, Train Steps/Sec: 1.80, Samples/Sec: 86.19
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 18:42:07] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/0080000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 18:42:10] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 18:44:30] (step=0080000) Perceptual Loss: 0.3664, Eval Time: 142.92
+[2025-05-13 18:45:26] (step=0080100) Train Loss: 0.0836, Train Steps/Sec: 0.45, Samples/Sec: 21.46
+[2025-05-13 18:46:22] (step=0080200) Train Loss: 0.0797, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 18:47:17] (step=0080300) Train Loss: 0.0771, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 18:48:13] (step=0080400) Train Loss: 0.0836, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 18:49:09] (step=0080500) Train Loss: 0.0821, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 18:50:04] (step=0080600) Train Loss: 0.0806, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 18:51:00] (step=0080700) Train Loss: 0.0808, Train Steps/Sec: 1.80, Samples/Sec: 86.16
+[2025-05-13 18:51:56] (step=0080800) Train Loss: 0.0807, Train Steps/Sec: 1.80, Samples/Sec: 86.22
+[2025-05-13 18:52:51] (step=0080900) Train Loss: 0.0790, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 18:53:47] (step=0081000) Train Loss: 0.0759, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 18:54:42] (step=0081100) Train Loss: 0.0812, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 18:55:38] (step=0081200) Train Loss: 0.0802, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 18:56:34] (step=0081300) Train Loss: 0.0833, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 18:57:17] Beginning epoch 54...
+[2025-05-13 18:57:30] (step=0081400) Train Loss: 0.0843, Train Steps/Sec: 1.78, Samples/Sec: 85.40
+[2025-05-13 18:58:26] (step=0081500) Train Loss: 0.0808, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-13 18:59:21] (step=0081600) Train Loss: 0.0818, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 19:00:17] (step=0081700) Train Loss: 0.0830, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 19:01:12] (step=0081800) Train Loss: 0.0811, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 19:02:08] (step=0081900) Train Loss: 0.0821, Train Steps/Sec: 1.80, Samples/Sec: 86.19
+[2025-05-13 19:03:04] (step=0082000) Train Loss: 0.0787, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 19:03:17] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 19:04:13] (step=0082100) Train Loss: 0.0765, Train Steps/Sec: 1.45, Samples/Sec: 69.63
+[2025-05-13 19:05:08] (step=0082200) Train Loss: 0.0813, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 19:06:04] (step=0082300) Train Loss: 0.0817, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 19:06:59] (step=0082400) Train Loss: 0.0835, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 19:07:55] (step=0082500) Train Loss: 0.0817, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 19:08:50] (step=0082600) Train Loss: 0.0819, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 19:09:46] (step=0082700) Train Loss: 0.0790, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:10:42] (step=0082800) Train Loss: 0.0809, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-13 19:11:29] Beginning epoch 55...
+[2025-05-13 19:11:38] (step=0082900) Train Loss: 0.0787, Train Steps/Sec: 1.79, Samples/Sec: 85.72
+[2025-05-13 19:12:33] (step=0083000) Train Loss: 0.0819, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 19:13:29] (step=0083100) Train Loss: 0.0784, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-13 19:14:25] (step=0083200) Train Loss: 0.0816, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:15:20] (step=0083300) Train Loss: 0.0773, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 19:16:16] (step=0083400) Train Loss: 0.0814, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 19:17:11] (step=0083500) Train Loss: 0.0778, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 19:18:07] (step=0083600) Train Loss: 0.0803, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:19:03] (step=0083700) Train Loss: 0.0777, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:19:58] (step=0083800) Train Loss: 0.0851, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 19:20:54] (step=0083900) Train Loss: 0.0808, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:21:49] (step=0084000) Train Loss: 0.0776, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 19:22:02] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 19:22:58] (step=0084100) Train Loss: 0.0822, Train Steps/Sec: 1.47, Samples/Sec: 70.36
+[2025-05-13 19:23:53] (step=0084200) Train Loss: 0.0786, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:24:49] (step=0084300) Train Loss: 0.0811, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:25:40] Beginning epoch 56...
+[2025-05-13 19:25:45] (step=0084400) Train Loss: 0.0834, Train Steps/Sec: 1.78, Samples/Sec: 85.59
+[2025-05-13 19:26:40] (step=0084500) Train Loss: 0.0789, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 19:27:36] (step=0084600) Train Loss: 0.0824, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:28:31] (step=0084700) Train Loss: 0.0795, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 19:29:27] (step=0084800) Train Loss: 0.0819, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 19:30:23] (step=0084900) Train Loss: 0.0795, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 19:31:18] (step=0085000) Train Loss: 0.0824, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 19:32:14] (step=0085100) Train Loss: 0.0831, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 19:33:10] (step=0085200) Train Loss: 0.0801, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 19:34:05] (step=0085300) Train Loss: 0.0803, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 19:35:01] (step=0085400) Train Loss: 0.0826, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 19:35:56] (step=0085500) Train Loss: 0.0798, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 19:36:52] (step=0085600) Train Loss: 0.0829, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 19:37:48] (step=0085700) Train Loss: 0.0784, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:38:43] (step=0085800) Train Loss: 0.0805, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-13 19:39:38] Beginning epoch 57...
+[2025-05-13 19:39:39] (step=0085900) Train Loss: 0.0818, Train Steps/Sec: 1.79, Samples/Sec: 85.76
+[2025-05-13 19:40:35] (step=0086000) Train Loss: 0.0836, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 19:40:47] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 19:41:43] (step=0086100) Train Loss: 0.0828, Train Steps/Sec: 1.47, Samples/Sec: 70.34
+[2025-05-13 19:42:39] (step=0086200) Train Loss: 0.0835, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 19:43:34] (step=0086300) Train Loss: 0.0812, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 19:44:30] (step=0086400) Train Loss: 0.0787, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 19:45:25] (step=0086500) Train Loss: 0.0780, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 19:46:21] (step=0086600) Train Loss: 0.0742, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 19:47:17] (step=0086700) Train Loss: 0.0736, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:48:12] (step=0086800) Train Loss: 0.0756, Train Steps/Sec: 1.79, Samples/Sec: 86.15
+[2025-05-13 19:49:08] (step=0086900) Train Loss: 0.0793, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 19:50:04] (step=0087000) Train Loss: 0.0741, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 19:50:59] (step=0087100) Train Loss: 0.0800, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 19:51:55] (step=0087200) Train Loss: 0.0756, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 19:52:50] (step=0087300) Train Loss: 0.0814, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 19:53:46] (step=0087400) Train Loss: 0.0815, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 19:53:49] Beginning epoch 58...
+[2025-05-13 19:54:42] (step=0087500) Train Loss: 0.0801, Train Steps/Sec: 1.78, Samples/Sec: 85.61
+[2025-05-13 19:55:37] (step=0087600) Train Loss: 0.0816, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 19:56:33] (step=0087700) Train Loss: 0.0803, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 19:57:29] (step=0087800) Train Loss: 0.0781, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 19:58:24] (step=0087900) Train Loss: 0.0794, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 19:59:20] (step=0088000) Train Loss: 0.0823, Train Steps/Sec: 1.80, Samples/Sec: 86.42
+[2025-05-13 19:59:33] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 20:00:29] (step=0088100) Train Loss: 0.0797, Train Steps/Sec: 1.44, Samples/Sec: 69.36
+[2025-05-13 20:01:25] (step=0088200) Train Loss: 0.0775, Train Steps/Sec: 1.80, Samples/Sec: 86.20
+[2025-05-13 20:02:20] (step=0088300) Train Loss: 0.0831, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 20:03:16] (step=0088400) Train Loss: 0.0791, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 20:04:11] (step=0088500) Train Loss: 0.0747, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 20:05:07] (step=0088600) Train Loss: 0.0761, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 20:06:02] (step=0088700) Train Loss: 0.0797, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 20:06:58] (step=0088800) Train Loss: 0.0801, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 20:07:54] (step=0088900) Train Loss: 0.0788, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 20:08:01] Beginning epoch 59...
+[2025-05-13 20:08:50] (step=0089000) Train Loss: 0.0771, Train Steps/Sec: 1.78, Samples/Sec: 85.55
+[2025-05-13 20:09:45] (step=0089100) Train Loss: 0.0769, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 20:10:41] (step=0089200) Train Loss: 0.0770, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 20:11:36] (step=0089300) Train Loss: 0.0745, Train Steps/Sec: 1.80, Samples/Sec: 86.47
+[2025-05-13 20:12:32] (step=0089400) Train Loss: 0.0795, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 20:13:28] (step=0089500) Train Loss: 0.0764, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 20:14:23] (step=0089600) Train Loss: 0.0829, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 20:15:19] (step=0089700) Train Loss: 0.0791, Train Steps/Sec: 1.80, Samples/Sec: 86.42
+[2025-05-13 20:16:14] (step=0089800) Train Loss: 0.0774, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 20:17:10] (step=0089900) Train Loss: 0.0785, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 20:18:05] (step=0090000) Train Loss: 0.0791, Train Steps/Sec: 1.80, Samples/Sec: 86.47
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 20:18:18] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 20:18:20] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 20:20:40] (step=0090000) Perceptual Loss: 0.3569, Eval Time: 142.08
+[2025-05-13 20:21:35] (step=0090100) Train Loss: 0.0777, Train Steps/Sec: 0.48, Samples/Sec: 22.83
+[2025-05-13 20:22:31] (step=0090200) Train Loss: 0.0833, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 20:23:26] (step=0090300) Train Loss: 0.0799, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 20:24:22] (step=0090400) Train Loss: 0.0787, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 20:24:33] Beginning epoch 60...
+[2025-05-13 20:25:18] (step=0090500) Train Loss: 0.0781, Train Steps/Sec: 1.79, Samples/Sec: 85.87
+[2025-05-13 20:26:13] (step=0090600) Train Loss: 0.0801, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 20:27:09] (step=0090700) Train Loss: 0.0800, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 20:28:04] (step=0090800) Train Loss: 0.0781, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 20:29:00] (step=0090900) Train Loss: 0.0776, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 20:29:56] (step=0091000) Train Loss: 0.0756, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 20:30:51] (step=0091100) Train Loss: 0.0799, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 20:31:47] (step=0091200) Train Loss: 0.0828, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 20:32:42] (step=0091300) Train Loss: 0.0777, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 20:33:38] (step=0091400) Train Loss: 0.0781, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 20:34:33] (step=0091500) Train Loss: 0.0776, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 20:35:29] (step=0091600) Train Loss: 0.0771, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 20:36:24] (step=0091700) Train Loss: 0.0785, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 20:37:20] (step=0091800) Train Loss: 0.0744, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 20:38:15] (step=0091900) Train Loss: 0.0800, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 20:38:30] Beginning epoch 61...
+[2025-05-13 20:39:11] (step=0092000) Train Loss: 0.0803, Train Steps/Sec: 1.79, Samples/Sec: 85.91
+[2025-05-13 20:39:24] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 20:40:19] (step=0092100) Train Loss: 0.0762, Train Steps/Sec: 1.46, Samples/Sec: 70.09
+[2025-05-13 20:41:15] (step=0092200) Train Loss: 0.0736, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 20:42:11] (step=0092300) Train Loss: 0.0760, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 20:43:06] (step=0092400) Train Loss: 0.0765, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 20:44:02] (step=0092500) Train Loss: 0.0716, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 20:44:57] (step=0092600) Train Loss: 0.0747, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 20:45:53] (step=0092700) Train Loss: 0.0740, Train Steps/Sec: 1.80, Samples/Sec: 86.45
+[2025-05-13 20:46:48] (step=0092800) Train Loss: 0.0762, Train Steps/Sec: 1.80, Samples/Sec: 86.23
+[2025-05-13 20:47:44] (step=0092900) Train Loss: 0.0763, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 20:48:39] (step=0093000) Train Loss: 0.0757, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 20:49:35] (step=0093100) Train Loss: 0.0817, Train Steps/Sec: 1.80, Samples/Sec: 86.44
+[2025-05-13 20:50:30] (step=0093200) Train Loss: 0.0814, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 20:51:26] (step=0093300) Train Loss: 0.0733, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 20:52:22] (step=0093400) Train Loss: 0.0757, Train Steps/Sec: 1.80, Samples/Sec: 86.19
+[2025-05-13 20:52:41] Beginning epoch 62...
+[2025-05-13 20:53:18] (step=0093500) Train Loss: 0.0821, Train Steps/Sec: 1.79, Samples/Sec: 85.78
+[2025-05-13 20:54:13] (step=0093600) Train Loss: 0.0715, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-13 20:55:09] (step=0093700) Train Loss: 0.0772, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 20:56:04] (step=0093800) Train Loss: 0.0760, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 20:57:00] (step=0093900) Train Loss: 0.0788, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 20:57:55] (step=0094000) Train Loss: 0.0789, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 20:58:08] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 20:59:04] (step=0094100) Train Loss: 0.0759, Train Steps/Sec: 1.46, Samples/Sec: 69.94
+[2025-05-13 21:00:00] (step=0094200) Train Loss: 0.0751, Train Steps/Sec: 1.80, Samples/Sec: 86.24
+[2025-05-13 21:00:55] (step=0094300) Train Loss: 0.0806, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 21:01:51] (step=0094400) Train Loss: 0.0743, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 21:02:47] (step=0094500) Train Loss: 0.0792, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-13 21:03:42] (step=0094600) Train Loss: 0.0774, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 21:04:38] (step=0094700) Train Loss: 0.0754, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-13 21:05:33] (step=0094800) Train Loss: 0.0751, Train Steps/Sec: 1.80, Samples/Sec: 86.16
+[2025-05-13 21:06:29] (step=0094900) Train Loss: 0.0785, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 21:06:52] Beginning epoch 63...
+[2025-05-13 21:07:25] (step=0095000) Train Loss: 0.0740, Train Steps/Sec: 1.79, Samples/Sec: 85.89
+[2025-05-13 21:08:20] (step=0095100) Train Loss: 0.0768, Train Steps/Sec: 1.80, Samples/Sec: 86.47
+[2025-05-13 21:09:16] (step=0095200) Train Loss: 0.0742, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:10:11] (step=0095300) Train Loss: 0.0733, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:11:07] (step=0095400) Train Loss: 0.0738, Train Steps/Sec: 1.80, Samples/Sec: 86.49
+[2025-05-13 21:12:02] (step=0095500) Train Loss: 0.0742, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 21:12:58] (step=0095600) Train Loss: 0.0765, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 21:13:54] (step=0095700) Train Loss: 0.0765, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:14:49] (step=0095800) Train Loss: 0.0696, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 21:15:44] (step=0095900) Train Loss: 0.0792, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:16:40] (step=0096000) Train Loss: 0.0766, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:16:53] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 21:17:48] (step=0096100) Train Loss: 0.0756, Train Steps/Sec: 1.46, Samples/Sec: 70.14
+[2025-05-13 21:18:44] (step=0096200) Train Loss: 0.0745, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-13 21:19:40] (step=0096300) Train Loss: 0.0734, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:20:35] (step=0096400) Train Loss: 0.0746, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 21:21:02] Beginning epoch 64...
+[2025-05-13 21:21:31] (step=0096500) Train Loss: 0.0731, Train Steps/Sec: 1.79, Samples/Sec: 85.91
+[2025-05-13 21:22:26] (step=0096600) Train Loss: 0.0765, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:23:22] (step=0096700) Train Loss: 0.0743, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:24:17] (step=0096800) Train Loss: 0.0754, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-13 21:25:13] (step=0096900) Train Loss: 0.0754, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-13 21:26:08] (step=0097000) Train Loss: 0.0764, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 21:27:04] (step=0097100) Train Loss: 0.0777, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:27:59] (step=0097200) Train Loss: 0.0740, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 21:28:55] (step=0097300) Train Loss: 0.0729, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:29:50] (step=0097400) Train Loss: 0.0747, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 21:30:46] (step=0097500) Train Loss: 0.0731, Train Steps/Sec: 1.80, Samples/Sec: 86.18
+[2025-05-13 21:31:42] (step=0097600) Train Loss: 0.0752, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 21:32:37] (step=0097700) Train Loss: 0.0801, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 21:33:33] (step=0097800) Train Loss: 0.0741, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:34:28] (step=0097900) Train Loss: 0.0748, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 21:34:59] Beginning epoch 65...
+[2025-05-13 21:35:24] (step=0098000) Train Loss: 0.0793, Train Steps/Sec: 1.79, Samples/Sec: 85.92
+[2025-05-13 21:35:37] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 21:36:32] (step=0098100) Train Loss: 0.0752, Train Steps/Sec: 1.46, Samples/Sec: 70.07
+[2025-05-13 21:37:28] (step=0098200) Train Loss: 0.0783, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-13 21:38:24] (step=0098300) Train Loss: 0.0771, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:39:19] (step=0098400) Train Loss: 0.0755, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:40:14] (step=0098500) Train Loss: 0.0778, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:41:10] (step=0098600) Train Loss: 0.0728, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:42:05] (step=0098700) Train Loss: 0.0710, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:43:01] (step=0098800) Train Loss: 0.0695, Train Steps/Sec: 1.80, Samples/Sec: 86.30
+[2025-05-13 21:43:57] (step=0098900) Train Loss: 0.0819, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-13 21:44:52] (step=0099000) Train Loss: 0.0755, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:45:48] (step=0099100) Train Loss: 0.0752, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 21:46:43] (step=0099200) Train Loss: 0.0724, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 21:47:39] (step=0099300) Train Loss: 0.0757, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:48:34] (step=0099400) Train Loss: 0.0735, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:49:09] Beginning epoch 66...
+[2025-05-13 21:49:30] (step=0099500) Train Loss: 0.0761, Train Steps/Sec: 1.78, Samples/Sec: 85.60
+[2025-05-13 21:50:26] (step=0099600) Train Loss: 0.0724, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 21:51:21] (step=0099700) Train Loss: 0.0746, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 21:52:17] (step=0099800) Train Loss: 0.0781, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-13 21:53:12] (step=0099900) Train Loss: 0.0736, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 21:54:08] (step=0100000) Train Loss: 0.0757, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 21:54:32] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/0100000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 21:54:35] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 21:56:54] (step=0100000) Perceptual Loss: 0.3645, Eval Time: 142.37
+[2025-05-13 21:57:50] (step=0100100) Train Loss: 0.0767, Train Steps/Sec: 0.45, Samples/Sec: 21.59
+[2025-05-13 21:58:45] (step=0100200) Train Loss: 0.0762, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 21:59:41] (step=0100300) Train Loss: 0.0739, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 22:00:36] (step=0100400) Train Loss: 0.0692, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 22:01:32] (step=0100500) Train Loss: 0.0781, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 22:02:27] (step=0100600) Train Loss: 0.0742, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 22:03:23] (step=0100700) Train Loss: 0.0696, Train Steps/Sec: 1.80, Samples/Sec: 86.42
+[2025-05-13 22:04:18] (step=0100800) Train Loss: 0.0731, Train Steps/Sec: 1.80, Samples/Sec: 86.31
+[2025-05-13 22:05:14] (step=0100900) Train Loss: 0.0724, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-13 22:05:52] Beginning epoch 67...
+[2025-05-13 22:06:10] (step=0101000) Train Loss: 0.0785, Train Steps/Sec: 1.79, Samples/Sec: 85.89
+[2025-05-13 22:07:05] (step=0101100) Train Loss: 0.0756, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 22:08:01] (step=0101200) Train Loss: 0.0781, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 22:08:56] (step=0101300) Train Loss: 0.0771, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 22:09:52] (step=0101400) Train Loss: 0.0686, Train Steps/Sec: 1.80, Samples/Sec: 86.17
+[2025-05-13 22:10:47] (step=0101500) Train Loss: 0.0776, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 22:11:43] (step=0101600) Train Loss: 0.0778, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 22:12:38] (step=0101700) Train Loss: 0.0759, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 22:13:34] (step=0101800) Train Loss: 0.0743, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 22:14:29] (step=0101900) Train Loss: 0.0691, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 22:15:25] (step=0102000) Train Loss: 0.0764, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 22:15:38] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 22:16:33] (step=0102100) Train Loss: 0.0700, Train Steps/Sec: 1.45, Samples/Sec: 69.77
+[2025-05-13 22:17:29] (step=0102200) Train Loss: 0.0738, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 22:18:24] (step=0102300) Train Loss: 0.0731, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 22:19:20] (step=0102400) Train Loss: 0.0748, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 22:20:02] Beginning epoch 68...
+[2025-05-13 22:20:15] (step=0102500) Train Loss: 0.0743, Train Steps/Sec: 1.79, Samples/Sec: 86.12
+[2025-05-13 22:21:11] (step=0102600) Train Loss: 0.0732, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 22:22:06] (step=0102700) Train Loss: 0.0761, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 22:23:02] (step=0102800) Train Loss: 0.0764, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 22:23:57] (step=0102900) Train Loss: 0.0725, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 22:24:52] (step=0103000) Train Loss: 0.0804, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 22:25:48] (step=0103100) Train Loss: 0.0770, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 22:26:43] (step=0103200) Train Loss: 0.0752, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 22:27:39] (step=0103300) Train Loss: 0.0748, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 22:28:34] (step=0103400) Train Loss: 0.0769, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-13 22:29:30] (step=0103500) Train Loss: 0.0723, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 22:30:25] (step=0103600) Train Loss: 0.0737, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 22:31:20] (step=0103700) Train Loss: 0.0743, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 22:32:16] (step=0103800) Train Loss: 0.0732, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 22:33:11] (step=0103900) Train Loss: 0.0682, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 22:33:57] Beginning epoch 69...
+[2025-05-13 22:34:07] (step=0104000) Train Loss: 0.0793, Train Steps/Sec: 1.79, Samples/Sec: 86.09
+[2025-05-13 22:34:19] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 22:35:15] (step=0104100) Train Loss: 0.0687, Train Steps/Sec: 1.48, Samples/Sec: 70.97
+[2025-05-13 22:36:10] (step=0104200) Train Loss: 0.0759, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 22:37:05] (step=0104300) Train Loss: 0.0775, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 22:38:01] (step=0104400) Train Loss: 0.0736, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 22:38:56] (step=0104500) Train Loss: 0.0761, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 22:39:52] (step=0104600) Train Loss: 0.0701, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 22:40:47] (step=0104700) Train Loss: 0.0753, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 22:41:43] (step=0104800) Train Loss: 0.0747, Train Steps/Sec: 1.80, Samples/Sec: 86.43
+[2025-05-13 22:42:38] (step=0104900) Train Loss: 0.0766, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 22:43:34] (step=0105000) Train Loss: 0.0745, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 22:44:29] (step=0105100) Train Loss: 0.0748, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 22:45:24] (step=0105200) Train Loss: 0.0741, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 22:46:20] (step=0105300) Train Loss: 0.0747, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 22:47:15] (step=0105400) Train Loss: 0.0731, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-13 22:48:05] Beginning epoch 70...
+[2025-05-13 22:48:11] (step=0105500) Train Loss: 0.0744, Train Steps/Sec: 1.79, Samples/Sec: 85.84
+[2025-05-13 22:49:07] (step=0105600) Train Loss: 0.0749, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 22:50:02] (step=0105700) Train Loss: 0.0728, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 22:50:58] (step=0105800) Train Loss: 0.0741, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 22:51:53] (step=0105900) Train Loss: 0.0680, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 22:52:48] (step=0106000) Train Loss: 0.0736, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-13 22:53:01] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 22:53:56] (step=0106100) Train Loss: 0.0725, Train Steps/Sec: 1.47, Samples/Sec: 70.48
+[2025-05-13 22:54:52] (step=0106200) Train Loss: 0.0723, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 22:55:47] (step=0106300) Train Loss: 0.0728, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 22:56:43] (step=0106400) Train Loss: 0.0689, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 22:57:38] (step=0106500) Train Loss: 0.0729, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 22:58:34] (step=0106600) Train Loss: 0.0730, Train Steps/Sec: 1.81, Samples/Sec: 86.68
+[2025-05-13 22:59:29] (step=0106700) Train Loss: 0.0702, Train Steps/Sec: 1.81, Samples/Sec: 86.67
+[2025-05-13 23:00:24] (step=0106800) Train Loss: 0.0734, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 23:01:20] (step=0106900) Train Loss: 0.0798, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 23:02:14] Beginning epoch 71...
+[2025-05-13 23:02:16] (step=0107000) Train Loss: 0.0737, Train Steps/Sec: 1.79, Samples/Sec: 86.13
+[2025-05-13 23:03:11] (step=0107100) Train Loss: 0.0700, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:04:06] (step=0107200) Train Loss: 0.0755, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 23:05:02] (step=0107300) Train Loss: 0.0698, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:05:57] (step=0107400) Train Loss: 0.0769, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-13 23:06:53] (step=0107500) Train Loss: 0.0779, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 23:07:48] (step=0107600) Train Loss: 0.0715, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 23:08:44] (step=0107700) Train Loss: 0.0764, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 23:09:39] (step=0107800) Train Loss: 0.0746, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:10:34] (step=0107900) Train Loss: 0.0700, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:11:30] (step=0108000) Train Loss: 0.0758, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:11:42] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 23:12:37] (step=0108100) Train Loss: 0.0702, Train Steps/Sec: 1.48, Samples/Sec: 71.20
+[2025-05-13 23:13:33] (step=0108200) Train Loss: 0.0765, Train Steps/Sec: 1.80, Samples/Sec: 86.44
+[2025-05-13 23:14:28] (step=0108300) Train Loss: 0.0740, Train Steps/Sec: 1.81, Samples/Sec: 86.66
+[2025-05-13 23:15:23] (step=0108400) Train Loss: 0.0796, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:16:19] (step=0108500) Train Loss: 0.0721, Train Steps/Sec: 1.81, Samples/Sec: 86.67
+[2025-05-13 23:16:21] Beginning epoch 72...
+[2025-05-13 23:17:15] (step=0108600) Train Loss: 0.0707, Train Steps/Sec: 1.79, Samples/Sec: 86.06
+[2025-05-13 23:18:10] (step=0108700) Train Loss: 0.0699, Train Steps/Sec: 1.80, Samples/Sec: 86.57
+[2025-05-13 23:19:06] (step=0108800) Train Loss: 0.0734, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 23:20:01] (step=0108900) Train Loss: 0.0728, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 23:20:56] (step=0109000) Train Loss: 0.0715, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 23:21:52] (step=0109100) Train Loss: 0.0663, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 23:22:47] (step=0109200) Train Loss: 0.0705, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 23:23:43] (step=0109300) Train Loss: 0.0714, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 23:24:38] (step=0109400) Train Loss: 0.0725, Train Steps/Sec: 1.80, Samples/Sec: 86.50
+[2025-05-13 23:25:34] (step=0109500) Train Loss: 0.0681, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 23:26:29] (step=0109600) Train Loss: 0.0675, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-13 23:27:25] (step=0109700) Train Loss: 0.0743, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 23:28:20] (step=0109800) Train Loss: 0.0715, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 23:29:15] (step=0109900) Train Loss: 0.0752, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:30:11] (step=0110000) Train Loss: 0.0713, Train Steps/Sec: 1.81, Samples/Sec: 86.67
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 23:30:23] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-13 23:30:25] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-13 23:32:45] (step=0110000) Perceptual Loss: 0.3485, Eval Time: 142.16
+[2025-05-13 23:32:51] Beginning epoch 73...
+[2025-05-13 23:33:41] (step=0110100) Train Loss: 0.0743, Train Steps/Sec: 0.48, Samples/Sec: 22.83
+[2025-05-13 23:34:36] (step=0110200) Train Loss: 0.0694, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 23:35:32] (step=0110300) Train Loss: 0.0693, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 23:36:27] (step=0110400) Train Loss: 0.0721, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 23:37:23] (step=0110500) Train Loss: 0.0705, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 23:38:18] (step=0110600) Train Loss: 0.0721, Train Steps/Sec: 1.80, Samples/Sec: 86.59
+[2025-05-13 23:39:14] (step=0110700) Train Loss: 0.0696, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-13 23:40:09] (step=0110800) Train Loss: 0.0772, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-13 23:41:05] (step=0110900) Train Loss: 0.0719, Train Steps/Sec: 1.80, Samples/Sec: 86.58
+[2025-05-13 23:42:00] (step=0111000) Train Loss: 0.0722, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-13 23:42:55] (step=0111100) Train Loss: 0.0708, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:43:51] (step=0111200) Train Loss: 0.0712, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-13 23:44:46] (step=0111300) Train Loss: 0.0733, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 23:45:42] (step=0111400) Train Loss: 0.0734, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-13 23:46:37] (step=0111500) Train Loss: 0.0669, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+[2025-05-13 23:46:47] Beginning epoch 74...
+[2025-05-13 23:47:33] (step=0111600) Train Loss: 0.0743, Train Steps/Sec: 1.79, Samples/Sec: 85.89
+[2025-05-13 23:48:29] (step=0111700) Train Loss: 0.0703, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 23:49:24] (step=0111800) Train Loss: 0.0707, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 23:50:20] (step=0111900) Train Loss: 0.0728, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 23:51:15] (step=0112000) Train Loss: 0.0731, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-13 23:51:28] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-13 23:52:23] (step=0112100) Train Loss: 0.0713, Train Steps/Sec: 1.47, Samples/Sec: 70.41
+[2025-05-13 23:53:19] (step=0112200) Train Loss: 0.0769, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 23:54:14] (step=0112300) Train Loss: 0.0723, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-13 23:55:09] (step=0112400) Train Loss: 0.0703, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-13 23:56:05] (step=0112500) Train Loss: 0.0692, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-13 23:57:00] (step=0112600) Train Loss: 0.0693, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-13 23:57:56] (step=0112700) Train Loss: 0.0713, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-13 23:58:51] (step=0112800) Train Loss: 0.0739, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-13 23:59:47] (step=0112900) Train Loss: 0.0718, Train Steps/Sec: 1.79, Samples/Sec: 86.10
+[2025-05-14 00:00:43] (step=0113000) Train Loss: 0.0737, Train Steps/Sec: 1.80, Samples/Sec: 86.34
+[2025-05-14 00:00:57] Beginning epoch 75...
+[2025-05-14 00:01:39] (step=0113100) Train Loss: 0.0681, Train Steps/Sec: 1.79, Samples/Sec: 85.85
+[2025-05-14 00:02:34] (step=0113200) Train Loss: 0.0681, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-14 00:03:30] (step=0113300) Train Loss: 0.0720, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-14 00:04:26] (step=0113400) Train Loss: 0.0723, Train Steps/Sec: 1.79, Samples/Sec: 86.03
+[2025-05-14 00:05:21] (step=0113500) Train Loss: 0.0737, Train Steps/Sec: 1.80, Samples/Sec: 86.44
+[2025-05-14 00:06:17] (step=0113600) Train Loss: 0.0723, Train Steps/Sec: 1.80, Samples/Sec: 86.61
+[2025-05-14 00:07:12] (step=0113700) Train Loss: 0.0679, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-14 00:08:07] (step=0113800) Train Loss: 0.0710, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-14 00:09:03] (step=0113900) Train Loss: 0.0701, Train Steps/Sec: 1.81, Samples/Sec: 86.64
+[2025-05-14 00:09:58] (step=0114000) Train Loss: 0.0676, Train Steps/Sec: 1.81, Samples/Sec: 86.65
+[2025-05-14 00:10:11] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-14 00:11:06] (step=0114100) Train Loss: 0.0662, Train Steps/Sec: 1.47, Samples/Sec: 70.60
+[2025-05-14 00:12:02] (step=0114200) Train Loss: 0.0737, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-14 00:12:57] (step=0114300) Train Loss: 0.0727, Train Steps/Sec: 1.80, Samples/Sec: 86.62
+[2025-05-14 00:13:52] (step=0114400) Train Loss: 0.0677, Train Steps/Sec: 1.80, Samples/Sec: 86.64
+[2025-05-14 00:14:48] (step=0114500) Train Loss: 0.0695, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-14 00:15:06] Beginning epoch 76...
+[2025-05-14 00:15:44] (step=0114600) Train Loss: 0.0658, Train Steps/Sec: 1.78, Samples/Sec: 85.60
+[2025-05-14 00:16:39] (step=0114700) Train Loss: 0.0699, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-14 00:17:35] (step=0114800) Train Loss: 0.0735, Train Steps/Sec: 1.80, Samples/Sec: 86.19
+[2025-05-14 00:18:31] (step=0114900) Train Loss: 0.0716, Train Steps/Sec: 1.80, Samples/Sec: 86.55
+[2025-05-14 00:19:26] (step=0115000) Train Loss: 0.0695, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-14 00:20:22] (step=0115100) Train Loss: 0.0709, Train Steps/Sec: 1.80, Samples/Sec: 86.52
+[2025-05-14 00:21:17] (step=0115200) Train Loss: 0.0680, Train Steps/Sec: 1.80, Samples/Sec: 86.63
+[2025-05-14 00:22:12] (step=0115300) Train Loss: 0.0707, Train Steps/Sec: 1.80, Samples/Sec: 86.60
+[2025-05-14 00:23:08] (step=0115400) Train Loss: 0.0754, Train Steps/Sec: 1.80, Samples/Sec: 86.25
+[2025-05-14 00:24:04] (step=0115500) Train Loss: 0.0700, Train Steps/Sec: 1.80, Samples/Sec: 86.26
+[2025-05-14 00:24:59] (step=0115600) Train Loss: 0.0734, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-14 00:25:55] (step=0115700) Train Loss: 0.0663, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-14 00:26:51] (step=0115800) Train Loss: 0.0719, Train Steps/Sec: 1.80, Samples/Sec: 86.32
+[2025-05-14 00:27:46] (step=0115900) Train Loss: 0.0726, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-14 00:28:42] (step=0116000) Train Loss: 0.0657, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-14 00:28:54] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-14 00:29:16] Beginning epoch 77...
+[2025-05-14 00:29:51] (step=0116100) Train Loss: 0.0710, Train Steps/Sec: 1.45, Samples/Sec: 69.73
+[2025-05-14 00:30:46] (step=0116200) Train Loss: 0.0693, Train Steps/Sec: 1.79, Samples/Sec: 86.14
+[2025-05-14 00:31:42] (step=0116300) Train Loss: 0.0686, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-14 00:32:37] (step=0116400) Train Loss: 0.0702, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-14 00:33:33] (step=0116500) Train Loss: 0.0697, Train Steps/Sec: 1.80, Samples/Sec: 86.35
+[2025-05-14 00:34:29] (step=0116600) Train Loss: 0.0697, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-14 00:35:24] (step=0116700) Train Loss: 0.0726, Train Steps/Sec: 1.80, Samples/Sec: 86.36
+[2025-05-14 00:36:20] (step=0116800) Train Loss: 0.0721, Train Steps/Sec: 1.79, Samples/Sec: 86.05
+[2025-05-14 00:37:15] (step=0116900) Train Loss: 0.0649, Train Steps/Sec: 1.80, Samples/Sec: 86.44
+[2025-05-14 00:38:11] (step=0117000) Train Loss: 0.0760, Train Steps/Sec: 1.80, Samples/Sec: 86.42
+[2025-05-14 00:39:07] (step=0117100) Train Loss: 0.0654, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-14 00:40:02] (step=0117200) Train Loss: 0.0691, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-14 00:40:58] (step=0117300) Train Loss: 0.0685, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-14 00:41:53] (step=0117400) Train Loss: 0.0700, Train Steps/Sec: 1.80, Samples/Sec: 86.21
+[2025-05-14 00:42:49] (step=0117500) Train Loss: 0.0709, Train Steps/Sec: 1.79, Samples/Sec: 86.02
+[2025-05-14 00:43:15] Beginning epoch 78...
+[2025-05-14 00:43:45] (step=0117600) Train Loss: 0.0671, Train Steps/Sec: 1.79, Samples/Sec: 85.79
+[2025-05-14 00:44:41] (step=0117700) Train Loss: 0.0723, Train Steps/Sec: 1.80, Samples/Sec: 86.39
+[2025-05-14 00:45:36] (step=0117800) Train Loss: 0.0711, Train Steps/Sec: 1.80, Samples/Sec: 86.41
+[2025-05-14 00:46:32] (step=0117900) Train Loss: 0.0731, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-14 00:47:28] (step=0118000) Train Loss: 0.0678, Train Steps/Sec: 1.80, Samples/Sec: 86.20
+[2025-05-14 00:47:40] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/latest.pth.tar
+[2025-05-14 00:48:36] (step=0118100) Train Loss: 0.0664, Train Steps/Sec: 1.46, Samples/Sec: 70.19
+[2025-05-14 00:49:32] (step=0118200) Train Loss: 0.0727, Train Steps/Sec: 1.79, Samples/Sec: 85.95
+[2025-05-14 00:50:27] (step=0118300) Train Loss: 0.0686, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-14 00:51:23] (step=0118400) Train Loss: 0.0702, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-14 00:52:18] (step=0118500) Train Loss: 0.0692, Train Steps/Sec: 1.80, Samples/Sec: 86.38
+[2025-05-14 00:53:14] (step=0118600) Train Loss: 0.0713, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-14 00:54:10] (step=0118700) Train Loss: 0.0760, Train Steps/Sec: 1.80, Samples/Sec: 86.40
+[2025-05-14 00:55:05] (step=0118800) Train Loss: 0.0727, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-14 00:56:01] (step=0118900) Train Loss: 0.0722, Train Steps/Sec: 1.80, Samples/Sec: 86.33
+[2025-05-14 00:56:56] (step=0119000) Train Loss: 0.0685, Train Steps/Sec: 1.80, Samples/Sec: 86.37
+[2025-05-14 00:57:26] Beginning epoch 79...
+[2025-05-14 00:57:52] (step=0119100) Train Loss: 0.0719, Train Steps/Sec: 1.79, Samples/Sec: 85.85
+[2025-05-14 00:58:48] (step=0119200) Train Loss: 0.0694, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-14 00:59:43] (step=0119300) Train Loss: 0.0717, Train Steps/Sec: 1.80, Samples/Sec: 86.56
+[2025-05-14 01:00:39] (step=0119400) Train Loss: 0.0651, Train Steps/Sec: 1.80, Samples/Sec: 86.22
+[2025-05-14 01:01:35] (step=0119500) Train Loss: 0.0720, Train Steps/Sec: 1.80, Samples/Sec: 86.19
+[2025-05-14 01:02:30] (step=0119600) Train Loss: 0.0714, Train Steps/Sec: 1.80, Samples/Sec: 86.29
+[2025-05-14 01:03:26] (step=0119700) Train Loss: 0.0678, Train Steps/Sec: 1.80, Samples/Sec: 86.48
+[2025-05-14 01:04:21] (step=0119800) Train Loss: 0.0699, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-14 01:05:17] (step=0119900) Train Loss: 0.0682, Train Steps/Sec: 1.80, Samples/Sec: 86.53
+[2025-05-14 01:06:12] (step=0120000) Train Loss: 0.0692, Train Steps/Sec: 1.80, Samples/Sec: 86.51
+Using cache found in ./models/facebookresearch_dino_main
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 01:06:38] Saved checkpoint to logs/nwm_cdit_wuhan/checkpoints/0120000.pth.tar
+Using cache found in ./models/facebookresearch_dino_main
+[2025-05-14 01:06:42] Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!
+[2025-05-14 01:09:02] (step=0120000) Perceptual Loss: 0.3585, Eval Time: 143.48
+[2025-05-14 01:09:58] (step=0120100) Train Loss: 0.0695, Train Steps/Sec: 0.44, Samples/Sec: 21.29
+[2025-05-14 01:10:53] (step=0120200) Train Loss: 0.0661, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-14 01:11:49] (step=0120300) Train Loss: 0.0679, Train Steps/Sec: 1.80, Samples/Sec: 86.28
+[2025-05-14 01:12:45] (step=0120400) Train Loss: 0.0644, Train Steps/Sec: 1.80, Samples/Sec: 86.23
+[2025-05-14 01:13:40] (step=0120500) Train Loss: 0.0688, Train Steps/Sec: 1.80, Samples/Sec: 86.46
+[2025-05-14 01:14:13] Beginning epoch 80...
+[2025-05-14 01:14:36] (step=0120600) Train Loss: 0.0682, Train Steps/Sec: 1.79, Samples/Sec: 86.01
+[2025-05-14 01:15:32] (step=0120700) Train Loss: 0.0689, Train Steps/Sec: 1.80, Samples/Sec: 86.27
+[2025-05-14 01:16:27] (step=0120800) Train Loss: 0.0716, Train Steps/Sec: 1.80, Samples/Sec: 86.42
+[2025-05-14 01:17:23] (step=0120900) Train Loss: 0.0649, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-14 01:18:18] (step=0121000) Train Loss: 0.0769, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-14 01:19:14] (step=0121100) Train Loss: 0.0756, Train Steps/Sec: 1.80, Samples/Sec: 86.54
+[2025-05-14 01:19:26,470] torch.distributed.elastic.agent.server.api: [WARNING] Received Signals.SIGINT death signal, shutting down workers
+[2025-05-14 01:19:26,472] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 986901 closing signal SIGINT
+[2025-05-14 01:19:26,472] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 986902 closing signal SIGINT
+[2025-05-14 01:19:26,472] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 986903 closing signal SIGINT
+Traceback (most recent call last):
+  File "train.py", line 437, in <module>
+Traceback (most recent call last):
+  File "train.py", line 437, in <module>
+Traceback (most recent call last):
+  File "train.py", line 437, in <module>
+    main(args)
+  File "train.py", line 300, in main
+    scaler.scale(loss).backward()
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_tensor.py", line 492, in backward
+    main(args)
+  File "train.py", line 300, in main
+    scaler.scale(loss).backward()
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_tensor.py", line 492, in backward
+    main(args)
+  File "train.py", line 300, in main
+    scaler.scale(loss).backward()
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/_tensor.py", line 492, in backward
+        torch.autograd.backward(torch.autograd.backward(
+
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/autograd/__init__.py", line 251, in backward
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/autograd/__init__.py", line 251, in backward
+    torch.autograd.backward(
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/autograd/__init__.py", line 251, in backward
+            Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward passVariable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward passVariable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+
+
+KeyboardInterruptKeyboardInterruptKeyboardInterrupt
+
+
+Traceback (most recent call last):
+  File "/data1/tpz/anaconda3/envs/nwm2/bin/torchrun", line 8, in <module>
+    sys.exit(main())
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
+    return f(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/run.py", line 806, in main
+    run(args)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/run.py", line 797, in run
+    elastic_launch(
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 134, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 255, in launch_agent
+    result = agent.run()
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/metrics/api.py", line 124, in wrapper
+    result = f(*args, **kwargs)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 736, in run
+    result = self._invoke_run(role)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/agent/server/api.py", line 877, in _invoke_run
+    time.sleep(monitor_interval)
+  File "/data1/zwc/miniconda3/envs/nwm2/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/api.py", line 62, in _terminate_process_handler
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+torch.distributed.elastic.multiprocessing.api.SignalException: Process 986831 got signal: 2