rti/minit2i.py

"""MiniT2I: pixel-space MMDiT text-to-image backbone.

Port of the released model (https://huggingface.co/MiniT2I/MiniT2I) that matches
the published checkpoints key for key, so RoPE and block internals must not be
"simplified".

There is no AdaLN: conditioning runs entirely through the joint text+image
attention stream, with frozen FLAN-T5-Large embeddings as the text stream. The
time and pooled-text `vec` exist in the checkpoint but the blocks ignore them.

Flow matching: x_t = t*img + (1-t)*noise with noise scaled by 2; the net predicts
x0, and v = (x0 - x_t) / clamp(1-t). t=1 is clean, t=0 is noise.
"""
from __future__ import annotations

import math
import os
from dataclasses import dataclass, replace
from typing import Optional

import torch
import torch.nn.functional as F
from torch import nn

MINIT2I_HF_REPO = "MiniT2I/MiniT2I"


# ---------------------------------------------------------------------------
# Primitives (verbatim from upstream mmdit.py)
# ---------------------------------------------------------------------------
def rotate_half(x: torch.Tensor) -> torch.Tensor:
    # NOTE: interleaved variant (reshape → 2 → -1), NOT the half-split variant.
    # The checkpoint's RoPE depends on this exact form.
    x1, x2 = x.reshape(*x.shape[:-1], 2, -1).unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(dim))
        self.eps = eps

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        y = x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
        return y * self.weight


class TimestepEmbedder(nn.Module):
    """Kept for state-dict compatibility; its output is unused in the forward."""

    def __init__(self, hidden_size: int, frequency_embedding_size: int = 256):
        super().__init__()
        self.frequency_embedding_size = frequency_embedding_size
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size),
        )

    def forward(self, t: torch.Tensor) -> torch.Tensor:
        half = self.frequency_embedding_size // 2
        freqs = torch.exp(
            -math.log(10000.0)
            * torch.arange(half, device=t.device, dtype=torch.float32)
            / half
        )
        args = t.float()[:, None] * freqs[None]
        emb = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        return self.mlp(emb.to(dtype=self.mlp[0].weight.dtype))


class BottleneckPatchEmbed(nn.Module):
    """proj1: (in_ch -> pca) strided conv; proj2: (pca -> D) 1x1 conv."""

    def __init__(self, img_size=512, patch_size=16, in_channels=3, pca_channels=128, hidden_size=768):
        super().__init__()
        self.img_size = img_size
        self.patch_size = patch_size
        self.proj1 = nn.Conv2d(in_channels, pca_channels, kernel_size=patch_size, stride=patch_size, bias=False)
        self.proj2 = nn.Conv2d(pca_channels, hidden_size, kernel_size=1, stride=1, bias=True)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.proj2(self.proj1(x))
        return x.flatten(2).transpose(1, 2)


class SwiGLUMlp(nn.Module):
    def __init__(self, in_features: int, hidden_features: int):
        super().__init__()
        hidden_dim = (hidden_features + 7) // 8 * 8
        self.w1 = nn.Linear(in_features, hidden_dim, bias=False)
        self.w3 = nn.Linear(in_features, hidden_dim, bias=False)
        self.w2 = nn.Linear(hidden_dim, in_features, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w2(F.silu(self.w1(x)) * self.w3(x))


class TextRotaryEmbedding1D(nn.Module):
    def __init__(self, head_dim: int, theta: float = 10000.0):
        super().__init__()
        self.head_dim = head_dim
        self.theta = theta

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        b, length, h, d = x.shape
        inv = 1.0 / (self.theta ** (torch.arange(0, d, 2, device=x.device, dtype=torch.float32) / d))
        pos = torch.arange(length, device=x.device, dtype=torch.float32)
        angles = torch.einsum("l,f->lf", pos, inv)
        angles = torch.cat([angles, angles], dim=-1)
        cos = angles.cos().to(dtype=x.dtype)
        sin = angles.sin().to(dtype=x.dtype)
        return x * cos[None, :, None, :] + rotate_half(x) * sin[None, :, None, :]


class VisionRotaryEmbeddingFast(nn.Module):
    def __init__(self, head_dim: int, theta: float = 10000.0):
        super().__init__()
        self.dim = head_dim // 2
        self.theta = theta

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        length = x.shape[1]
        side = int(math.sqrt(length))
        if side * side != length:
            raise ValueError(f"image token length must be square, got {length}")
        freqs = 1.0 / (
            self.theta
            ** (torch.arange(0, self.dim, 2, device=x.device, dtype=torch.float32)[: self.dim // 2] / self.dim)
        )
        t = torch.arange(side, device=x.device, dtype=torch.float32)
        base = torch.einsum("l,f->lf", t, freqs)
        f_h, f_w = torch.broadcast_tensors(base[:, None, :], base[None, :, :])
        angles = torch.cat([f_h, f_w], dim=-1)
        angles = torch.cat([angles, angles], dim=-1).reshape(length, -1)
        cos = angles.cos().to(dtype=x.dtype)
        sin = angles.sin().to(dtype=x.dtype)
        return x * cos[None, :, None, :] + rotate_half(x) * sin[None, :, None, :]


class MultiModalRotaryEmbeddingFast(nn.Module):
    def __init__(self, head_dim: int):
        super().__init__()
        self.text_rope = TextRotaryEmbedding1D(head_dim)
        self.vision_rope = VisionRotaryEmbeddingFast(head_dim)

    def forward(self, x: torch.Tensor, txt_len: int) -> torch.Tensor:
        txt = self.text_rope(x[:, :txt_len])
        img = self.vision_rope(x[:, txt_len:])
        return torch.cat([txt, img], dim=1)


# ---------------------------------------------------------------------------
# Blocks
# ---------------------------------------------------------------------------
class PlainTextTransformerBlock(nn.Module):
    def __init__(self, hidden_size=768, num_heads=12, head_dim=64, mlp_ratio=2.6667):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = head_dim
        inner_dim = num_heads * head_dim
        self.norm1 = RMSNorm(hidden_size)
        self.norm2 = RMSNorm(hidden_size)
        self.qkv = nn.Linear(hidden_size, inner_dim * 3)
        self.attn_proj = nn.Linear(inner_dim, hidden_size)
        self.mlp = SwiGLUMlp(hidden_size, int(hidden_size * mlp_ratio))
        self.q_norm = RMSNorm(head_dim)
        self.k_norm = RMSNorm(head_dim)
        self.rope = TextRotaryEmbedding1D(head_dim)

    def forward(self, txt: torch.Tensor) -> torch.Tensor:
        b, length, _ = txt.shape
        qkv = self.qkv(self.norm1(txt)).reshape(b, length, 3, self.num_heads, self.head_dim)
        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
        q = self.rope(self.q_norm(q))
        k = self.rope(self.k_norm(k))
        attn = torch.einsum("bqhd,bkhd->bhqk", q, k) * (self.head_dim ** -0.5)
        out = torch.einsum("bhqk,bkhd->bqhd", attn.softmax(dim=-1), v).reshape(b, length, -1)
        txt = txt + self.attn_proj(out)
        txt = txt + self.mlp(self.norm2(txt))
        return txt


class DoubleStreamDiTBlock(nn.Module):
    def __init__(self, hidden_size=768, txt_hidden_size=768, num_heads=12, head_dim=64, mlp_ratio=2.6667):
        super().__init__()
        self.hidden_size = hidden_size
        self.txt_hidden_size = txt_hidden_size
        self.num_heads = num_heads
        self.head_dim = head_dim
        inner_dim = num_heads * head_dim
        self.img_norm1 = RMSNorm(hidden_size)
        self.img_norm2 = RMSNorm(hidden_size)
        self.txt_norm1 = RMSNorm(txt_hidden_size)
        self.txt_norm2 = RMSNorm(txt_hidden_size)
        self.img_qkv = nn.Linear(hidden_size, inner_dim * 3)
        self.txt_qkv = nn.Linear(txt_hidden_size, inner_dim * 3)
        self.q_norm = RMSNorm(head_dim)
        self.k_norm = RMSNorm(head_dim)
        self.rope = MultiModalRotaryEmbeddingFast(head_dim)
        self.img_attn_proj = nn.Linear(inner_dim, hidden_size)
        self.txt_attn_proj = nn.Linear(inner_dim, txt_hidden_size)
        self.img_mlp = SwiGLUMlp(hidden_size, int(hidden_size * mlp_ratio))
        self.txt_mlp = SwiGLUMlp(txt_hidden_size, int(txt_hidden_size * mlp_ratio))

    def forward(self, x: torch.Tensor, txt: torch.Tensor, vec=None):
        b, li, _ = x.shape
        lt = txt.shape[1]
        x_norm = self.img_norm1(x)
        txt_norm = self.txt_norm1(txt)
        qkv_i = self.img_qkv(x_norm).reshape(b, li, 3, self.num_heads, self.head_dim)
        qkv_t = self.txt_qkv(txt_norm).reshape(b, lt, 3, self.num_heads, self.head_dim)
        q_i, k_i, v_i = qkv_i[:, :, 0], qkv_i[:, :, 1], qkv_i[:, :, 2]
        q_t, k_t, v_t = qkv_t[:, :, 0], qkv_t[:, :, 1], qkv_t[:, :, 2]
        q_i, k_i = self.q_norm(q_i), self.k_norm(k_i)
        q_t, k_t = self.q_norm(q_t), self.k_norm(k_t)
        q = self.rope(torch.cat([q_t, q_i], dim=1), txt_len=lt)
        k = self.rope(torch.cat([k_t, k_i], dim=1), txt_len=lt)
        v = torch.cat([v_t, v_i], dim=1)
        attn = torch.einsum("bqhd,bkhd->bhqk", q, k) * (self.head_dim ** -0.5)
        out = torch.einsum("bhqk,bkhd->bqhd", attn.softmax(dim=-1), v)
        x = x + self.img_attn_proj(out[:, lt:].reshape(b, li, -1))
        txt = txt + self.txt_attn_proj(out[:, :lt].reshape(b, lt, -1))
        x = x + self.img_mlp(self.img_norm2(x))
        txt = txt + self.txt_mlp(self.txt_norm2(txt))
        return x, txt


class FinalLayer(nn.Module):
    def __init__(self, hidden_size=768, patch_size=16, out_channels=3):
        super().__init__()
        self.patch_size = patch_size
        self.out_channels = out_channels
        self.norm_final = RMSNorm(hidden_size)
        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels)

    def forward(self, x: torch.Tensor, vec=None) -> torch.Tensor:
        return self.linear(self.norm_final(x))


def get_1d_sincos_pos_embed(embed_dim: int, pos: torch.Tensor) -> torch.Tensor:
    omega = torch.arange(embed_dim // 2, device=pos.device, dtype=torch.float32)
    omega = 1.0 / (10000 ** (omega / (embed_dim / 2.0)))
    out = torch.einsum("m,d->md", pos.reshape(-1), omega)
    return torch.cat([out.sin(), out.cos()], dim=1)


def get_2d_sincos_pos_embed(embed_dim: int, grid_size: int, device, dtype) -> torch.Tensor:
    grid_h = torch.arange(grid_size, device=device, dtype=torch.float32)
    grid_w = torch.arange(grid_size, device=device, dtype=torch.float32)
    grid = torch.meshgrid(grid_w, grid_h, indexing="xy")
    grid = torch.stack(grid, dim=0).reshape(2, 1, grid_size, grid_size)
    emb_h = get_1d_sincos_pos_embed(embed_dim // 2, grid[0])
    emb_w = get_1d_sincos_pos_embed(embed_dim // 2, grid[1])
    return torch.cat([emb_h, emb_w], dim=1).to(dtype=dtype)


# ---------------------------------------------------------------------------
# Config + net
# ---------------------------------------------------------------------------
@dataclass
class MMJiTConfig:
    image_size: int = 512
    patch_size: int = 16
    in_channels: int = 3
    txt_input_size: int = 1024          # FLAN-T5-Large hidden size
    hidden_size: int = 768
    txt_hidden_size: int = 768
    cond_vec_size: int = 768
    depth_double: int = 17
    txt_preamble_depth: int = 2
    num_heads: int = 12
    head_dim: int = 64
    mlp_ratio: float = 2.6667
    pca_channels: int = 128
    prompt_length: int = 256
    n_T: int = 100
    prediction: str = "x"
    sampler: str = "euler"
    cfg_channels: int = 3
    cfg_interval: tuple = (0.0, 1.0)
    llm: str = "google/flan-t5-large"


class MMJiT(nn.Module):
    """The `net` sub-tree. State-dict matches the `model.net.<...>` checkpoint keys."""

    def __init__(self, cfg: MMJiTConfig):
        super().__init__()
        self.cfg = cfg
        self.latent_img_size = cfg.image_size // cfg.patch_size
        self.img_embedder = BottleneckPatchEmbed(
            cfg.image_size, cfg.patch_size, cfg.in_channels, cfg.pca_channels, cfg.hidden_size
        )
        self.txt_embedder = nn.Linear(cfg.txt_input_size, cfg.txt_hidden_size, bias=False)
        self.mask_token = nn.Parameter(torch.zeros(1, 1, cfg.txt_input_size))
        self.t_embedder = TimestepEmbedder(cfg.cond_vec_size)
        self.pooled_embedder = nn.Linear(cfg.txt_input_size, cfg.cond_vec_size, bias=False)
        self.txt_preamble_blocks = nn.ModuleList(
            [
                PlainTextTransformerBlock(cfg.txt_hidden_size, cfg.num_heads, cfg.head_dim, cfg.mlp_ratio)
                for _ in range(cfg.txt_preamble_depth)
            ]
        )
        self.double_blocks = nn.ModuleList(
            [
                DoubleStreamDiTBlock(
                    cfg.hidden_size, cfg.txt_hidden_size, cfg.num_heads, cfg.head_dim, cfg.mlp_ratio
                )
                for _ in range(cfg.depth_double)
            ]
        )
        self.final_layer = FinalLayer(cfg.hidden_size, cfg.patch_size, cfg.in_channels)

    def unpatchify(self, x: torch.Tensor) -> torch.Tensor:
        b = x.shape[0]
        p = self.cfg.patch_size
        c = self.cfg.in_channels
        h = w = int(math.sqrt(x.shape[1]))
        x = x.reshape(b, h, w, p, p, c)
        x = torch.einsum("nhwpqc->nchpwq", x)
        return x.reshape(b, c, h * p, w * p)

    def forward(self, img: torch.Tensor, t: torch.Tensor, context: torch.Tensor, attn_mask: torch.Tensor) -> torch.Tensor:
        if img.ndim == 4 and img.shape[1] != self.cfg.in_channels:
            img = img.permute(0, 3, 1, 2)
        attn_mask = attn_mask.to(device=context.device)
        context = torch.where(attn_mask[:, :, None] > 0.5, context, self.mask_token.to(dtype=context.dtype))
        x = self.img_embedder(img)
        pos = get_2d_sincos_pos_embed(self.cfg.hidden_size, self.latent_img_size, x.device, x.dtype)
        x = x + pos[None]
        t_vec = self.t_embedder(t)
        txt = self.txt_embedder(context.to(dtype=self.txt_embedder.weight.dtype))
        pooled_text = context.mean(dim=1)
        vec = t_vec + self.pooled_embedder(pooled_text.to(dtype=self.pooled_embedder.weight.dtype))
        for block in self.txt_preamble_blocks:
            txt = block(txt)
        for block in self.double_blocks:
            x, txt = block(x, txt, vec)
        combined = torch.cat([txt, x], dim=1)
        out = self.final_layer(combined, vec)
        img_out = out[:, txt.shape[1]:, :]
        return self.unpatchify(img_out)


# ---------------------------------------------------------------------------
# Wrapper (mirrors upstream DiffusionModel): flow-matching + Euler/CFG sampler
# ---------------------------------------------------------------------------
class MiniT2I(nn.Module):
    """Holds `self.net = MMJiT(cfg)` under the same name the checkpoint uses.

    `forward` returns the x0 prediction (`predict='x'`), so the repo's existing
    x→velocity machinery applies. `sample()` runs the native Euler + CFG loop.
    """

    NOISE_SCALE = 2.0
    T_EPS = 0.001

    def __init__(self, cfg: MMJiTConfig):
        super().__init__()
        self.cfg = cfg
        self.net = MMJiT(cfg)

    @property
    def image_size(self) -> int:
        return self.cfg.image_size

    def forward(self, img: torch.Tensor, t: torch.Tensor, context: torch.Tensor, attn_mask: torch.Tensor) -> torch.Tensor:
        return self.net(img, t, context, attn_mask)

    def pred_velocity(self, x, t, context, mask):
        x0 = self.net(x, t, context, mask)
        return (x0 - x) / torch.clamp(1 - t[:, None, None, None], min=self.T_EPS)

    def cfg_velocity(self, x, t, context, mask, cfg_scale: float):
        b = x.shape[0]
        xx = torch.cat([x, x], dim=0)
        tt = torch.cat([t, t], dim=0)
        cc = torch.cat([context, context], dim=0)
        mm = torch.cat([mask, torch.zeros_like(mask)], dim=0)   # null branch = all-masked
        out = self.pred_velocity(xx, tt, cc, mm)
        cond, uncond = out[:b], out[b:]
        lo, hi = self.cfg.cfg_interval
        use_cfg = ((t >= lo) & (t <= hi)).to(out.dtype)[:, None, None, None]
        scale = torch.where(use_cfg > 0, out.new_tensor(cfg_scale), out.new_tensor(1.0))
        return uncond + (cond - uncond) * scale

    @torch.no_grad()
    def sample(self, context, mask, cfg_scale: float = 2.5, steps: Optional[int] = None,
               generator=None, progress: bool = False) -> torch.Tensor:
        """Euler flow-matching sampler. Returns pixels in [-1, 1]."""
        steps = int(steps or self.cfg.n_T)
        b = context.shape[0]
        device = context.device
        dtype = next(self.parameters()).dtype
        x = torch.randn(
            b, self.cfg.in_channels, self.cfg.image_size, self.cfg.image_size,
            generator=generator, device=device, dtype=dtype,
        ) * self.NOISE_SCALE
        timesteps = torch.linspace(0.0, 1.0, steps + 1, device=device, dtype=dtype)
        iterator = range(steps)
        if progress:
            from tqdm.auto import tqdm
            iterator = tqdm(iterator)
        context = context.to(dtype)
        mask = mask.to(dtype)
        for i in iterator:
            t_cur = timesteps[i].expand(b)
            t_next = timesteps[i + 1].expand(b)
            if cfg_scale != 1.0:
                v = self.cfg_velocity(x, t_cur, context, mask, cfg_scale)
            else:
                v = self.pred_velocity(x, t_cur, context, mask)
            x = x + (t_next - t_cur)[:, None, None, None] * v
        return x.clamp(-1, 1)


# ---------------------------------------------------------------------------
# Registry + pretrained loader
# ---------------------------------------------------------------------------
# cfg_vec_size / txt_hidden_size follow hidden_size for both released variants.
MiniT2I_configs = {
    "MiniT2I-B/16": MMJiTConfig(
        hidden_size=768, txt_hidden_size=768, cond_vec_size=768,
        depth_double=17, num_heads=12, head_dim=64, mlp_ratio=2.6667,
    ),
    "MiniT2I-L/16": MMJiTConfig(
        hidden_size=1248, txt_hidden_size=1248, cond_vec_size=1248,
        depth_double=23, num_heads=24, head_dim=52, mlp_ratio=2.7051282051282053,
    ),
}

MiniT2I_HF_SUBFOLDER = {
    "MiniT2I-B/16": "minit2i-b-16",
    "MiniT2I-L/16": "minit2i-l-16",
}


def _build(variant: str):
    base_cfg = MiniT2I_configs[variant]

    def ctor(**overrides) -> MiniT2I:
        # Accept common repo kwargs (image_size, in_channels, patch_size, ...) as
        # dataclass field overrides; ignore anything not in MMJiTConfig.
        fields = {f for f in base_cfg.__dataclass_fields__}
        cfg = replace(base_cfg, **{k: v for k, v in overrides.items() if k in fields})
        return MiniT2I(cfg)

    return ctor


MiniT2I_models = {name: _build(name) for name in MiniT2I_configs}


def default_cfg_scale(variant: str) -> float:
    """Recommended classifier-free guidance scale per released variant."""
    return 6.0 if "L/" in variant else 2.5


def _cache_dir(cache_dir: Optional[str]) -> Optional[str]:
    # L/16 is 3.65 GB — keep off the boot nvme by default (see CLAUDE.md).
    return cache_dir or os.environ.get("MINIT2I_CACHE") or None


@torch.no_grad()
def load_pretrained(
    variant: str,
    model: Optional[MiniT2I] = None,
    *,
    cache_dir: Optional[str] = None,
    device: str | torch.device = "cpu",
    dtype: Optional[torch.dtype] = None,
) -> MiniT2I:
    """Download the released diffusers safetensors and load them into `model`.

    Checkpoint keys are prefixed `model.net.`; we strip that and load into
    `model.net` with strict=True (the unused t/pooled embedders exist in the
    checkpoint, so strict passes cleanly).
    """
    from huggingface_hub import hf_hub_download
    from safetensors.torch import load_file

    if variant not in MiniT2I_HF_SUBFOLDER:
        raise KeyError(f"unknown MiniT2I variant {variant!r}; choices: {list(MiniT2I_HF_SUBFOLDER)}")
    if model is None:
        model = MiniT2I_models[variant]()

    sub = MiniT2I_HF_SUBFOLDER[variant]
    path = hf_hub_download(
        MINIT2I_HF_REPO,
        f"{sub}/transformer/diffusion_pytorch_model.safetensors",
        cache_dir=_cache_dir(cache_dir),
    )
    raw = load_file(path)
    prefix = "model.net."
    net_sd = {}
    for k, v in raw.items():
        if k.startswith(prefix):
            net_sd[k[len(prefix):]] = v
        elif k.startswith("net."):
            net_sd[k[len("net."):]] = v
        else:
            net_sd[k] = v
    missing, unexpected = model.net.load_state_dict(net_sd, strict=True)
    assert not missing and not unexpected, f"key mismatch: missing={missing} unexpected={unexpected}"

    model = model.to(device)
    if dtype is not None:
        model = model.to(dtype)
    return model


@torch.no_grad()
def load_pretrained_repatched(
    variant: str,
    model: MiniT2I,
    *,
    cache_dir: Optional[str] = None,
    device: str | torch.device = "cpu",
    dtype: Optional[torch.dtype] = None,
) -> tuple[MiniT2I, list]:
    """Load the released $512$/p16 weights into a RE-PATCHED model (e.g. p=32 @1024).

    Both p16@512 and p32@1024 tokenize to a $32{\\times}32$ grid, so the entire
    transformer trunk (blocks, text path, rope, embedders) transfers by shape.
    Only the patch conv (`img_embedder.proj1`) and the output head
    (`final_layer.linear`) depend on patch size and are left at init --- these must
    be finetuned. Returns (model, list of re-initialised keys)."""
    from huggingface_hub import hf_hub_download
    from safetensors.torch import load_file
    sub = MiniT2I_HF_SUBFOLDER[variant]
    path = hf_hub_download(
        MINIT2I_HF_REPO, f"{sub}/transformer/diffusion_pytorch_model.safetensors",
        cache_dir=_cache_dir(cache_dir))
    raw = load_file(path)
    net_sd = {}
    for k, v in raw.items():
        for pre in ("model.net.", "net."):
            if k.startswith(pre):
                k = k[len(pre):]; break
        net_sd[k] = v
    tgt = model.net.state_dict()
    keep = {k: v for k, v in net_sd.items() if k in tgt and tgt[k].shape == v.shape}
    reinit = sorted(set(tgt) - set(keep))
    model.net.load_state_dict(keep, strict=False)
    if int(os.environ.get("RANK", "0")) == 0:
        print(f"[repatch] transferred {len(keep)}/{len(tgt)} trunk tensors; "
              f"re-init {len(reinit)}: {reinit}", flush=True)
    model = model.to(device)
    if dtype is not None:
        model = model.to(dtype)
    return model, reinit