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from __future__ import annotations
import math
from pathlib import Path
from typing import Any, Mapping
class CausalConv3d:
def __init__(
self,
weights: Mapping[str, Any],
prefix: str,
*,
kernel_size: int | tuple[int, int, int],
padding: int | tuple[int, int, int] = 0,
stride: int | tuple[int, int, int] = 1,
) -> None:
self.weights = weights
self.prefix = prefix
self.kernel_size = _triple(kernel_size)
self.padding = _triple(padding)
self.stride = _triple(stride)
def __call__(self, x: Any) -> Any:
return causal_conv3d(
x,
self.weights[f"{self.prefix}.weight"],
self.weights.get(f"{self.prefix}.bias"),
padding=self.padding,
stride=self.stride,
)
class ResidualBlock:
def __init__(self, weights: Mapping[str, Any], prefix: str) -> None:
self.weights = weights
self.prefix = prefix
self.conv1 = CausalConv3d(weights, f"{prefix}.residual.2", kernel_size=3, padding=1)
self.conv2 = CausalConv3d(weights, f"{prefix}.residual.6", kernel_size=3, padding=1)
self.shortcut = (
CausalConv3d(weights, f"{prefix}.shortcut", kernel_size=1)
if f"{prefix}.shortcut.weight" in weights
else None
)
def __call__(self, x: Any) -> Any:
residual = vae_rms_norm(x, self.weights[f"{self.prefix}.residual.0.gamma"])
residual = silu(residual)
residual = self.conv1(residual)
residual = vae_rms_norm(residual, self.weights[f"{self.prefix}.residual.3.gamma"])
residual = silu(residual)
residual = self.conv2(residual)
shortcut = x if self.shortcut is None else self.shortcut(x)
return residual + shortcut
class AttentionBlock:
def __init__(self, weights: Mapping[str, Any], prefix: str) -> None:
self.weights = weights
self.prefix = prefix
def __call__(self, x: Any) -> Any:
import mlx.core as mx
identity = x
b, c, t, h, w = x.shape
frames = x.transpose(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
frames = vae_rms_norm(frames, self.weights[f"{self.prefix}.norm.gamma"])
qkv = conv2d(
frames,
self.weights[f"{self.prefix}.to_qkv.weight"],
self.weights.get(f"{self.prefix}.to_qkv.bias"),
)
q, k, v = mx.split(qkv, 3, axis=1)
attended = vae_attention(q, k, v)
projected = conv2d(
attended,
self.weights[f"{self.prefix}.proj.weight"],
self.weights.get(f"{self.prefix}.proj.bias"),
)
projected = projected.reshape(b, t, c, h, w).transpose(0, 2, 1, 3, 4)
return projected + identity
class Resample:
def __init__(self, weights: Mapping[str, Any], prefix: str, *, mode: str) -> None:
self.weights = weights
self.prefix = prefix
self.mode = mode
def __call__(self, x: Any) -> Any:
import mlx.core as mx
if self.mode not in {"upsample2d", "upsample3d"}:
raise NotImplementedError(f"unsupported decoder resample mode: {self.mode}")
b, c, t, h, w = x.shape
frames = x.transpose(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
frames = mx.repeat(frames, 2, axis=2)
frames = mx.repeat(frames, 2, axis=3)
frames = conv2d(
frames,
self.weights[f"{self.prefix}.resample.1.weight"],
self.weights.get(f"{self.prefix}.resample.1.bias"),
padding=1,
)
out_channels = frames.shape[1]
return frames.reshape(b, t, out_channels, h * 2, w * 2).transpose(0, 2, 1, 3, 4)
class Decoder3d:
def __init__(self, weights: Mapping[str, Any]) -> None:
self.weights = weights
self.conv1 = CausalConv3d(weights, "decoder.conv1", kernel_size=3, padding=1)
self.middle = [
ResidualBlock(weights, "decoder.middle.0"),
AttentionBlock(weights, "decoder.middle.1"),
ResidualBlock(weights, "decoder.middle.2"),
]
self.upsamples = self._build_upsamples()
self.head = CausalConv3d(weights, "decoder.head.2", kernel_size=3, padding=1)
def _build_upsamples(self) -> list[Any]:
layers: list[Any] = []
modes = {3: "upsample3d", 7: "upsample3d", 11: "upsample2d"}
for index in range(15):
prefix = f"decoder.upsamples.{index}"
if f"{prefix}.resample.1.weight" in self.weights:
layers.append(Resample(self.weights, prefix, mode=modes[index]))
else:
layers.append(ResidualBlock(self.weights, prefix))
return layers
def __call__(self, x: Any) -> Any:
x = self.conv1(x)
for layer in self.middle:
x = layer(x)
for layer in self.upsamples:
x = layer(x)
x = vae_rms_norm(x, self.weights["decoder.head.0.gamma"])
x = silu(x)
return self.head(x)
class WanVAEDecoder:
"""MLX implementation of the Anima/Wan VAE decoder path."""
def __init__(self, weights: Mapping[str, Any]) -> None:
self.weights = dict(weights)
self.conv2 = CausalConv3d(self.weights, "conv2", kernel_size=1)
self.decoder = Decoder3d(self.weights)
@classmethod
def from_safetensors(cls, path: str | Path, *, dtype: str = "float32") -> "WanVAEDecoder":
from anima_mlx.utils.weights import load_mlx_safetensors_subset
path = _resolve_vae_path(path)
weights = load_mlx_safetensors_subset(
path,
key_filter=lambda key: key.startswith(("conv2.", "decoder.")),
dtype=dtype,
)
return cls(weights)
def decode(self, latent: Any) -> Any:
return self.decoder(self.conv2(latent))
def decode_tiled(self, latent: Any, *, tile_size: int = 64, overlap: int = 16) -> Any:
"""Decode latent spatial tiles.
This is a memory fallback, not a quality-equivalent path: the Wan decoder
has a middle spatial attention block, so each tile attends over only its
local crop instead of the full latent plane.
"""
import mlx.core as mx
if tile_size <= 0:
raise ValueError("tile_size must be positive")
if overlap < 0:
raise ValueError("overlap must be non-negative")
if overlap >= tile_size:
raise ValueError("overlap must be smaller than tile_size")
batch, _, frames, latent_h, latent_w = latent.shape
if latent_h <= tile_size and latent_w <= tile_size:
return self.decode(latent)
output_h = latent_h * 8
output_w = latent_w * 8
output = mx.zeros((batch, 3, frames, output_h, output_w), dtype=latent.dtype)
output_div = mx.zeros((batch, 1, frames, output_h, output_w), dtype=latent.dtype)
y_positions = _tile_positions(latent_h, tile_size, overlap)
x_positions = _tile_positions(latent_w, tile_size, overlap)
for y in y_positions:
tile_h = min(tile_size, latent_h - y)
for x in x_positions:
tile_w = min(tile_size, latent_w - x)
tile = latent[:, :, :, y : y + tile_h, x : x + tile_w]
decoded = self.decode(tile)
mx.eval(decoded)
out_y = y * 8
out_x = x * 8
mask = _tile_mask(decoded.shape, overlap_h=min(overlap, tile_h), overlap_w=min(overlap, tile_w), dtype=decoded.dtype)
output = _add_tile(output, decoded * mask, out_y=out_y, out_x=out_x)
output_div = _add_tile(output_div, mask, out_y=out_y, out_x=out_x)
mx.eval(output, output_div)
return output / output_div
def __call__(self, latent: Any) -> Any:
return self.decode(latent)
def _tile_positions(length: int, tile_size: int, overlap: int) -> list[int]:
if length <= tile_size:
return [0]
stride = tile_size - overlap
positions: list[int] = []
current = 0
while current < length:
pos = max(0, min(length - overlap, current))
if positions and pos == positions[-1]:
break
positions.append(pos)
if pos + tile_size >= length:
break
current += stride
return positions
def _tile_mask(shape: tuple[int, ...], *, overlap_h: int, overlap_w: int, dtype: Any) -> Any:
import mlx.core as mx
_, _, frames, height, width = shape
mask = mx.ones((1, 1, frames, height, width), dtype=dtype)
feather_h = min(overlap_h * 8, height)
feather_w = min(overlap_w * 8, width)
if feather_h < height:
for index in range(feather_h):
value = mx.array((index + 1) / feather_h, dtype=dtype)
mask = mask.at[:, :, :, index : index + 1, :].multiply(value)
mask = mask.at[:, :, :, height - 1 - index : height - index, :].multiply(value)
if feather_w < width:
for index in range(feather_w):
value = mx.array((index + 1) / feather_w, dtype=dtype)
mask = mask.at[:, :, :, :, index : index + 1].multiply(value)
mask = mask.at[:, :, :, :, width - 1 - index : width - index].multiply(value)
return mask
def _add_tile(output: Any, tile: Any, *, out_y: int, out_x: int) -> Any:
height = tile.shape[-2]
width = tile.shape[-1]
return output.at[:, :, :, out_y : out_y + height, out_x : out_x + width].add(tile)
def _triple(value: int | tuple[int, int, int]) -> tuple[int, int, int]:
if isinstance(value, int):
return (value, value, value)
return value
def _pair(value: int | tuple[int, int]) -> tuple[int, int]:
if isinstance(value, int):
return (value, value)
return value
def _conv2d_weight(weight: Any) -> Any:
return weight.transpose(0, 2, 3, 1)
def _conv3d_weight(weight: Any) -> Any:
return weight.transpose(0, 2, 3, 4, 1)
def conv2d(
x: Any,
weight: Any,
bias: Any | None = None,
*,
padding: int | tuple[int, int] = 0,
stride: int | tuple[int, int] = 1,
) -> Any:
import mlx.core as mx
x_cl = x.transpose(0, 2, 3, 1)
y = mx.conv2d(x_cl, _conv2d_weight(weight), stride=_pair(stride), padding=_pair(padding))
if bias is not None:
y = y + bias.reshape(1, 1, 1, -1)
return y.transpose(0, 3, 1, 2)
def causal_conv3d(
x: Any,
weight: Any,
bias: Any | None = None,
*,
padding: int | tuple[int, int, int] = 0,
stride: int | tuple[int, int, int] = 1,
) -> Any:
import mlx.core as mx
pad_t, pad_h, pad_w = _triple(padding)
stride_t, stride_h, stride_w = _triple(stride)
if x.shape[2] == 1:
frame = x[:, :, 0, :, :]
kernel = weight[:, :, -1, :, :]
y = conv2d(frame, kernel, bias, padding=(pad_h, pad_w), stride=(stride_h, stride_w))
return y[:, :, None, :, :]
x_cl = x.transpose(0, 2, 3, 4, 1)
if pad_t:
x_cl = mx.pad(x_cl, [(0, 0), (2 * pad_t, 0), (0, 0), (0, 0), (0, 0)])
y = mx.conv3d(
x_cl,
_conv3d_weight(weight),
stride=(stride_t, stride_h, stride_w),
padding=(0, pad_h, pad_w),
)
if bias is not None:
y = y + bias.reshape(1, 1, 1, 1, -1)
return y.transpose(0, 4, 1, 2, 3)
def vae_rms_norm(x: Any, gamma: Any) -> Any:
import mlx.core as mx
x32 = x.astype(mx.float32)
denom = mx.sqrt(mx.sum(mx.square(x32), axis=1, keepdims=True))
denom = mx.maximum(denom, mx.array(1e-12, dtype=mx.float32))
scale = math.sqrt(x.shape[1])
return x32 / denom * scale * gamma.astype(mx.float32)
def silu(x: Any) -> Any:
import mlx.core as mx
return x * mx.sigmoid(x)
def vae_attention(q: Any, k: Any, v: Any) -> Any:
import mlx.core as mx
b, c, h, w = q.shape
tokens = h * w
q_tokens = q.reshape(b, c, tokens).transpose(0, 2, 1)
k_tokens = k.reshape(b, c, tokens)
v_tokens = v.reshape(b, c, tokens).transpose(0, 2, 1)
scores = (q_tokens @ k_tokens) * (c ** -0.5)
probs = mx.softmax(scores, axis=-1)
out = probs @ v_tokens
return out.transpose(0, 2, 1).reshape(b, c, h, w)
def _resolve_vae_path(path: str | Path) -> Path:
resolved = Path(path)
if resolved.is_dir():
return resolved / "vae.safetensors"
return resolved
|