Refactor and remove Wan2.1/2.2 model files; update README.md to include new model features and usage instructions for LTX-2 and Wan2 models.
This commit is contained in:
Prince Canuma
589
mlx_video/models/wan2/vae.py
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589
mlx_video/models/wan2/vae.py
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"""3D VAE Decoder for Wan2.1/2.2 (compression 4×8×8).
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Module structure mirrors original PyTorch checkpoint key hierarchy
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so weights load directly without key sanitization.
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"""
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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CACHE_T = 2
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# Per-channel normalization statistics for z_dim=16
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VAE_MEAN = [
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-0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
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0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921,
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]
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VAE_STD = [
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2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
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3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160,
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]
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class CausalConv3d(nn.Module):
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"""3D convolution with causal temporal padding."""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: int | tuple,
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stride: int | tuple = 1,
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padding: int | tuple = 0,
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):
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super().__init__()
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if isinstance(kernel_size, int):
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kernel_size = (kernel_size, kernel_size, kernel_size)
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if isinstance(stride, int):
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stride = (stride, stride, stride)
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if isinstance(padding, int):
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padding = (padding, padding, padding)
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self.kernel_size = kernel_size
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self.stride = stride
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# Causal padding: match reference formula dilation*(k-1) + (1-stride)
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# With dilation=1: k-stride (pads left only, no future context)
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self._causal_pad_t = kernel_size[0] - stride[0]
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self._pad_h = padding[1]
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self._pad_w = padding[2]
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# MLX Conv3d: weight shape [O, D, H, W, I]
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self.weight = mx.zeros((out_channels, kernel_size[0], kernel_size[1], kernel_size[2], in_channels))
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self.bias = mx.zeros((out_channels,))
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def __call__(self, x: mx.array, cache_x: mx.array = None) -> mx.array:
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"""x: [B, C, T, H, W] (channel-first)"""
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b, c, t, h, w = x.shape
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causal_pad = self._causal_pad_t
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if cache_x is not None and causal_pad > 0:
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x = mx.concatenate([cache_x, x], axis=2)
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causal_pad = max(0, causal_pad - cache_x.shape[2])
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if causal_pad > 0:
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pad_t = mx.zeros((b, c, causal_pad, h, w), dtype=x.dtype)
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x = mx.concatenate([pad_t, x], axis=2)
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if self._pad_h > 0 or self._pad_w > 0:
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x = mx.pad(x, [(0, 0), (0, 0), (0, 0),
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(self._pad_h, self._pad_h), (self._pad_w, self._pad_w)])
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x = x.transpose(0, 2, 3, 4, 1) # [B, T, H, W, C]
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out = self._conv3d(x)
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return out.transpose(0, 4, 1, 2, 3) # [B, O, T', H', W']
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def _conv3d(self, x: mx.array) -> mx.array:
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"""3D conv via sliding window + 2D conv per time step.
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x: [B, T, H, W, C_in] -> [B, T_out, H_out, W_out, C_out]
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"""
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b, t, h, w, c_in = x.shape
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kt, kh, kw = self.kernel_size
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st, sh, sw = self.stride
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t_out = (t - kt) // st + 1
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# Pre-reshape weight: [O, D, H, W, I] -> [O, H, W, D*I]
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w_2d = self.weight.transpose(0, 2, 3, 1, 4).reshape(
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self.weight.shape[0], kh, kw, kt * c_in
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)
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outputs = []
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for t_i in range(t_out):
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t_start = t_i * st
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window = x[:, t_start : t_start + kt]
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window = window.transpose(0, 2, 3, 1, 4).reshape(b, h, w, kt * c_in)
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out_2d = mx.conv2d(window, w_2d, stride=(sh, sw)) + self.bias
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outputs.append(out_2d)
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return mx.stack(outputs, axis=1)
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class RMS_norm(nn.Module):
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"""Channel-first L2 normalization matching original Wan VAE.
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Uses F.normalize (L2 norm) with learned scale, equivalent to RMS norm.
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images=True: gamma shape (dim, 1, 1) for 4D (per-frame) input.
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images=False: gamma shape (dim, 1, 1, 1) for 5D video input.
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"""
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def __init__(self, dim: int, channel_first: bool = True, images: bool = True):
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super().__init__()
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self.channel_first = channel_first
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self.scale = dim**0.5
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if channel_first:
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broadcastable = (1, 1) if images else (1, 1, 1)
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self.gamma = mx.ones((dim, *broadcastable))
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else:
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self.gamma = mx.ones((dim,))
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def __call__(self, x: mx.array) -> mx.array:
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norm_dim = 1 if self.channel_first else -1
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# L2 normalize along channel dim (matches F.normalize)
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norm = mx.sqrt(mx.clip(mx.sum(x * x, axis=norm_dim, keepdims=True), a_min=1e-12, a_max=None))
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return (x / norm) * self.scale * self.gamma
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class ResidualBlock(nn.Module):
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"""Residual block with causal 3D convolutions.
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Uses `residual` list with None gaps to match original PyTorch
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nn.Sequential indices: [0]=norm, [1]=SiLU, [2]=conv, [3]=norm,
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[4]=SiLU, [5]=Dropout, [6]=conv. Only indices 0,2,3,6 have params.
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"""
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def __init__(self, in_dim: int, out_dim: int):
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super().__init__()
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self.residual = [
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RMS_norm(in_dim, images=False), # [0]
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None, # [1] SiLU
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CausalConv3d(in_dim, out_dim, 3, padding=1), # [2]
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RMS_norm(out_dim, images=False), # [3]
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None, # [4] SiLU
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None, # [5] Dropout
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CausalConv3d(out_dim, out_dim, 3, padding=1), # [6]
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]
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self.shortcut = CausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else None
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def __call__(self, x: mx.array, feat_cache=None, feat_idx=None) -> mx.array:
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h = x if self.shortcut is None else self.shortcut(x)
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if feat_cache is not None:
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# First conv: norm -> silu -> [cache] -> conv
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x = nn.silu(self.residual[0](x))
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idx = feat_idx[0]
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cache_x = x[:, :, -CACHE_T:]
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if cache_x.shape[2] < CACHE_T and feat_cache[idx] is not None:
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cache_x = mx.concatenate([feat_cache[idx][:, :, -1:], cache_x], axis=2)
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x = self.residual[2](x, cache_x=feat_cache[idx])
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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# Second conv: norm -> silu -> [cache] -> conv
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x = nn.silu(self.residual[3](x))
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idx = feat_idx[0]
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cache_x = x[:, :, -CACHE_T:]
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if cache_x.shape[2] < CACHE_T and feat_cache[idx] is not None:
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cache_x = mx.concatenate([feat_cache[idx][:, :, -1:], cache_x], axis=2)
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x = self.residual[6](x, cache_x=feat_cache[idx])
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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else:
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x = nn.silu(self.residual[0](x))
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x = self.residual[2](x)
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x = nn.silu(self.residual[3](x))
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x = self.residual[6](x)
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return x + h
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class AttentionBlock(nn.Module):
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"""Single-head spatial self-attention."""
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def __init__(self, dim: int):
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super().__init__()
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self.norm = RMS_norm(dim, images=True)
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self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
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self.proj = nn.Conv2d(dim, dim, 1)
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def __call__(self, x: mx.array) -> mx.array:
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"""x: [B, C, T, H, W]"""
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identity = x
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b, c, t, h, w = x.shape
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# [B,C,T,H,W] -> [B,T,C,H,W] -> [BT,C,H,W] -> norm -> [BT,H,W,C]
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x = x.transpose(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
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x = self.norm(x)
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x = x.transpose(0, 2, 3, 1) # [BT, H, W, C]
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qkv = self.to_qkv(x) # [BT, H, W, 3C]
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qkv = qkv.reshape(b * t, h * w, 3, c).transpose(2, 0, 1, 3)
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q, k, v = qkv[0], qkv[1], qkv[2]
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q = q[:, None, :, :] # [BT, 1, HW, C]
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k = k[:, None, :, :]
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v = v[:, None, :, :]
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out = mx.fast.scaled_dot_product_attention(q, k, v, scale=c**-0.5)
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out = out.squeeze(1).reshape(b * t, h, w, c) # [BT, H, W, C]
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out = self.proj(out) # [BT, H, W, C]
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out = out.reshape(b, t, h, w, c).transpose(0, 4, 1, 2, 3) # [B, C, T, H, W]
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return out + identity
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class Resample(nn.Module):
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"""Resample block matching original Wan VAE structure.
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Supports both upsampling (decoder) and downsampling (encoder).
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Uses list-based param storage to match original nn.Sequential key hierarchy.
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"""
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def __init__(self, dim: int, mode: str):
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super().__init__()
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assert mode in ("upsample2d", "upsample3d", "downsample2d", "downsample3d")
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self.mode = mode
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self.dim = dim
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if mode.startswith("upsample"):
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# resample.0 = Upsample (no params), resample.1 = Conv2d
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self.resample = [None, nn.Conv2d(dim, dim // 2, 3, padding=1)]
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if mode == "upsample3d":
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self.time_conv = CausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
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else:
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# resample.0 = ZeroPad2d (no params), resample.1 = Conv2d(stride=2)
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self.resample = [None, nn.Conv2d(dim, dim, 3, stride=2)]
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if mode == "downsample3d":
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self.time_conv = CausalConv3d(
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dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
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def __call__(self, x: mx.array, feat_cache=None, feat_idx=None) -> mx.array:
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"""x: [B, C, T, H, W]"""
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b, c, t, h, w = x.shape
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if self.mode == "upsample3d":
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# Temporal upsample via learned conv
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x_t = self.time_conv(x) # [B, 2C, T, H, W]
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x_t = x_t.reshape(b, 2, c, t, h, w)
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x = mx.stack([x_t[:, 0], x_t[:, 1]], axis=3).reshape(b, c, t * 2, h, w)
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t = t * 2
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if self.mode.startswith("upsample"):
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# Per-frame spatial upsample: nearest 2x + Conv2d
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x = x.transpose(0, 2, 3, 4, 1).reshape(b * t, h, w, c) # [BT, H, W, C]
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x = mx.repeat(x, 2, axis=1)
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x = mx.repeat(x, 2, axis=2)
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x = self.resample[1](x) # Conv2d [BT, 2H, 2W, C//2]
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c_out = x.shape[-1]
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return x.reshape(b, t, h * 2, w * 2, c_out).transpose(0, 4, 1, 2, 3)
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else:
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# Per-frame spatial downsample: ZeroPad(0,1,0,1) + Conv2d(stride=2)
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x = x.transpose(0, 2, 3, 4, 1).reshape(b * t, h, w, c) # [BT, H, W, C]
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x = mx.pad(x, [(0, 0), (0, 1), (0, 1), (0, 0)]) # ZeroPad2d(0,1,0,1)
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x = self.resample[1](x) # Conv2d stride=2
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c_out = x.shape[-1]
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h_out, w_out = x.shape[1], x.shape[2]
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x = x.reshape(b, t, h_out, w_out, c_out).transpose(0, 4, 1, 2, 3)
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if self.mode == "downsample3d":
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if feat_cache is not None:
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idx = feat_idx[0]
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if feat_cache[idx] is None:
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# First chunk: save x, skip time_conv
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feat_cache[idx] = x
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feat_idx[0] += 1
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else:
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# Subsequent chunks: use cached frame as temporal context
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cache_x = x[:, :, -1:]
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x = self.time_conv(
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x, cache_x=feat_cache[idx][:, :, -1:])
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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else:
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x = self.time_conv(x)
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return x
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class Decoder3d(nn.Module):
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"""3D VAE Decoder matching Wan2.1 architecture.
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Uses flat `middle` and `upsamples` lists to match original
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PyTorch nn.Sequential weight key hierarchy.
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"""
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def __init__(
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self,
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dim: int = 96,
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z_dim: int = 16,
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dim_mult: list = None,
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num_res_blocks: int = 2,
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temporal_upsample: list = None,
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):
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super().__init__()
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if dim_mult is None:
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dim_mult = [1, 2, 4, 4]
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if temporal_upsample is None:
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temporal_upsample = [True, True, False]
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dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
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self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
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# Middle: [ResBlock, AttentionBlock, ResBlock]
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self.middle = [
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ResidualBlock(dims[0], dims[0]),
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AttentionBlock(dims[0]),
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ResidualBlock(dims[0], dims[0]),
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]
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# Flat upsample list matching original nn.Sequential indexing
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upsamples = []
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for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
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if i in (1, 2, 3):
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in_dim = in_dim // 2
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for _ in range(num_res_blocks + 1):
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upsamples.append(ResidualBlock(in_dim, out_dim))
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in_dim = out_dim
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if i != len(dim_mult) - 1:
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mode = "upsample3d" if temporal_upsample[i] else "upsample2d"
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upsamples.append(Resample(out_dim, mode=mode))
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self.upsamples = upsamples
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# Output head: [RMS_norm, SiLU (no params), CausalConv3d]
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self.head = [
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RMS_norm(dims[-1], images=False), # [0]
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None, # [1] SiLU
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CausalConv3d(dims[-1], 3, 3, padding=1), # [2]
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]
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def __call__(self, x: mx.array) -> mx.array:
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"""x: [B, z_dim, T, H, W] -> [B, 3, T_out, H_out, W_out]"""
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x = self.conv1(x)
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for layer in self.middle:
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x = layer(x)
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for layer in self.upsamples:
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x = layer(x)
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x = nn.silu(self.head[0](x))
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x = self.head[2](x)
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return x
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class Encoder3d(nn.Module):
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"""3D VAE Encoder matching Wan2.1 architecture.
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Mirror of Decoder3d with downsampling instead of upsampling.
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Uses flat lists to match original PyTorch nn.Sequential weight key hierarchy.
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"""
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def __init__(
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self,
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dim: int = 96,
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z_dim: int = 16,
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dim_mult: list = None,
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num_res_blocks: int = 2,
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temporal_downsample: list = None,
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):
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super().__init__()
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if dim_mult is None:
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dim_mult = [1, 2, 4, 4]
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if temporal_downsample is None:
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temporal_downsample = [False, True, True]
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dims = [dim * u for u in [1] + dim_mult]
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self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)
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# Flat downsample list matching original nn.Sequential indexing
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downsamples = []
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for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
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for _ in range(num_res_blocks):
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downsamples.append(ResidualBlock(in_dim, out_dim))
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in_dim = out_dim
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if i != len(dim_mult) - 1:
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mode = "downsample3d" if temporal_downsample[i] else "downsample2d"
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downsamples.append(Resample(out_dim, mode=mode))
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self.downsamples = downsamples
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# Middle: [ResBlock, AttentionBlock, ResBlock]
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self.middle = [
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ResidualBlock(dims[-1], dims[-1]),
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AttentionBlock(dims[-1]),
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ResidualBlock(dims[-1], dims[-1]),
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]
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|
||||
# Output head: [RMS_norm, SiLU (no params), CausalConv3d]
|
||||
self.head = [
|
||||
RMS_norm(dims[-1], images=False),
|
||||
None, # SiLU
|
||||
CausalConv3d(dims[-1], z_dim, 3, padding=1),
|
||||
]
|
||||
|
||||
def __call__(self, x: mx.array, feat_cache=None, feat_idx=None) -> mx.array:
|
||||
"""x: [B, 3, T, H, W] -> [B, z_dim, T_lat, H_lat, W_lat]"""
|
||||
if feat_cache is not None:
|
||||
# conv1 with caching
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:]
|
||||
if cache_x.shape[2] < CACHE_T and feat_cache[idx] is not None:
|
||||
cache_x = mx.concatenate(
|
||||
[feat_cache[idx][:, :, -1:], cache_x], axis=2)
|
||||
x = self.conv1(x, cache_x=feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = self.conv1(x)
|
||||
|
||||
for layer in self.downsamples:
|
||||
if feat_cache is not None and isinstance(layer, (ResidualBlock, Resample)):
|
||||
x = layer(x, feat_cache=feat_cache, feat_idx=feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
for layer in self.middle:
|
||||
if feat_cache is not None and isinstance(layer, ResidualBlock):
|
||||
x = layer(x, feat_cache=feat_cache, feat_idx=feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
if feat_cache is not None:
|
||||
# Head: norm -> silu -> [cache] -> conv
|
||||
x = nn.silu(self.head[0](x))
|
||||
idx = feat_idx[0]
|
||||
cache_x = x[:, :, -CACHE_T:]
|
||||
if cache_x.shape[2] < CACHE_T and feat_cache[idx] is not None:
|
||||
cache_x = mx.concatenate(
|
||||
[feat_cache[idx][:, :, -1:], cache_x], axis=2)
|
||||
x = self.head[2](x, cache_x=feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = nn.silu(self.head[0](x))
|
||||
x = self.head[2](x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class WanVAE(nn.Module):
|
||||
"""Wan2.1 VAE wrapper with per-channel normalization.
|
||||
|
||||
Supports both encode (for I2V) and decode (for all models).
|
||||
"""
|
||||
|
||||
def __init__(self, z_dim: int = 16, encoder: bool = False):
|
||||
super().__init__()
|
||||
self.z_dim = z_dim
|
||||
self.mean = mx.array(VAE_MEAN)
|
||||
self.std = mx.array(VAE_STD)
|
||||
self.inv_std = 1.0 / self.std
|
||||
|
||||
self.conv2 = CausalConv3d(z_dim, z_dim, 1)
|
||||
self.decoder = Decoder3d(dim=96, z_dim=z_dim)
|
||||
|
||||
if encoder:
|
||||
self.encoder = Encoder3d(dim=96, z_dim=z_dim * 2)
|
||||
self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
|
||||
|
||||
def encode(self, x: mx.array) -> mx.array:
|
||||
"""Encode video to normalized latent using chunked encoding.
|
||||
|
||||
Uses chunked encoding with temporal caching to match reference behavior.
|
||||
First frame encoded alone, then 4-frame chunks with cached context.
|
||||
|
||||
Args:
|
||||
x: Video [B, 3, T, H, W] in [-1, 1]
|
||||
|
||||
Returns:
|
||||
Normalized latent [B, z_dim, T_lat, H_lat, W_lat]
|
||||
"""
|
||||
# Count cacheable CausalConv3d slots in encoder
|
||||
num_slots = self._count_encoder_cache_slots()
|
||||
feat_cache = [None] * num_slots
|
||||
|
||||
t = x.shape[2]
|
||||
num_chunks = 1 + (t - 1) // 4
|
||||
|
||||
out = None
|
||||
for i in range(num_chunks):
|
||||
feat_idx = [0]
|
||||
if i == 0:
|
||||
chunk = x[:, :, :1]
|
||||
else:
|
||||
chunk = x[:, :, 1 + 4 * (i - 1) : 1 + 4 * i]
|
||||
|
||||
chunk_out = self.encoder(chunk, feat_cache=feat_cache, feat_idx=feat_idx)
|
||||
|
||||
if out is None:
|
||||
out = chunk_out
|
||||
else:
|
||||
out = mx.concatenate([out, chunk_out], axis=2)
|
||||
|
||||
mu, _ = mx.split(self.conv1(out), 2, axis=1)
|
||||
|
||||
# Normalize: (mu - mean) * inv_std
|
||||
mean = self.mean.reshape(1, -1, 1, 1, 1)
|
||||
inv_std = self.inv_std.reshape(1, -1, 1, 1, 1)
|
||||
return (mu - mean) * inv_std
|
||||
|
||||
def _count_encoder_cache_slots(self) -> int:
|
||||
"""Count CausalConv3d that participate in chunked encoding cache."""
|
||||
count = 1 # encoder.conv1
|
||||
for layer in self.encoder.downsamples:
|
||||
if isinstance(layer, ResidualBlock):
|
||||
count += 2 # two convs in residual path
|
||||
elif isinstance(layer, Resample) and layer.mode == "downsample3d":
|
||||
count += 1 # time_conv
|
||||
for layer in self.encoder.middle:
|
||||
if isinstance(layer, ResidualBlock):
|
||||
count += 2
|
||||
count += 1 # encoder.head CausalConv3d
|
||||
return count
|
||||
|
||||
def decode(self, z: mx.array) -> mx.array:
|
||||
"""Decode latent to video.
|
||||
|
||||
Args:
|
||||
z: Normalized latent [B, z_dim, T, H, W]
|
||||
|
||||
Returns:
|
||||
Video [B, 3, T_out, H_out, W_out] clamped to [-1, 1]
|
||||
"""
|
||||
mean = self.mean.reshape(1, -1, 1, 1, 1)
|
||||
inv_std = self.inv_std.reshape(1, -1, 1, 1, 1)
|
||||
z = z / inv_std + mean
|
||||
|
||||
x = self.conv2(z)
|
||||
out = self.decoder(x)
|
||||
return mx.clip(out, -1, 1)
|
||||
|
||||
def decode_tiled(self, z: mx.array, tiling_config=None) -> mx.array:
|
||||
"""Decode latent to video using tiling to reduce memory usage.
|
||||
|
||||
Splits the latent tensor into overlapping spatial/temporal tiles,
|
||||
decodes each tile independently, and blends them with trapezoidal
|
||||
masks. Reuses the LTX-2 tiling infrastructure.
|
||||
|
||||
Args:
|
||||
z: Normalized latent [B, z_dim, T, H, W]
|
||||
tiling_config: Optional TilingConfig. If None, uses default.
|
||||
|
||||
Returns:
|
||||
Video [B, 3, T_out, H_out, W_out] clamped to [-1, 1]
|
||||
"""
|
||||
from mlx_video.models.wan.tiling import TilingConfig, decode_with_tiling
|
||||
|
||||
if tiling_config is None:
|
||||
tiling_config = TilingConfig.default()
|
||||
|
||||
# Check if tiling is actually needed
|
||||
_, _, f, h, w = z.shape
|
||||
needs_tiling = False
|
||||
if tiling_config.spatial_config is not None:
|
||||
s_tile = tiling_config.spatial_config.tile_size_in_pixels // 8
|
||||
if h > s_tile or w > s_tile:
|
||||
needs_tiling = True
|
||||
if tiling_config.temporal_config is not None:
|
||||
t_tile = tiling_config.temporal_config.tile_size_in_frames // 4
|
||||
if f > t_tile:
|
||||
needs_tiling = True
|
||||
|
||||
if not needs_tiling:
|
||||
return self.decode(z)
|
||||
|
||||
# Denormalize once (small tensor), then tile the denormalized latents
|
||||
mean = self.mean.reshape(1, -1, 1, 1, 1)
|
||||
inv_std = self.inv_std.reshape(1, -1, 1, 1, 1)
|
||||
z_denorm = z / inv_std + mean
|
||||
|
||||
def tile_decode(tile_latents, **kwargs):
|
||||
x = self.conv2(tile_latents)
|
||||
out = self.decoder(x)
|
||||
return mx.clip(out, -1, 1)
|
||||
|
||||
return decode_with_tiling(
|
||||
decoder_fn=tile_decode,
|
||||
latents=z_denorm,
|
||||
tiling_config=tiling_config,
|
||||
spatial_scale=8, # 3× spatial 2× upsamples = 8×
|
||||
temporal_scale=4, # 2× temporal upsamples × 2 = 4×
|
||||
causal_temporal=False, # Wan2.1 uses non-causal temporal (T → 4T)
|
||||
)
|
||||
Reference in New Issue
Block a user