mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-08-21 20:46:50 +08:00
703 lines
23 KiB
Python
703 lines
23 KiB
Python
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# MLX implementation of vae2_1.py
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import logging
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from typing import Optional, List, Tuple
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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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from mlx.utils import tree_unflatten
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__all__ = [
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'Wan2_1_VAE',
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]
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CACHE_T = 2
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debug_line = 0
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class CausalConv3d(nn.Conv3d):
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"""
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Causal 3d convolution for MLX.
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Expects input in BTHWC format (batch, time, height, width, channels).
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# Padding order: (W, W, H, H, T, 0)
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self._padding = (self.padding[2], self.padding[2], self.padding[1],
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self.padding[1], 2 * self.padding[0], 0)
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self.padding = (0, 0, 0)
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def __call__(self, x, cache_x=None):
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padding = list(self._padding)
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if cache_x is not None and self._padding[4] > 0:
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x = mx.concatenate([cache_x, x], axis=1) # Concat along time axis
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padding[4] -= cache_x.shape[1]
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# Pad in BTHWC format
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pad_width = [(0, 0), (padding[4], padding[5]), (padding[2], padding[3]),
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(padding[0], padding[1]), (0, 0)]
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x = mx.pad(x, pad_width)
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result = super().__call__(x)
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return result
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class RMS_norm(nn.Module):
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def __init__(self, dim, channel_first=False, images=True, bias=False):
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super().__init__()
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self.channel_first = channel_first
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self.images = images
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self.scale = dim**0.5
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# Just keep as 1D - let broadcasting do its magic
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self.gamma = mx.ones((dim,))
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self.bias = mx.zeros((dim,)) if bias else 0.
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def __call__(self, x):
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norm = mx.sqrt(mx.sum(x * x, axis=-1, keepdims=True) + 1e-6)
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x = x / norm
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return x * self.scale * self.gamma + self.bias
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class Upsample(nn.Module):
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"""
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Upsampling layer that matches PyTorch's behavior.
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"""
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def __init__(self, scale_factor, mode='nearest-exact'):
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super().__init__()
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self.scale_factor = scale_factor
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self.mode = mode # mode is now unused, but kept for signature consistency
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def __call__(self, x):
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scale_h, scale_w = self.scale_factor
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out = mx.repeat(x, int(scale_h), axis=1) # Repeat along H dimension
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out = mx.repeat(out, int(scale_w), axis=2) # Repeat along W dimension
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return out
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class AsymmetricPad(nn.Module):
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"""A module to apply asymmetric padding, compatible with nn.Sequential."""
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def __init__(self, pad_width: tuple):
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super().__init__()
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self.pad_width = pad_width
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def __call__(self, x):
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return mx.pad(x, self.pad_width)
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# Update your Resample class to use 'nearest-exact'
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class Resample(nn.Module):
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def __init__(self, dim, mode):
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assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d',
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'downsample3d')
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super().__init__()
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self.dim = dim
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self.mode = mode
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# layers
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if mode == 'upsample2d':
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self.resample = nn.Sequential(
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Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
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nn.Conv2d(dim, dim // 2, 3, padding=1)
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)
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elif mode == 'upsample3d':
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self.resample = nn.Sequential(
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Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
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nn.Conv2d(dim, dim // 2, 3, padding=1)
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)
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self.time_conv = CausalConv3d(
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dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
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# --- CORRECTED PADDING LOGIC ---
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elif mode == 'downsample2d':
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pad_layer = AsymmetricPad(pad_width=((0, 0), (0, 1), (0, 1), (0, 0)))
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conv_layer = nn.Conv2d(dim, dim, 3, stride=(2, 2), padding=0)
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self.resample = nn.Sequential(pad_layer, conv_layer)
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elif mode == 'downsample3d':
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# The spatial downsampling part uses the same logic
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pad_layer = AsymmetricPad(pad_width=((0, 0), (0, 1), (0, 1), (0, 0)))
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conv_layer = nn.Conv2d(dim, dim, 3, stride=(2, 2), padding=0)
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self.resample = nn.Sequential(pad_layer, conv_layer)
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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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else:
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self.resample = nn.Identity()
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def __call__(self, x, feat_cache=None, feat_idx=[0]):
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# The __call__ method logic remains unchanged from your original code
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b, t, h, w, c = x.shape
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if self.mode == 'upsample3d':
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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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feat_cache[idx] = 'Rep'
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feat_idx[0] += 1
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else:
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cache_x = x[:, -CACHE_T:, :, :, :]
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if cache_x.shape[1] < 2 and feat_cache[idx] is not None and feat_cache[idx] != 'Rep':
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cache_x = mx.concatenate([
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feat_cache[idx][:, -1:, :, :, :], cache_x
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], axis=1)
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if cache_x.shape[1] < 2 and feat_cache[idx] is not None and feat_cache[idx] == 'Rep':
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cache_x = mx.concatenate([
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mx.zeros_like(cache_x), cache_x
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], axis=1)
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if feat_cache[idx] == 'Rep':
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x = self.time_conv(x)
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else:
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x = self.time_conv(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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x = x.reshape(b, t, h, w, 2, c)
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x = mx.stack([x[:, :, :, :, 0, :], x[:, :, :, :, 1, :]], axis=2)
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x = x.reshape(b, t * 2, h, w, c)
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t = x.shape[1]
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x = x.reshape(b * t, h, w, c)
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x = self.resample(x)
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_, h_new, w_new, c_new = x.shape
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x = x.reshape(b, t, h_new, w_new, c_new)
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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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feat_cache[idx] = x
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feat_idx[0] += 1
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else:
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cache_x = x[:, -1:, :, :, :]
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x = self.time_conv(
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mx.concatenate([feat_cache[idx][:, -1:, :, :, :], x], axis=1))
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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return x
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class ResidualBlock(nn.Module):
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def __init__(self, in_dim, out_dim, dropout=0.0):
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super().__init__()
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self.in_dim = in_dim
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self.out_dim = out_dim
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# layers
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self.residual = nn.Sequential(
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RMS_norm(in_dim, images=False),
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nn.SiLU(),
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CausalConv3d(in_dim, out_dim, 3, padding=1),
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RMS_norm(out_dim, images=False),
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nn.SiLU(),
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nn.Dropout(dropout) if dropout > 0 else nn.Identity(),
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CausalConv3d(out_dim, out_dim, 3, padding=1)
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)
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self.shortcut = CausalConv3d(in_dim, out_dim, 1) \
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if in_dim != out_dim else nn.Identity()
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def __call__(self, x, feat_cache=None, feat_idx=[0]):
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h = self.shortcut(x)
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for i, layer in enumerate(self.residual.layers):
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if isinstance(layer, CausalConv3d) and feat_cache is not None:
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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[1] < 2 and feat_cache[idx] is not None:
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cache_x = mx.concatenate([
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feat_cache[idx][:, -1:, :, :, :], cache_x
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], axis=1)
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x = layer(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 = layer(x)
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return x + h
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class AttentionBlock(nn.Module):
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"""
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Causal self-attention with a single head.
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"""
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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# layers
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self.norm = RMS_norm(dim)
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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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# zero out the last layer params
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self.proj.weight = mx.zeros_like(self.proj.weight)
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def __call__(self, x):
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# x is in BTHWC format
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identity = x
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b, t, h, w, c = x.shape
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x = x.reshape(b * t, h, w, c) # Combine batch and time
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x = self.norm(x)
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# compute query, key, value
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qkv = self.to_qkv(x) # Output: (b*t, h, w, 3*c)
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qkv = qkv.reshape(b * t, h * w, 3 * c)
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q, k, v = mx.split(qkv, 3, axis=-1)
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# Reshape for attention
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q = q.reshape(b * t, h * w, c)
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k = k.reshape(b * t, h * w, c)
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v = v.reshape(b * t, h * w, c)
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# Scaled dot product attention
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scale = 1.0 / mx.sqrt(mx.array(c, dtype=q.dtype))
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scores = (q @ k.transpose(0, 2, 1)) * scale
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weights = mx.softmax(scores, axis=-1)
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x = weights @ v
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x = x.reshape(b * t, h, w, c)
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# output
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x = self.proj(x)
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x = x.reshape(b, t, h, w, c)
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return x + identity
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class Encoder3d(nn.Module):
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def __init__(self,
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dim=128,
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z_dim=4,
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dim_mult=[1, 2, 4, 4],
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num_res_blocks=2,
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attn_scales=[],
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temperal_downsample=[True, True, False],
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dropout=0.0):
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super().__init__()
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self.dim = dim
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self.z_dim = z_dim
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self.dim_mult = dim_mult
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self.num_res_blocks = num_res_blocks
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self.attn_scales = attn_scales
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self.temperal_downsample = temperal_downsample
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# dimensions
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dims = [dim * u for u in [1] + dim_mult]
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scale = 1.0
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# init block
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self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)
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# downsample blocks
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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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# residual (+attention) blocks
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for _ in range(num_res_blocks):
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downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
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if scale in attn_scales:
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downsamples.append(AttentionBlock(out_dim))
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in_dim = out_dim
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# downsample block
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if i != len(dim_mult) - 1:
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mode = 'downsample3d' if temperal_downsample[i] else 'downsample2d'
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downsamples.append(Resample(out_dim, mode=mode))
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scale /= 2.0
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self.downsamples = nn.Sequential(*downsamples)
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# middle blocks
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self.middle = nn.Sequential(
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ResidualBlock(dims[-1], dims[-1], dropout),
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AttentionBlock(dims[-1]),
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ResidualBlock(dims[-1], dims[-1], dropout)
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)
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# output blocks
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self.head = nn.Sequential(
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RMS_norm(dims[-1], images=False),
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nn.SiLU(),
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CausalConv3d(dims[-1], z_dim, 3, padding=1)
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)
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def __call__(self, x, feat_cache=None, feat_idx=[0]):
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if feat_cache is not None:
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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[1] < 2 and feat_cache[idx] is not None:
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cache_x = mx.concatenate([
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feat_cache[idx][:, -1:, :, :, :], cache_x
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], axis=1)
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x = self.conv1(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 = self.conv1(x)
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## downsamples
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for i, layer in enumerate(self.downsamples.layers):
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if feat_cache is not None:
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x = layer(x, feat_cache, feat_idx)
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else:
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x = layer(x)
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## middle
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for layer in self.middle.layers:
|
||
|
|
if isinstance(layer, ResidualBlock) and feat_cache is not None:
|
||
|
|
x = layer(x, feat_cache, feat_idx)
|
||
|
|
else:
|
||
|
|
x = layer(x)
|
||
|
|
|
||
|
|
## head
|
||
|
|
for i, layer in enumerate(self.head.layers):
|
||
|
|
if isinstance(layer, CausalConv3d) and feat_cache is not None:
|
||
|
|
idx = feat_idx[0]
|
||
|
|
cache_x = x[:, -CACHE_T:, :, :, :]
|
||
|
|
if cache_x.shape[1] < 2 and feat_cache[idx] is not None:
|
||
|
|
cache_x = mx.concatenate([
|
||
|
|
feat_cache[idx][:, -1:, :, :, :], cache_x
|
||
|
|
], axis=1)
|
||
|
|
x = layer(x, feat_cache[idx])
|
||
|
|
feat_cache[idx] = cache_x
|
||
|
|
feat_idx[0] += 1
|
||
|
|
else:
|
||
|
|
x = layer(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class Decoder3d(nn.Module):
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
dim=128,
|
||
|
|
z_dim=4,
|
||
|
|
dim_mult=[1, 2, 4, 4],
|
||
|
|
num_res_blocks=2,
|
||
|
|
attn_scales=[],
|
||
|
|
temperal_upsample=[False, True, True],
|
||
|
|
dropout=0.0):
|
||
|
|
super().__init__()
|
||
|
|
self.dim = dim
|
||
|
|
self.z_dim = z_dim
|
||
|
|
self.dim_mult = dim_mult
|
||
|
|
self.num_res_blocks = num_res_blocks
|
||
|
|
self.attn_scales = attn_scales
|
||
|
|
self.temperal_upsample = temperal_upsample
|
||
|
|
|
||
|
|
# dimensions
|
||
|
|
dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
|
||
|
|
scale = 1.0 / 2**(len(dim_mult) - 2)
|
||
|
|
|
||
|
|
# init block
|
||
|
|
self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
|
||
|
|
|
||
|
|
# middle blocks
|
||
|
|
self.middle = nn.Sequential(
|
||
|
|
ResidualBlock(dims[0], dims[0], dropout),
|
||
|
|
AttentionBlock(dims[0]),
|
||
|
|
ResidualBlock(dims[0], dims[0], dropout)
|
||
|
|
)
|
||
|
|
|
||
|
|
# upsample blocks
|
||
|
|
upsamples = []
|
||
|
|
for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
|
||
|
|
# residual (+attention) blocks
|
||
|
|
if i == 1 or i == 2 or i == 3:
|
||
|
|
in_dim = in_dim // 2
|
||
|
|
for _ in range(num_res_blocks + 1):
|
||
|
|
upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
|
||
|
|
if scale in attn_scales:
|
||
|
|
upsamples.append(AttentionBlock(out_dim))
|
||
|
|
in_dim = out_dim
|
||
|
|
|
||
|
|
# upsample block
|
||
|
|
if i != len(dim_mult) - 1:
|
||
|
|
mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d'
|
||
|
|
upsamples.append(Resample(out_dim, mode=mode))
|
||
|
|
scale *= 2.0
|
||
|
|
|
||
|
|
self.upsamples = nn.Sequential(*upsamples)
|
||
|
|
|
||
|
|
# output blocks
|
||
|
|
self.head = nn.Sequential(
|
||
|
|
RMS_norm(dims[-1], images=False),
|
||
|
|
nn.SiLU(),
|
||
|
|
CausalConv3d(dims[-1], 3, 3, padding=1)
|
||
|
|
)
|
||
|
|
|
||
|
|
def __call__(self, x, feat_cache=None, feat_idx=[0]):
|
||
|
|
## conv1
|
||
|
|
if feat_cache is not None:
|
||
|
|
idx = feat_idx[0]
|
||
|
|
cache_x = x[:, -CACHE_T:, :, :, :]
|
||
|
|
if cache_x.shape[1] < 2 and feat_cache[idx] is not None:
|
||
|
|
cache_x = mx.concatenate([
|
||
|
|
feat_cache[idx][:, -1:, :, :, :], cache_x
|
||
|
|
], axis=1)
|
||
|
|
x = self.conv1(x, feat_cache[idx])
|
||
|
|
feat_cache[idx] = cache_x
|
||
|
|
feat_idx[0] += 1
|
||
|
|
else:
|
||
|
|
x = self.conv1(x)
|
||
|
|
|
||
|
|
## middle
|
||
|
|
for layer in self.middle.layers:
|
||
|
|
if isinstance(layer, ResidualBlock) and feat_cache is not None:
|
||
|
|
x = layer(x, feat_cache, feat_idx)
|
||
|
|
else:
|
||
|
|
x = layer(x)
|
||
|
|
|
||
|
|
## upsamples
|
||
|
|
for layer in self.upsamples.layers:
|
||
|
|
if feat_cache is not None:
|
||
|
|
x = layer(x, feat_cache, feat_idx)
|
||
|
|
else:
|
||
|
|
x = layer(x)
|
||
|
|
|
||
|
|
## head
|
||
|
|
for i, layer in enumerate(self.head.layers):
|
||
|
|
if isinstance(layer, CausalConv3d) and feat_cache is not None:
|
||
|
|
idx = feat_idx[0]
|
||
|
|
cache_x = x[:, -CACHE_T:, :, :, :]
|
||
|
|
if cache_x.shape[1] < 2 and feat_cache[idx] is not None:
|
||
|
|
cache_x = mx.concatenate([
|
||
|
|
feat_cache[idx][:, -1:, :, :, :], cache_x
|
||
|
|
], axis=1)
|
||
|
|
x = layer(x, feat_cache[idx])
|
||
|
|
feat_cache[idx] = cache_x
|
||
|
|
feat_idx[0] += 1
|
||
|
|
else:
|
||
|
|
x = layer(x)
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
def count_conv3d(model):
|
||
|
|
count = 0
|
||
|
|
for name, module in model.named_modules():
|
||
|
|
if isinstance(module, CausalConv3d):
|
||
|
|
count += 1
|
||
|
|
return count
|
||
|
|
|
||
|
|
|
||
|
|
class WanVAE_(nn.Module):
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
dim=128,
|
||
|
|
z_dim=4,
|
||
|
|
dim_mult=[1, 2, 4, 4],
|
||
|
|
num_res_blocks=2,
|
||
|
|
attn_scales=[],
|
||
|
|
temperal_downsample=[True, True, False],
|
||
|
|
dropout=0.0):
|
||
|
|
super().__init__()
|
||
|
|
self.dim = dim
|
||
|
|
self.z_dim = z_dim
|
||
|
|
self.dim_mult = dim_mult
|
||
|
|
self.num_res_blocks = num_res_blocks
|
||
|
|
self.attn_scales = attn_scales
|
||
|
|
self.temperal_downsample = temperal_downsample
|
||
|
|
self.temperal_upsample = temperal_downsample[::-1]
|
||
|
|
|
||
|
|
# modules
|
||
|
|
self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks,
|
||
|
|
attn_scales, self.temperal_downsample, dropout)
|
||
|
|
self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
|
||
|
|
self.conv2 = CausalConv3d(z_dim, z_dim, 1)
|
||
|
|
self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks,
|
||
|
|
attn_scales, self.temperal_upsample, dropout)
|
||
|
|
|
||
|
|
def encode(self, x, scale):
|
||
|
|
# x is in BTHWC format
|
||
|
|
self.clear_cache()
|
||
|
|
## cache
|
||
|
|
t = x.shape[1]
|
||
|
|
iter_ = 1 + (t - 1) // 4
|
||
|
|
## Split encode input x by time into 1, 4, 4, 4....
|
||
|
|
for i in range(iter_):
|
||
|
|
self._enc_conv_idx = [0]
|
||
|
|
if i == 0:
|
||
|
|
out = self.encoder(
|
||
|
|
x[:, :1, :, :, :],
|
||
|
|
feat_cache=self._enc_feat_map,
|
||
|
|
feat_idx=self._enc_conv_idx)
|
||
|
|
else:
|
||
|
|
out_ = self.encoder(
|
||
|
|
x[:, 1 + 4 * (i - 1):1 + 4 * i, :, :, :],
|
||
|
|
feat_cache=self._enc_feat_map,
|
||
|
|
feat_idx=self._enc_conv_idx)
|
||
|
|
out = mx.concatenate([out, out_], axis=1)
|
||
|
|
|
||
|
|
z = self.conv1(out)
|
||
|
|
mu, log_var = mx.split(z, 2, axis=-1) # Split along channel dimension
|
||
|
|
|
||
|
|
if isinstance(scale[0], mx.array):
|
||
|
|
# Reshape scale for broadcasting in BTHWC format
|
||
|
|
scale_mean = scale[0].reshape(1, 1, 1, 1, self.z_dim)
|
||
|
|
scale_std = scale[1].reshape(1, 1, 1, 1, self.z_dim)
|
||
|
|
mu = (mu - scale_mean) * scale_std
|
||
|
|
else:
|
||
|
|
mu = (mu - scale[0]) * scale[1]
|
||
|
|
self.clear_cache()
|
||
|
|
|
||
|
|
return mu, log_var
|
||
|
|
|
||
|
|
def decode(self, z, scale):
|
||
|
|
# z is in BTHWC format
|
||
|
|
self.clear_cache()
|
||
|
|
if isinstance(scale[0], mx.array):
|
||
|
|
scale_mean = scale[0].reshape(1, 1, 1, 1, self.z_dim)
|
||
|
|
scale_std = scale[1].reshape(1, 1, 1, 1, self.z_dim)
|
||
|
|
z = z / scale_std + scale_mean
|
||
|
|
else:
|
||
|
|
z = z / scale[1] + scale[0]
|
||
|
|
iter_ = z.shape[1]
|
||
|
|
x = self.conv2(z)
|
||
|
|
for i in range(iter_):
|
||
|
|
self._conv_idx = [0]
|
||
|
|
if i == 0:
|
||
|
|
out = self.decoder(
|
||
|
|
x[:, i:i + 1, :, :, :],
|
||
|
|
feat_cache=self._feat_map,
|
||
|
|
feat_idx=self._conv_idx)
|
||
|
|
else:
|
||
|
|
out_ = self.decoder(
|
||
|
|
x[:, i:i + 1, :, :, :],
|
||
|
|
feat_cache=self._feat_map,
|
||
|
|
feat_idx=self._conv_idx)
|
||
|
|
out = mx.concatenate([out, out_], axis=1)
|
||
|
|
self.clear_cache()
|
||
|
|
return out
|
||
|
|
|
||
|
|
def reparameterize(self, mu, log_var):
|
||
|
|
std = mx.exp(0.5 * log_var)
|
||
|
|
eps = mx.random.normal(std.shape)
|
||
|
|
return eps * std + mu
|
||
|
|
|
||
|
|
def __call__(self, x):
|
||
|
|
mu, log_var = self.encode(x, self.scale)
|
||
|
|
z = self.reparameterize(mu, log_var)
|
||
|
|
x_recon = self.decode(z, self.scale)
|
||
|
|
return x_recon, mu, log_var
|
||
|
|
|
||
|
|
def sample(self, imgs, deterministic=False):
|
||
|
|
mu, log_var = self.encode(imgs, self.scale)
|
||
|
|
if deterministic:
|
||
|
|
return mu
|
||
|
|
std = mx.exp(0.5 * mx.clip(log_var, -30.0, 20.0))
|
||
|
|
return mu + std * mx.random.normal(std.shape)
|
||
|
|
|
||
|
|
def clear_cache(self):
|
||
|
|
self._conv_num = count_conv3d(self.decoder)
|
||
|
|
self._conv_idx = [0]
|
||
|
|
self._feat_map = [None] * self._conv_num
|
||
|
|
#cache encode
|
||
|
|
self._enc_conv_num = count_conv3d(self.encoder)
|
||
|
|
self._enc_conv_idx = [0]
|
||
|
|
self._enc_feat_map = [None] * self._enc_conv_num
|
||
|
|
|
||
|
|
|
||
|
|
def _video_vae(pretrained_path=None, z_dim=None, **kwargs):
|
||
|
|
# params
|
||
|
|
cfg = dict(
|
||
|
|
dim=96,
|
||
|
|
z_dim=z_dim,
|
||
|
|
dim_mult=[1, 2, 4, 4],
|
||
|
|
num_res_blocks=2,
|
||
|
|
attn_scales=[],
|
||
|
|
temperal_downsample=[False, True, True],
|
||
|
|
dropout=0.0)
|
||
|
|
cfg.update(**kwargs)
|
||
|
|
|
||
|
|
# init model
|
||
|
|
model = WanVAE_(**cfg)
|
||
|
|
|
||
|
|
# load checkpoint
|
||
|
|
if pretrained_path:
|
||
|
|
logging.info(f'loading {pretrained_path}')
|
||
|
|
weights = mx.load(pretrained_path)
|
||
|
|
model.update(tree_unflatten(list(weights.items())))
|
||
|
|
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
class Wan2_1_VAE:
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
z_dim=16,
|
||
|
|
vae_pth='cache/vae_step_411000.pth',
|
||
|
|
dtype=mx.float32):
|
||
|
|
self.dtype = dtype
|
||
|
|
|
||
|
|
mean = [
|
||
|
|
-0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
|
||
|
|
0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921
|
||
|
|
]
|
||
|
|
std = [
|
||
|
|
2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
|
||
|
|
3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160
|
||
|
|
]
|
||
|
|
self.mean = mx.array(mean, dtype=dtype)
|
||
|
|
self.std = mx.array(std, dtype=dtype)
|
||
|
|
self.scale = [self.mean, 1.0 / self.std]
|
||
|
|
|
||
|
|
# init model
|
||
|
|
self.model = _video_vae(
|
||
|
|
pretrained_path=vae_pth,
|
||
|
|
z_dim=z_dim,
|
||
|
|
)
|
||
|
|
|
||
|
|
def encode(self, videos):
|
||
|
|
"""
|
||
|
|
videos: A list of videos each with shape [C, T, H, W].
|
||
|
|
Returns: List of encoded videos in [C, T, H, W] format.
|
||
|
|
"""
|
||
|
|
encoded = []
|
||
|
|
for video in videos:
|
||
|
|
# Convert CTHW -> BTHWC
|
||
|
|
x = mx.expand_dims(video, axis=0) # Add batch dimension
|
||
|
|
x = x.transpose(0, 2, 3, 4, 1) # BCTHW -> BTHWC
|
||
|
|
|
||
|
|
# Encode
|
||
|
|
z = self.model.encode(x, self.scale)[0] # Get mu only
|
||
|
|
|
||
|
|
# Convert back BTHWC -> CTHW and remove batch dimension
|
||
|
|
z = z.transpose(0, 4, 1, 2, 3) # BTHWC -> BCTHW
|
||
|
|
z = z.squeeze(0) # Remove batch dimension -> CTHW
|
||
|
|
|
||
|
|
encoded.append(z.astype(mx.float32))
|
||
|
|
|
||
|
|
return encoded
|
||
|
|
|
||
|
|
def decode(self, zs):
|
||
|
|
"""
|
||
|
|
zs: A list of latent codes each with shape [C, T, H, W].
|
||
|
|
Returns: List of decoded videos in [C, T, H, W] format.
|
||
|
|
"""
|
||
|
|
decoded = []
|
||
|
|
for z in zs:
|
||
|
|
# Convert CTHW -> BTHWC
|
||
|
|
x = mx.expand_dims(z, axis=0) # Add batch dimension
|
||
|
|
x = x.transpose(0, 2, 3, 4, 1) # BCTHW -> BTHWC
|
||
|
|
|
||
|
|
# Decode
|
||
|
|
x = self.model.decode(x, self.scale)
|
||
|
|
|
||
|
|
# Convert back BTHWC -> CTHW and remove batch dimension
|
||
|
|
x = x.transpose(0, 4, 1, 2, 3) # BTHWC -> BCTHW
|
||
|
|
x = x.squeeze(0) # Remove batch dimension -> CTHW
|
||
|
|
|
||
|
|
# Clamp values
|
||
|
|
x = mx.clip(x, -1, 1)
|
||
|
|
|
||
|
|
decoded.append(x.astype(mx.float32))
|
||
|
|
|
||
|
|
return decoded
|