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507 lines
20 KiB
Python
507 lines
20 KiB
Python
# original version: https://github.com/Wan-Video/Wan2.1/blob/main/wan/modules/model.py
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import math
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import torch
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import torch.nn as nn
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from einops import repeat
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from comfy.ldm.modules.attention import optimized_attention
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from comfy.ldm.flux.layers import EmbedND
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from comfy.ldm.flux.math import apply_rope
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import comfy.ldm.common_dit
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def sinusoidal_embedding_1d(dim, position):
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# preprocess
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assert dim % 2 == 0
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half = dim // 2
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position = position.type(torch.float64)
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# calculation
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sinusoid = torch.outer(
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position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
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x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
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return x
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class WanRMSNorm(nn.Module):
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def __init__(self, dim, eps=1e-5, device=None, dtype=None):
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super().__init__()
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self.dim = dim
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim, device=device, dtype=dtype))
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def forward(self, x):
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r"""
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Args:
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x(Tensor): Shape [B, L, C]
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"""
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return self._norm(x.float()).type_as(x) * self.weight
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
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class WanSelfAttention(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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window_size=(-1, -1),
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qk_norm=True,
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eps=1e-6, operation_settings={}):
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assert dim % num_heads == 0
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.window_size = window_size
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self.qk_norm = qk_norm
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self.eps = eps
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# layers
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self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.norm_q = WanRMSNorm(dim, eps=eps, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
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self.norm_k = WanRMSNorm(dim, eps=eps, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
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def forward(self, x, freqs):
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r"""
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Args:
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x(Tensor): Shape [B, L, num_heads, C / num_heads]
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freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
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"""
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b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
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# query, key, value function
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def qkv_fn(x):
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q = self.norm_q(self.q(x)).view(b, s, n, d)
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k = self.norm_k(self.k(x)).view(b, s, n, d)
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v = self.v(x).view(b, s, n * d)
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return q, k, v
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q, k, v = qkv_fn(x)
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q, k = apply_rope(q, k, freqs)
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x = optimized_attention(
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q.view(b, s, n * d),
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k.view(b, s, n * d),
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v,
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heads=self.num_heads,
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)
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x = self.o(x)
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return x
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class WanT2VCrossAttention(WanSelfAttention):
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def forward(self, x, context):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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context(Tensor): Shape [B, L2, C]
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"""
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# compute query, key, value
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q = self.norm_q(self.q(x))
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k = self.norm_k(self.k(context))
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v = self.v(context)
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# compute attention
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x = optimized_attention(q, k, v, heads=self.num_heads)
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x = self.o(x)
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return x
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class WanI2VCrossAttention(WanSelfAttention):
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def __init__(self,
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dim,
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num_heads,
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window_size=(-1, -1),
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qk_norm=True,
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eps=1e-6, operation_settings={}):
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super().__init__(dim, num_heads, window_size, qk_norm, eps)
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self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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# self.alpha = nn.Parameter(torch.zeros((1, )))
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self.norm_k_img = WanRMSNorm(dim, eps=eps, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
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def forward(self, x, context):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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context(Tensor): Shape [B, L2, C]
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"""
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context_img = context[:, :257]
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context = context[:, 257:]
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# compute query, key, value
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q = self.norm_q(self.q(x))
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k = self.norm_k(self.k(context))
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v = self.v(context)
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k_img = self.norm_k_img(self.k_img(context_img))
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v_img = self.v_img(context_img)
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img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads)
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# compute attention
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x = optimized_attention(q, k, v, heads=self.num_heads)
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# output
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x = x + img_x
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x = self.o(x)
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return x
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WAN_CROSSATTENTION_CLASSES = {
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't2v_cross_attn': WanT2VCrossAttention,
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'i2v_cross_attn': WanI2VCrossAttention,
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}
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class WanAttentionBlock(nn.Module):
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def __init__(self,
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cross_attn_type,
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dim,
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ffn_dim,
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num_heads,
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window_size=(-1, -1),
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qk_norm=True,
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cross_attn_norm=False,
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eps=1e-6, operation_settings={}):
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super().__init__()
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self.dim = dim
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self.ffn_dim = ffn_dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.qk_norm = qk_norm
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self.cross_attn_norm = cross_attn_norm
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self.eps = eps
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# layers
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self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,
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eps, operation_settings=operation_settings)
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self.norm3 = operation_settings.get("operations").LayerNorm(
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dim, eps,
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elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if cross_attn_norm else nn.Identity()
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self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim,
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num_heads,
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(-1, -1),
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qk_norm,
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eps, operation_settings=operation_settings)
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self.norm2 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.ffn = nn.Sequential(
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operation_settings.get("operations").Linear(dim, ffn_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'),
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operation_settings.get("operations").Linear(ffn_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
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# modulation
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self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
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def forward(
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self,
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x,
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e,
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freqs,
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context,
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):
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r"""
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Args:
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x(Tensor): Shape [B, L, C]
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e(Tensor): Shape [B, 6, C]
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freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
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"""
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# assert e.dtype == torch.float32
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e = (self.modulation + e).chunk(6, dim=1)
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# assert e[0].dtype == torch.float32
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# self-attention
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y = self.self_attn(
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self.norm1(x) * (1 + e[1]) + e[0],
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freqs)
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x = x + y * e[2]
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# cross-attention & ffn function
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def cross_attn_ffn(x, context, e):
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x = x + self.cross_attn(self.norm3(x), context)
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y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
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x = x + y * e[5]
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return x
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x = cross_attn_ffn(x, context, e)
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return x
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class Head(nn.Module):
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def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}):
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super().__init__()
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self.dim = dim
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self.out_dim = out_dim
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self.patch_size = patch_size
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self.eps = eps
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# layers
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out_dim = math.prod(patch_size) * out_dim
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self.norm = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.head = operation_settings.get("operations").Linear(dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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# modulation
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self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
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def forward(self, x, e):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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e(Tensor): Shape [B, C]
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"""
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# assert e.dtype == torch.float32
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e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1)
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x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
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return x
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class MLPProj(torch.nn.Module):
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def __init__(self, in_dim, out_dim, operation_settings={}):
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super().__init__()
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self.proj = torch.nn.Sequential(
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operation_settings.get("operations").LayerNorm(in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), operation_settings.get("operations").Linear(in_dim, in_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
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torch.nn.GELU(), operation_settings.get("operations").Linear(in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
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operation_settings.get("operations").LayerNorm(out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
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def forward(self, image_embeds):
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clip_extra_context_tokens = self.proj(image_embeds)
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return clip_extra_context_tokens
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class WanModel(torch.nn.Module):
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r"""
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Wan diffusion backbone supporting both text-to-video and image-to-video.
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"""
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def __init__(self,
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model_type='t2v',
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patch_size=(1, 2, 2),
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text_len=512,
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in_dim=16,
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dim=2048,
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ffn_dim=8192,
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freq_dim=256,
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text_dim=4096,
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out_dim=16,
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num_heads=16,
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num_layers=32,
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window_size=(-1, -1),
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qk_norm=True,
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cross_attn_norm=True,
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eps=1e-6,
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image_model=None,
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device=None,
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dtype=None,
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operations=None,
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):
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r"""
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Initialize the diffusion model backbone.
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Args:
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model_type (`str`, *optional*, defaults to 't2v'):
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Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
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patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
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3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
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text_len (`int`, *optional*, defaults to 512):
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Fixed length for text embeddings
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in_dim (`int`, *optional*, defaults to 16):
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Input video channels (C_in)
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dim (`int`, *optional*, defaults to 2048):
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Hidden dimension of the transformer
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ffn_dim (`int`, *optional*, defaults to 8192):
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Intermediate dimension in feed-forward network
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freq_dim (`int`, *optional*, defaults to 256):
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Dimension for sinusoidal time embeddings
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text_dim (`int`, *optional*, defaults to 4096):
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Input dimension for text embeddings
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out_dim (`int`, *optional*, defaults to 16):
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Output video channels (C_out)
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num_heads (`int`, *optional*, defaults to 16):
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Number of attention heads
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num_layers (`int`, *optional*, defaults to 32):
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Number of transformer blocks
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window_size (`tuple`, *optional*, defaults to (-1, -1)):
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Window size for local attention (-1 indicates global attention)
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qk_norm (`bool`, *optional*, defaults to True):
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Enable query/key normalization
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cross_attn_norm (`bool`, *optional*, defaults to False):
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Enable cross-attention normalization
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eps (`float`, *optional*, defaults to 1e-6):
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Epsilon value for normalization layers
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"""
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super().__init__()
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self.dtype = dtype
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operation_settings = {"operations": operations, "device": device, "dtype": dtype}
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assert model_type in ['t2v', 'i2v']
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self.model_type = model_type
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self.patch_size = patch_size
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self.text_len = text_len
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self.in_dim = in_dim
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self.dim = dim
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self.ffn_dim = ffn_dim
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self.freq_dim = freq_dim
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self.text_dim = text_dim
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self.out_dim = out_dim
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self.num_heads = num_heads
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self.num_layers = num_layers
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self.window_size = window_size
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self.qk_norm = qk_norm
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self.cross_attn_norm = cross_attn_norm
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self.eps = eps
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# embeddings
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self.patch_embedding = operations.Conv3d(
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in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.text_embedding = nn.Sequential(
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operations.Linear(text_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'),
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operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
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self.time_embedding = nn.Sequential(
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operations.Linear(freq_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.SiLU(), operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
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self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
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# blocks
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cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
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self.blocks = nn.ModuleList([
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WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
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window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
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for _ in range(num_layers)
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])
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# head
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self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings)
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d = dim // num_heads
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self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)])
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if model_type == 'i2v':
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self.img_emb = MLPProj(1280, dim, operation_settings=operation_settings)
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def forward_orig(
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self,
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x,
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t,
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context,
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clip_fea=None,
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y=None,
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freqs=None,
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):
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r"""
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Forward pass through the diffusion model
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Args:
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x (Tensor):
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List of input video tensors with shape [B, C_in, F, H, W]
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t (Tensor):
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Diffusion timesteps tensor of shape [B]
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context (List[Tensor]):
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List of text embeddings each with shape [B, L, C]
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seq_len (`int`):
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Maximum sequence length for positional encoding
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clip_fea (Tensor, *optional*):
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CLIP image features for image-to-video mode
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y (List[Tensor], *optional*):
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Conditional video inputs for image-to-video mode, same shape as x
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Returns:
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List[Tensor]:
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List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
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"""
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if self.model_type == 'i2v':
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assert clip_fea is not None and y is not None
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if y is not None:
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x = torch.cat([x, y], dim=0)
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# embeddings
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x = self.patch_embedding(x)
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grid_sizes = x.shape[2:]
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x = x.flatten(2).transpose(1, 2)
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# time embeddings
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e = self.time_embedding(
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sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
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e0 = self.time_projection(e).unflatten(1, (6, self.dim))
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# context
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context = self.text_embedding(torch.cat([context, context.new_zeros(context.size(0), self.text_len - context.size(1), context.size(2))], dim=1))
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if clip_fea is not None:
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context_clip = self.img_emb(clip_fea) # bs x 257 x dim
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context = torch.concat([context_clip, context], dim=1)
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# arguments
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kwargs = dict(
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e=e0,
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freqs=freqs,
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context=context)
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for block in self.blocks:
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x = block(x, **kwargs)
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# head
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x = self.head(x, e)
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# unpatchify
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x = self.unpatchify(x, grid_sizes)
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return x
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# return [u.float() for u in x]
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|
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def forward(self, x, timestep, context, y=None, image=None, **kwargs):
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bs, c, t, h, w = x.shape
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x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
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patch_size = self.patch_size
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t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
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h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
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w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
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img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
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img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
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img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
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|
img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
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|
img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
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|
|
|
freqs = self.rope_embedder(img_ids).movedim(1, 2)
|
|
return self.forward_orig(x, timestep, context, clip_fea=y, y=image, freqs=freqs)[:, :, :t, :h, :w]
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|
|
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def unpatchify(self, x, grid_sizes):
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|
r"""
|
|
Reconstruct video tensors from patch embeddings.
|
|
|
|
Args:
|
|
x (List[Tensor]):
|
|
List of patchified features, each with shape [L, C_out * prod(patch_size)]
|
|
grid_sizes (Tensor):
|
|
Original spatial-temporal grid dimensions before patching,
|
|
shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
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|
|
|
Returns:
|
|
List[Tensor]:
|
|
Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8]
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|
"""
|
|
|
|
c = self.out_dim
|
|
u = x
|
|
b = u.shape[0]
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|
u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c)
|
|
u = torch.einsum('bfhwpqrc->bcfphqwr', u)
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|
u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)])
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|
return u
|