mirror of
https://github.com/comfyanonymous/ComfyUI.git
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393 lines
16 KiB
Python
393 lines
16 KiB
Python
"""
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CausalWanModel: Wan 2.1 backbone with KV-cached causal self-attention for
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autoregressive (frame-by-frame) video generation via Causal Forcing.
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Weight-compatible with the standard WanModel -- same layer names, same shapes.
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The difference is purely in the forward pass: this model processes one temporal
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block at a time and maintains a KV cache across blocks.
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Reference: https://github.com/thu-ml/Causal-Forcing
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"""
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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 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_rope1
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from comfy.ldm.wan.model import (
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sinusoidal_embedding_1d,
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WanT2VCrossAttention,
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WAN_CROSSATTENTION_CLASSES,
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Head,
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MLPProj,
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repeat_e,
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)
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import comfy.ldm.common_dit
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import comfy.model_management
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class CausalWanSelfAttention(nn.Module):
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"""Self-attention with KV cache support for autoregressive inference."""
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def __init__(self, dim, num_heads, window_size=(-1, -1), 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.qk_norm = qk_norm
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self.eps = eps
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ops = operation_settings.get("operations")
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device = operation_settings.get("device")
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dtype = operation_settings.get("dtype")
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self.q = ops.Linear(dim, dim, device=device, dtype=dtype)
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self.k = ops.Linear(dim, dim, device=device, dtype=dtype)
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self.v = ops.Linear(dim, dim, device=device, dtype=dtype)
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self.o = ops.Linear(dim, dim, device=device, dtype=dtype)
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self.norm_q = ops.RMSNorm(dim, eps=eps, elementwise_affine=True, device=device, dtype=dtype) if qk_norm else nn.Identity()
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self.norm_k = ops.RMSNorm(dim, eps=eps, elementwise_affine=True, device=device, dtype=dtype) if qk_norm else nn.Identity()
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def forward(self, x, freqs, kv_cache=None, transformer_options={}):
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b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
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q = apply_rope1(self.norm_q(self.q(x)).view(b, s, n, d), freqs)
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k = apply_rope1(self.norm_k(self.k(x)).view(b, s, n, d), freqs)
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v = self.v(x).view(b, s, n, d)
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if kv_cache is None:
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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.view(b, s, n * d),
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heads=self.num_heads,
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transformer_options=transformer_options,
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)
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else:
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end = kv_cache["end"].item()
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new_end = end + s
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# Roped K and plain V go into cache
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kv_cache["k"][:, end:new_end] = k
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kv_cache["v"][:, end:new_end] = v
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kv_cache["end"].fill_(new_end)
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x = optimized_attention(
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q.view(b, s, n * d),
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kv_cache["k"][:, :new_end].view(b, new_end, n * d),
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kv_cache["v"][:, :new_end].view(b, new_end, n * d),
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heads=self.num_heads,
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transformer_options=transformer_options,
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)
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x = self.o(x)
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return x
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class CausalWanAttentionBlock(nn.Module):
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"""Transformer block with KV-cached self-attention and cross-attention caching."""
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def __init__(self, cross_attn_type, dim, ffn_dim, num_heads,
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window_size=(-1, -1), qk_norm=True, 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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ops = operation_settings.get("operations")
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device = operation_settings.get("device")
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dtype = operation_settings.get("dtype")
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self.norm1 = ops.LayerNorm(dim, eps, elementwise_affine=False, device=device, dtype=dtype)
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self.self_attn = CausalWanSelfAttention(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings)
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self.norm3 = ops.LayerNorm(dim, eps, elementwise_affine=True, device=device, dtype=dtype) if cross_attn_norm else nn.Identity()
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self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](
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dim, num_heads, (-1, -1), qk_norm, eps, operation_settings=operation_settings)
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self.norm2 = ops.LayerNorm(dim, eps, elementwise_affine=False, device=device, dtype=dtype)
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self.ffn = nn.Sequential(
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ops.Linear(dim, ffn_dim, device=device, dtype=dtype),
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nn.GELU(approximate='tanh'),
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ops.Linear(ffn_dim, dim, device=device, dtype=dtype))
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self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=device, dtype=dtype))
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def forward(self, x, e, freqs, context, context_img_len=257,
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kv_cache=None, crossattn_cache=None, transformer_options={}):
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if e.ndim < 4:
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e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
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else:
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e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)
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# Self-attention with optional KV cache
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x = x.contiguous()
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y = self.self_attn(
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torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
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freqs, kv_cache=kv_cache, transformer_options=transformer_options)
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x = torch.addcmul(x, y, repeat_e(e[2], x))
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del y
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# Cross-attention with optional caching
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if crossattn_cache is not None and crossattn_cache.get("is_init"):
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q = self.cross_attn.norm_q(self.cross_attn.q(self.norm3(x)))
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x_ca = optimized_attention(
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q, crossattn_cache["k"], crossattn_cache["v"],
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heads=self.num_heads, transformer_options=transformer_options)
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x = x + self.cross_attn.o(x_ca)
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else:
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x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
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if crossattn_cache is not None:
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crossattn_cache["k"] = self.cross_attn.norm_k(self.cross_attn.k(context))
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crossattn_cache["v"] = self.cross_attn.v(context)
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crossattn_cache["is_init"] = True
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# FFN
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y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
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x = torch.addcmul(x, y, repeat_e(e[5], x))
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return x
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class CausalWanModel(torch.nn.Module):
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"""
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Wan 2.1 diffusion backbone with causal KV-cache support.
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Same weight structure as WanModel -- loads identical state dicts.
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Adds forward_block() for frame-by-frame autoregressive inference.
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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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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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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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self.patch_embedding = operations.Conv3d(
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in_dim, dim, kernel_size=patch_size, stride=patch_size,
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device=device, dtype=dtype)
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self.text_embedding = nn.Sequential(
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operations.Linear(text_dim, dim, device=device, dtype=dtype),
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nn.GELU(approximate='tanh'),
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operations.Linear(dim, dim, device=device, dtype=dtype))
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self.time_embedding = nn.Sequential(
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operations.Linear(freq_dim, dim, device=device, dtype=dtype),
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nn.SiLU(),
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operations.Linear(dim, dim, device=device, dtype=dtype))
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self.time_projection = nn.Sequential(
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nn.SiLU(),
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operations.Linear(dim, dim * 6, device=device, dtype=dtype))
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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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CausalWanAttentionBlock(
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cross_attn_type, dim, ffn_dim, num_heads,
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window_size, qk_norm, cross_attn_norm, eps,
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operation_settings=operation_settings)
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for _ in range(num_layers)
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])
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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(
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dim=d, theta=10000.0,
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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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else:
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self.img_emb = None
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self.ref_conv = None
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def rope_encode(self, t, h, w, t_start=0, device=None, dtype=None):
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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=device, dtype=dtype)
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img_ids[:, :, :, 0] += torch.linspace(
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t_start, t_start + (t_len - 1), steps=t_len, device=device, dtype=dtype
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).reshape(-1, 1, 1)
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img_ids[:, :, :, 1] += torch.linspace(
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0, h_len - 1, steps=h_len, device=device, dtype=dtype
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).reshape(1, -1, 1)
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img_ids[:, :, :, 2] += torch.linspace(
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0, w_len - 1, steps=w_len, device=device, dtype=dtype
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).reshape(1, 1, -1)
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img_ids = img_ids.reshape(1, -1, img_ids.shape[-1])
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return self.rope_embedder(img_ids).movedim(1, 2)
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def forward_block(self, x, timestep, context, start_frame,
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kv_caches, crossattn_caches, clip_fea=None):
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"""
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Forward one temporal block for autoregressive inference.
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Args:
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x: [B, C, block_frames, H, W] input latent for the current block
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timestep: [B, block_frames] per-frame timesteps
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context: [B, L, text_dim] raw text embeddings (pre-text_embedding)
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start_frame: temporal frame index for RoPE offset
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kv_caches: list of per-layer KV cache dicts
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crossattn_caches: list of per-layer cross-attention cache dicts
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clip_fea: optional CLIP features for I2V
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Returns:
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flow_pred: [B, C_out, block_frames, H, W] flow prediction
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"""
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x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
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bs, c, t, h, w = x.shape
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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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# Per-frame time embedding → [B, block_frames, 6, dim]
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e = self.time_embedding(
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sinusoidal_embedding_1d(self.freq_dim, timestep.flatten()))
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e = e.reshape(timestep.shape[0], -1, e.shape[-1])
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e0 = self.time_projection(e).unflatten(2, (6, self.dim))
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# Text embedding (reuses crossattn_cache after first block)
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context = self.text_embedding(context)
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context_img_len = None
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if clip_fea is not None and self.img_emb is not None:
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context_clip = self.img_emb(clip_fea)
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context = torch.concat([context_clip, context], dim=1)
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context_img_len = clip_fea.shape[-2]
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# RoPE for current block's temporal position
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freqs = self.rope_encode(t, h, w, t_start=start_frame, device=x.device, dtype=x.dtype)
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# Transformer blocks
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for i, block in enumerate(self.blocks):
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x = block(x, e=e0, freqs=freqs, context=context,
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context_img_len=context_img_len,
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kv_cache=kv_caches[i],
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crossattn_cache=crossattn_caches[i])
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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[:, :, :t, :h, :w]
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def unpatchify(self, x, grid_sizes):
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c = self.out_dim
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b = x.shape[0]
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u = x[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c)
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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
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def init_kv_caches(self, batch_size, max_seq_len, device, dtype):
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"""Create fresh KV caches for all layers."""
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caches = []
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for _ in range(self.num_layers):
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caches.append({
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"k": torch.zeros(batch_size, max_seq_len, self.num_heads, self.head_dim, device=device, dtype=dtype),
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"v": torch.zeros(batch_size, max_seq_len, self.num_heads, self.head_dim, device=device, dtype=dtype),
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"end": torch.tensor([0], dtype=torch.long, device=device),
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})
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return caches
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def init_crossattn_caches(self, batch_size, device, dtype):
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"""Create fresh cross-attention caches for all layers."""
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caches = []
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for _ in range(self.num_layers):
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caches.append({"is_init": False})
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return caches
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def reset_kv_caches(self, kv_caches):
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"""Reset KV caches to empty (reuse allocated memory)."""
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for cache in kv_caches:
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cache["end"].fill_(0)
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def reset_crossattn_caches(self, crossattn_caches):
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"""Reset cross-attention caches."""
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for cache in crossattn_caches:
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cache["is_init"] = False
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@property
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def head_dim(self):
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return self.dim // self.num_heads
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# Standard forward for non-causal use (compatibility with ComfyUI infrastructure)
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def forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, **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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t_len = t
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if time_dim_concat is not None:
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time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, self.patch_size)
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x = torch.cat([x, time_dim_concat], dim=2)
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t_len = x.shape[2]
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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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freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype)
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e = self.time_embedding(
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sinusoidal_embedding_1d(self.freq_dim, timestep.flatten()))
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e = e.reshape(timestep.shape[0], -1, e.shape[-1])
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e0 = self.time_projection(e).unflatten(2, (6, self.dim))
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context = self.text_embedding(context)
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context_img_len = None
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if clip_fea is not None and self.img_emb is not None:
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context_clip = self.img_emb(clip_fea)
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context = torch.concat([context_clip, context], dim=1)
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context_img_len = clip_fea.shape[-2]
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for block in self.blocks:
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x = block(x, e=e0, freqs=freqs, context=context,
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context_img_len=context_img_len,
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transformer_options=transformer_options)
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x = self.head(x, e)
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x = self.unpatchify(x, grid_sizes)
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return x[:, :, :t, :h, :w]
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