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https://github.com/comfyanonymous/ComfyUI.git
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Refactor CausalWanModel to inherit from WanModel.
This commit is contained in:
Talmaj Marinc
parent
3a9547192e
commit
e649a3bc72
@ -9,19 +9,16 @@ 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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WAN_CROSSATTENTION_CLASSES,
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Head,
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MLPProj,
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repeat_e,
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WanModel,
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WanAttentionBlock,
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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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@ -87,33 +84,18 @@ class CausalWanSelfAttention(nn.Module):
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return x
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class CausalWanAttentionBlock(nn.Module):
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class CausalWanAttentionBlock(WanAttentionBlock):
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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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super().__init__(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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self.self_attn = CausalWanSelfAttention(
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dim, num_heads, window_size, qk_norm, eps,
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operation_settings=operation_settings)
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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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@ -150,7 +132,7 @@ class CausalWanAttentionBlock(nn.Module):
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return x
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class CausalWanModel(torch.nn.Module):
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class CausalWanModel(WanModel):
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"""
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Wan 2.1 diffusion backbone with causal KV-cache support.
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@ -178,82 +160,14 @@ class CausalWanModel(torch.nn.Module):
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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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super().__init__(
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model_type=model_type, patch_size=patch_size, text_len=text_len,
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in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim,
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text_dim=text_dim, out_dim=out_dim, num_heads=num_heads,
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num_layers=num_layers, window_size=window_size, qk_norm=qk_norm,
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cross_attn_norm=cross_attn_norm, eps=eps, image_model=image_model,
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wan_attn_block_class=CausalWanAttentionBlock,
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device=device, dtype=dtype, operations=operations)
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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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@ -275,11 +189,11 @@ class CausalWanModel(torch.nn.Module):
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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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x = self.patch_embedding(x.float()).to(x.dtype)
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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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# Per-frame time embedding
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e = self.time_embedding(
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sinusoidal_embedding_1d(self.freq_dim, timestep.flatten()).to(dtype=x.dtype))
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e = e.reshape(timestep.shape[0], -1, e.shape[-1])
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@ -311,14 +225,6 @@ class CausalWanModel(torch.nn.Module):
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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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@ -365,39 +271,6 @@ class CausalWanModel(torch.nn.Module):
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clip_fea=clip_fea,
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)
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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()).to(dtype=x.dtype))
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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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return super().forward(x, timestep, context, clip_fea=clip_fea,
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time_dim_concat=time_dim_concat,
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transformer_options=transformer_options, **kwargs)
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