|
|
|
|
| import math
|
|
|
| import torch
|
| import torch.nn as nn
|
| from einops import repeat
|
|
|
| from comfy.ldm.modules.attention import optimized_attention
|
| from comfy.ldm.flux.layers import EmbedND
|
| from comfy.ldm.flux.math import apply_rope
|
| from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm
|
| import comfy.ldm.common_dit
|
| import comfy.model_management
|
|
|
|
|
| def sinusoidal_embedding_1d(dim, position):
|
|
|
| assert dim % 2 == 0
|
| half = dim // 2
|
| position = position.type(torch.float32)
|
|
|
|
|
| sinusoid = torch.outer(
|
| position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
|
| x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
|
| return x
|
|
|
|
|
| class WanSelfAttention(nn.Module):
|
|
|
| def __init__(self,
|
| dim,
|
| num_heads,
|
| window_size=(-1, -1),
|
| qk_norm=True,
|
| eps=1e-6, operation_settings={}):
|
| assert dim % num_heads == 0
|
| super().__init__()
|
| self.dim = dim
|
| self.num_heads = num_heads
|
| self.head_dim = dim // num_heads
|
| self.window_size = window_size
|
| self.qk_norm = qk_norm
|
| self.eps = eps
|
|
|
|
|
| self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
| self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
| self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
| self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
| self.norm_q = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
|
| self.norm_k = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
|
|
|
| def forward(self, x, freqs):
|
| r"""
|
| Args:
|
| x(Tensor): Shape [B, L, num_heads, C / num_heads]
|
| freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
|
| """
|
| b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
|
|
|
|
|
| def qkv_fn(x):
|
| q = self.norm_q(self.q(x)).view(b, s, n, d)
|
| k = self.norm_k(self.k(x)).view(b, s, n, d)
|
| v = self.v(x).view(b, s, n * d)
|
| return q, k, v
|
|
|
| q, k, v = qkv_fn(x)
|
| q, k = apply_rope(q, k, freqs)
|
|
|
| x = optimized_attention(
|
| q.view(b, s, n * d),
|
| k.view(b, s, n * d),
|
| v,
|
| heads=self.num_heads,
|
| )
|
|
|
| x = self.o(x)
|
| return x
|
|
|
|
|
| class WanT2VCrossAttention(WanSelfAttention):
|
|
|
| def forward(self, x, context, **kwargs):
|
| r"""
|
| Args:
|
| x(Tensor): Shape [B, L1, C]
|
| context(Tensor): Shape [B, L2, C]
|
| """
|
|
|
| q = self.norm_q(self.q(x))
|
| k = self.norm_k(self.k(context))
|
| v = self.v(context)
|
|
|
|
|
| x = optimized_attention(q, k, v, heads=self.num_heads)
|
|
|
| x = self.o(x)
|
| return x
|
|
|
|
|
| class WanI2VCrossAttention(WanSelfAttention):
|
|
|
| def __init__(self,
|
| dim,
|
| num_heads,
|
| window_size=(-1, -1),
|
| qk_norm=True,
|
| eps=1e-6, operation_settings={}):
|
| super().__init__(dim, num_heads, window_size, qk_norm, eps, operation_settings=operation_settings)
|
|
|
| self.k_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
| self.v_img = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
|
|
| self.norm_k_img = RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
|
|
|
| def forward(self, x, context, context_img_len):
|
| r"""
|
| Args:
|
| x(Tensor): Shape [B, L1, C]
|
| context(Tensor): Shape [B, L2, C]
|
| """
|
| context_img = context[:, :context_img_len]
|
| context = context[:, context_img_len:]
|
|
|
|
|
| q = self.norm_q(self.q(x))
|
| k = self.norm_k(self.k(context))
|
| v = self.v(context)
|
| k_img = self.norm_k_img(self.k_img(context_img))
|
| v_img = self.v_img(context_img)
|
| img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads)
|
|
|
| x = optimized_attention(q, k, v, heads=self.num_heads)
|
|
|
|
|
| x = x + img_x
|
| x = self.o(x)
|
| return x
|
|
|
|
|
| WAN_CROSSATTENTION_CLASSES = {
|
| 't2v_cross_attn': WanT2VCrossAttention,
|
| 'i2v_cross_attn': WanI2VCrossAttention,
|
| }
|
|
|
|
|
| class WanAttentionBlock(nn.Module):
|
|
|
| def __init__(self,
|
| cross_attn_type,
|
| dim,
|
| ffn_dim,
|
| num_heads,
|
| window_size=(-1, -1),
|
| qk_norm=True,
|
| cross_attn_norm=False,
|
| eps=1e-6, operation_settings={}):
|
| super().__init__()
|
| self.dim = dim
|
| self.ffn_dim = ffn_dim
|
| self.num_heads = num_heads
|
| self.window_size = window_size
|
| self.qk_norm = qk_norm
|
| self.cross_attn_norm = cross_attn_norm
|
| self.eps = eps
|
|
|
|
|
| self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
| self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,
|
| eps, operation_settings=operation_settings)
|
| self.norm3 = operation_settings.get("operations").LayerNorm(
|
| dim, eps,
|
| elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if cross_attn_norm else nn.Identity()
|
| self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim,
|
| num_heads,
|
| (-1, -1),
|
| qk_norm,
|
| eps, operation_settings=operation_settings)
|
| self.norm2 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
| self.ffn = nn.Sequential(
|
| operation_settings.get("operations").Linear(dim, ffn_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'),
|
| operation_settings.get("operations").Linear(ffn_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
|
|
|
|
|
| self.modulation = nn.Parameter(torch.empty(1, 6, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
|
|
|
| def forward(
|
| self,
|
| x,
|
| e,
|
| freqs,
|
| context,
|
| context_img_len=257,
|
| ):
|
| r"""
|
| Args:
|
| x(Tensor): Shape [B, L, C]
|
| e(Tensor): Shape [B, 6, C]
|
| freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
|
| """
|
|
|
|
|
| e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
|
|
|
|
|
|
|
| y = self.self_attn(
|
| self.norm1(x) * (1 + e[1]) + e[0],
|
| freqs)
|
|
|
| x = x + y * e[2]
|
|
|
|
|
| x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len)
|
| y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
|
| x = x + y * e[5]
|
| return x
|
|
|
|
|
| class Head(nn.Module):
|
|
|
| def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}):
|
| super().__init__()
|
| self.dim = dim
|
| self.out_dim = out_dim
|
| self.patch_size = patch_size
|
| self.eps = eps
|
|
|
|
|
| out_dim = math.prod(patch_size) * out_dim
|
| self.norm = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
| self.head = operation_settings.get("operations").Linear(dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
|
|
|
|
|
| self.modulation = nn.Parameter(torch.empty(1, 2, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
|
|
|
| def forward(self, x, e):
|
| r"""
|
| Args:
|
| x(Tensor): Shape [B, L1, C]
|
| e(Tensor): Shape [B, C]
|
| """
|
|
|
| e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e.unsqueeze(1)).chunk(2, dim=1)
|
| x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
|
| return x
|
|
|
|
|
| class MLPProj(torch.nn.Module):
|
|
|
| def __init__(self, in_dim, out_dim, flf_pos_embed_token_number=None, operation_settings={}):
|
| super().__init__()
|
|
|
| self.proj = torch.nn.Sequential(
|
| 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")),
|
| torch.nn.GELU(), operation_settings.get("operations").Linear(in_dim, out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")),
|
| operation_settings.get("operations").LayerNorm(out_dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
|
|
|
| if flf_pos_embed_token_number is not None:
|
| self.emb_pos = nn.Parameter(torch.empty((1, flf_pos_embed_token_number, in_dim), device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
|
| else:
|
| self.emb_pos = None
|
|
|
| def forward(self, image_embeds):
|
| if self.emb_pos is not None:
|
| image_embeds = image_embeds[:, :self.emb_pos.shape[1]] + comfy.model_management.cast_to(self.emb_pos[:, :image_embeds.shape[1]], dtype=image_embeds.dtype, device=image_embeds.device)
|
|
|
| clip_extra_context_tokens = self.proj(image_embeds)
|
| return clip_extra_context_tokens
|
|
|
|
|
| class WanModel(torch.nn.Module):
|
| r"""
|
| Wan diffusion backbone supporting both text-to-video and image-to-video.
|
| """
|
|
|
| def __init__(self,
|
| model_type='t2v',
|
| patch_size=(1, 2, 2),
|
| text_len=512,
|
| in_dim=16,
|
| dim=2048,
|
| ffn_dim=8192,
|
| freq_dim=256,
|
| text_dim=4096,
|
| out_dim=16,
|
| num_heads=16,
|
| num_layers=32,
|
| window_size=(-1, -1),
|
| qk_norm=True,
|
| cross_attn_norm=True,
|
| eps=1e-6,
|
| flf_pos_embed_token_number=None,
|
| image_model=None,
|
| device=None,
|
| dtype=None,
|
| operations=None,
|
| ):
|
| r"""
|
| Initialize the diffusion model backbone.
|
|
|
| Args:
|
| model_type (`str`, *optional*, defaults to 't2v'):
|
| Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
|
| patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
|
| 3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
|
| text_len (`int`, *optional*, defaults to 512):
|
| Fixed length for text embeddings
|
| in_dim (`int`, *optional*, defaults to 16):
|
| Input video channels (C_in)
|
| dim (`int`, *optional*, defaults to 2048):
|
| Hidden dimension of the transformer
|
| ffn_dim (`int`, *optional*, defaults to 8192):
|
| Intermediate dimension in feed-forward network
|
| freq_dim (`int`, *optional*, defaults to 256):
|
| Dimension for sinusoidal time embeddings
|
| text_dim (`int`, *optional*, defaults to 4096):
|
| Input dimension for text embeddings
|
| out_dim (`int`, *optional*, defaults to 16):
|
| Output video channels (C_out)
|
| num_heads (`int`, *optional*, defaults to 16):
|
| Number of attention heads
|
| num_layers (`int`, *optional*, defaults to 32):
|
| Number of transformer blocks
|
| window_size (`tuple`, *optional*, defaults to (-1, -1)):
|
| Window size for local attention (-1 indicates global attention)
|
| qk_norm (`bool`, *optional*, defaults to True):
|
| Enable query/key normalization
|
| cross_attn_norm (`bool`, *optional*, defaults to False):
|
| Enable cross-attention normalization
|
| eps (`float`, *optional*, defaults to 1e-6):
|
| Epsilon value for normalization layers
|
| """
|
|
|
| super().__init__()
|
| self.dtype = dtype
|
| operation_settings = {"operations": operations, "device": device, "dtype": dtype}
|
|
|
| assert model_type in ['t2v', 'i2v']
|
| self.model_type = model_type
|
|
|
| self.patch_size = patch_size
|
| self.text_len = text_len
|
| self.in_dim = in_dim
|
| self.dim = dim
|
| self.ffn_dim = ffn_dim
|
| self.freq_dim = freq_dim
|
| self.text_dim = text_dim
|
| self.out_dim = out_dim
|
| self.num_heads = num_heads
|
| self.num_layers = num_layers
|
| self.window_size = window_size
|
| self.qk_norm = qk_norm
|
| self.cross_attn_norm = cross_attn_norm
|
| self.eps = eps
|
|
|
|
|
| self.patch_embedding = operations.Conv3d(
|
| in_dim, dim, kernel_size=patch_size, stride=patch_size, device=operation_settings.get("device"), dtype=torch.float32)
|
| self.text_embedding = nn.Sequential(
|
| operations.Linear(text_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")), nn.GELU(approximate='tanh'),
|
| operations.Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
|
|
|
| self.time_embedding = nn.Sequential(
|
| 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")))
|
| self.time_projection = nn.Sequential(nn.SiLU(), operations.Linear(dim, dim * 6, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")))
|
|
|
|
|
| cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
|
| self.blocks = nn.ModuleList([
|
| WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
|
| window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
|
| for _ in range(num_layers)
|
| ])
|
|
|
|
|
| self.head = Head(dim, out_dim, patch_size, eps, operation_settings=operation_settings)
|
|
|
| d = dim // num_heads
|
| self.rope_embedder = EmbedND(dim=d, theta=10000.0, axes_dim=[d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)])
|
|
|
| if model_type == 'i2v':
|
| self.img_emb = MLPProj(1280, dim, flf_pos_embed_token_number=flf_pos_embed_token_number, operation_settings=operation_settings)
|
| else:
|
| self.img_emb = None
|
|
|
| def forward_orig(
|
| self,
|
| x,
|
| t,
|
| context,
|
| clip_fea=None,
|
| freqs=None,
|
| transformer_options={},
|
| ):
|
| r"""
|
| Forward pass through the diffusion model
|
|
|
| Args:
|
| x (Tensor):
|
| List of input video tensors with shape [B, C_in, F, H, W]
|
| t (Tensor):
|
| Diffusion timesteps tensor of shape [B]
|
| context (List[Tensor]):
|
| List of text embeddings each with shape [B, L, C]
|
| seq_len (`int`):
|
| Maximum sequence length for positional encoding
|
| clip_fea (Tensor, *optional*):
|
| CLIP image features for image-to-video mode
|
| y (List[Tensor], *optional*):
|
| Conditional video inputs for image-to-video mode, same shape as x
|
|
|
| Returns:
|
| List[Tensor]:
|
| List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
|
| """
|
|
|
| x = self.patch_embedding(x.float()).to(x.dtype)
|
| grid_sizes = x.shape[2:]
|
| x = x.flatten(2).transpose(1, 2)
|
|
|
|
|
| e = self.time_embedding(
|
| sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
|
| e0 = self.time_projection(e).unflatten(1, (6, self.dim))
|
|
|
|
|
| context = self.text_embedding(context)
|
|
|
| context_img_len = None
|
| if clip_fea is not None:
|
| if self.img_emb is not None:
|
| context_clip = self.img_emb(clip_fea)
|
| context = torch.concat([context_clip, context], dim=1)
|
| context_img_len = clip_fea.shape[-2]
|
|
|
| patches_replace = transformer_options.get("patches_replace", {})
|
| blocks_replace = patches_replace.get("dit", {})
|
| for i, block in enumerate(self.blocks):
|
| if ("double_block", i) in blocks_replace:
|
| def block_wrap(args):
|
| out = {}
|
| out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
|
| return out
|
| out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
|
| x = out["img"]
|
| else:
|
| x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
|
|
|
|
|
| x = self.head(x, e)
|
|
|
|
|
| x = self.unpatchify(x, grid_sizes)
|
| return x
|
|
|
| def forward(self, x, timestep, context, clip_fea=None, transformer_options={},**kwargs):
|
| bs, c, t, h, w = x.shape
|
| x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
|
| patch_size = self.patch_size
|
| t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
|
| h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
|
| w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
|
| img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
|
| 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)
|
| 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)
|
| 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)
|
| img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
|
|
|
| freqs = self.rope_embedder(img_ids).movedim(1, 2)
|
| return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options)[:, :, :t, :h, :w]
|
|
|
| def unpatchify(self, x, grid_sizes):
|
| 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)
|
|
|
| Returns:
|
| List[Tensor]:
|
| Reconstructed video tensors with shape [L, C_out, F, H / 8, W / 8]
|
| """
|
|
|
| c = self.out_dim
|
| u = x
|
| b = u.shape[0]
|
| u = u[:, :math.prod(grid_sizes)].view(b, *grid_sizes, *self.patch_size, c)
|
| u = torch.einsum('bfhwpqrc->bcfphqwr', u)
|
| u = u.reshape(b, c, *[i * j for i, j in zip(grid_sizes, self.patch_size)])
|
| return u
|
|
|
