mirror of
https://github.com/comfyanonymous/ComfyUI.git
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486 lines
22 KiB
Python
486 lines
22 KiB
Python
# Based on Flux code because of weird hunyuan video code license.
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from dataclasses import dataclass
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import torch
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from einops import repeat
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from torch import Tensor, nn
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from ..flux.layers import DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock, timestep_embedding
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from ..modules.attention import optimized_attention
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from ..modules.diffusionmodules.mmdit import PatchEmbed
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from ...patcher_extension import WrapperExecutor, get_all_wrappers, WrappersMP
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@dataclass
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class HunyuanVideoParams:
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in_channels: int
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out_channels: int
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vec_in_dim: int
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context_in_dim: int
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hidden_size: int
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mlp_ratio: float
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num_heads: int
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depth: int
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depth_single_blocks: int
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axes_dim: list
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theta: int
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patch_size: list
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qkv_bias: bool
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guidance_embed: bool
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byt5: bool
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meanflow: bool
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use_cond_type_embedding: bool
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vision_in_dim: int
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meanflow_sum: bool
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class SelfAttentionRef(nn.Module):
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def __init__(self, dim: int, qkv_bias: bool = False, dtype=None, device=None, operations=None):
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super().__init__()
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self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
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self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
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class TokenRefinerBlock(nn.Module):
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def __init__(
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self,
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hidden_size,
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heads,
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dtype=None,
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device=None,
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operations=None
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):
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super().__init__()
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self.heads = heads
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mlp_hidden_dim = hidden_size * 4
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device),
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)
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self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
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self.self_attn = SelfAttentionRef(hidden_size, True, dtype=dtype, device=device, operations=operations)
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self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
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self.mlp = nn.Sequential(
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operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
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nn.SiLU(),
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operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
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)
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def forward(self, x, c, mask, transformer_options={}):
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mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1)
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norm_x = self.norm1(x)
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qkv = self.self_attn.qkv(norm_x)
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q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4)
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attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options)
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x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1)
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x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1)
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return x
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class IndividualTokenRefiner(nn.Module):
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def __init__(
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self,
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hidden_size,
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heads,
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num_blocks,
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dtype=None,
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device=None,
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operations=None
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):
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super().__init__()
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self.blocks = nn.ModuleList(
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[
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TokenRefinerBlock(
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hidden_size=hidden_size,
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heads=heads,
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dtype=dtype,
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device=device,
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operations=operations
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)
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for _ in range(num_blocks)
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]
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)
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def forward(self, x, c, mask, transformer_options={}):
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m = None
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if mask is not None:
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m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1)
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m = m + m.transpose(2, 3)
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for block in self.blocks:
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x = block(x, c, m, transformer_options=transformer_options)
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return x
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class TokenRefiner(nn.Module):
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def __init__(
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self,
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text_dim,
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hidden_size,
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heads,
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num_blocks,
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dtype=None,
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device=None,
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operations=None
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):
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super().__init__()
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self.input_embedder = operations.Linear(text_dim, hidden_size, bias=True, dtype=dtype, device=device)
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self.t_embedder = MLPEmbedder(256, hidden_size, dtype=dtype, device=device, operations=operations)
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self.c_embedder = MLPEmbedder(text_dim, hidden_size, dtype=dtype, device=device, operations=operations)
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self.individual_token_refiner = IndividualTokenRefiner(hidden_size, heads, num_blocks, dtype=dtype, device=device, operations=operations)
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def forward(
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self,
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x,
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timesteps,
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mask,
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transformer_options={},
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):
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t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype))
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# m = mask.float().unsqueeze(-1)
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# c = (x.float() * m).sum(dim=1) / m.sum(dim=1) #TODO: the following works when the x.shape is the same length as the tokens but might break otherwise
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if x.dtype == torch.float16:
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c = x.float().sum(dim=1) / x.shape[1]
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else:
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c = x.sum(dim=1) / x.shape[1]
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c = t + self.c_embedder(c.to(x.dtype))
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x = self.input_embedder(x)
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x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options)
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return x
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class ByT5Mapper(nn.Module):
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def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_res=False, dtype=None, device=None, operations=None):
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super().__init__()
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self.layernorm = operations.LayerNorm(in_dim, dtype=dtype, device=device)
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self.fc1 = operations.Linear(in_dim, hidden_dim, dtype=dtype, device=device)
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self.fc2 = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
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self.fc3 = operations.Linear(out_dim, out_dim1, dtype=dtype, device=device)
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self.use_res = use_res
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self.act_fn = nn.GELU()
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def forward(self, x):
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if self.use_res:
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res = x
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x = self.layernorm(x)
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x = self.fc1(x)
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x = self.act_fn(x)
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x = self.fc2(x)
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x2 = self.act_fn(x)
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x2 = self.fc3(x2)
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if self.use_res:
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x2 = x2 + res
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return x2
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class HunyuanVideo(nn.Module):
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"""
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Transformer model for flow matching on sequences.
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"""
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def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
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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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params = HunyuanVideoParams(**kwargs)
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self.params = params
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self.patch_size = params.patch_size
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self.in_channels = params.in_channels
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self.out_channels = params.out_channels
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self.use_cond_type_embedding = params.use_cond_type_embedding
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self.vision_in_dim = params.vision_in_dim
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if params.hidden_size % params.num_heads != 0:
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raise ValueError(
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
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)
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pe_dim = params.hidden_size // params.num_heads
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if sum(params.axes_dim) != pe_dim:
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raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
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self.hidden_size = params.hidden_size
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self.num_heads = params.num_heads
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self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
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self.img_in = PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=len(self.patch_size) == 3, dtype=dtype, device=device, operations=operations)
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self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
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if params.vec_in_dim is not None:
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self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
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else:
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self.vector_in = None
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self.guidance_in = (
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MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
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)
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self.txt_in = TokenRefiner(params.context_in_dim, self.hidden_size, self.num_heads, 2, dtype=dtype, device=device, operations=operations)
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self.double_blocks = nn.ModuleList(
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[
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DoubleStreamBlock(
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self.hidden_size,
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self.num_heads,
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mlp_ratio=params.mlp_ratio,
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qkv_bias=params.qkv_bias,
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flipped_img_txt=True,
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dtype=dtype, device=device, operations=operations
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)
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for _ in range(params.depth)
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]
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)
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self.single_blocks = nn.ModuleList(
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[
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SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
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for _ in range(params.depth_single_blocks)
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]
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)
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if params.byt5:
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self.byt5_in = ByT5Mapper(
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in_dim=1472,
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out_dim=2048,
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hidden_dim=2048,
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out_dim1=self.hidden_size,
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use_res=False,
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dtype=dtype, device=device, operations=operations
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)
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else:
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self.byt5_in = None
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if params.meanflow:
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self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
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else:
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self.time_r_in = None
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if final_layer:
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self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)
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# HunyuanVideo 1.5 specific modules
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if self.vision_in_dim is not None:
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from ..wan.model import MLPProj
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self.vision_in = MLPProj(in_dim=self.vision_in_dim, out_dim=self.hidden_size, operation_settings=operation_settings)
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else:
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self.vision_in = None
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if self.use_cond_type_embedding:
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# 0: text_encoder feature 1: byt5 feature 2: vision_encoder feature
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self.cond_type_embedding = nn.Embedding(3, self.hidden_size)
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else:
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self.cond_type_embedding = None
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def forward_orig(
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self,
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img: Tensor,
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img_ids: Tensor,
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txt: Tensor,
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txt_ids: Tensor,
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txt_mask: Tensor,
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timesteps: Tensor,
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y: Tensor = None,
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txt_byt5=None,
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clip_fea=None,
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guidance: Tensor = None,
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guiding_frame_index=None,
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ref_latent=None,
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disable_time_r=False,
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control=None,
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transformer_options={},
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) -> Tensor:
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patches_replace = transformer_options.get("patches_replace", {})
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initial_shape = list(img.shape)
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# running on sequences img
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img = self.img_in(img)
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vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))
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if (self.time_r_in is not None) and (not disable_time_r):
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w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0] # This most likely could be improved
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if len(w) > 0:
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timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
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timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
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vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
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vec = (vec + vec_r) if self.params.meanflow_sum else (vec + vec_r) / 2
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if ref_latent is not None:
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ref_latent_ids = self.img_ids(ref_latent)
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ref_latent = self.img_in(ref_latent)
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img = torch.cat([ref_latent, img], dim=-2)
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ref_latent_ids[..., 0] = -1
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ref_latent_ids[..., 2] += (initial_shape[-1] // self.patch_size[-1])
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img_ids = torch.cat([ref_latent_ids, img_ids], dim=-2)
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if guiding_frame_index is not None:
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token_replace_vec = self.time_in(timestep_embedding(guiding_frame_index, 256, time_factor=1.0))
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if self.vector_in is not None:
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vec_ = self.vector_in(y[:, :self.params.vec_in_dim])
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vec = torch.cat([(vec_ + token_replace_vec).unsqueeze(1), (vec_ + vec).unsqueeze(1)], dim=1)
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else:
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vec = torch.cat([(token_replace_vec).unsqueeze(1), (vec).unsqueeze(1)], dim=1)
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frame_tokens = (initial_shape[-1] // self.patch_size[-1]) * (initial_shape[-2] // self.patch_size[-2])
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modulation_dims = [(0, frame_tokens, 0), (frame_tokens, None, 1)]
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modulation_dims_txt = [(0, None, 1)]
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else:
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if self.vector_in is not None:
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vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
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modulation_dims = None
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modulation_dims_txt = None
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if self.params.guidance_embed:
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if guidance is not None:
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vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
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if txt_mask is not None and not torch.is_floating_point(txt_mask):
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txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
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txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options)
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if self.cond_type_embedding is not None:
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self.cond_type_embedding.to(txt.device)
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cond_emb = self.cond_type_embedding(torch.zeros_like(txt[:, :, 0], device=txt.device, dtype=torch.long))
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txt = txt + cond_emb.to(txt.dtype)
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if self.byt5_in is not None and txt_byt5 is not None:
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txt_byt5 = self.byt5_in(txt_byt5)
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if self.cond_type_embedding is not None:
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cond_emb = self.cond_type_embedding(torch.ones_like(txt_byt5[:, :, 0], device=txt_byt5.device, dtype=torch.long))
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txt_byt5 = txt_byt5 + cond_emb.to(txt_byt5.dtype)
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txt = torch.cat((txt_byt5, txt), dim=1) # byt5 first for HunyuanVideo1.5
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else:
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txt = torch.cat((txt, txt_byt5), dim=1)
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txt_byt5_ids = torch.zeros((txt_ids.shape[0], txt_byt5.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)
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txt_ids = torch.cat((txt_ids, txt_byt5_ids), dim=1)
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if clip_fea is not None:
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txt_vision_states = self.vision_in(clip_fea)
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if self.cond_type_embedding is not None:
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cond_emb = self.cond_type_embedding(2 * torch.ones_like(txt_vision_states[:, :, 0], dtype=torch.long, device=txt_vision_states.device))
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txt_vision_states = txt_vision_states + cond_emb
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txt = torch.cat((txt_vision_states.to(txt.dtype), txt), dim=1)
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extra_txt_ids = torch.zeros((txt_ids.shape[0], txt_vision_states.shape[1], txt_ids.shape[-1]), device=txt_ids.device, dtype=txt_ids.dtype)
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txt_ids = torch.cat((txt_ids, extra_txt_ids), dim=1)
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ids = torch.cat((img_ids, txt_ids), dim=1)
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pe = self.pe_embedder(ids)
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img_len = img.shape[1]
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if txt_mask is not None:
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attn_mask_len = img_len + txt.shape[1]
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attn_mask = torch.zeros((1, 1, attn_mask_len), dtype=img.dtype, device=img.device)
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attn_mask[:, 0, img_len:] = txt_mask
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else:
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attn_mask = None
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blocks_replace = patches_replace.get("dit", {})
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transformer_options["total_blocks"] = len(self.double_blocks)
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transformer_options["block_type"] = "double"
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for i, block in enumerate(self.double_blocks):
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transformer_options["block_index"] = i
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if ("double_block", i) in blocks_replace:
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def block_wrap_2(args):
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out = {}
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out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"], transformer_options=args["transformer_options"])
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return out
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out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {"original_block": block_wrap_2})
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txt = out["txt"]
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img = out["img"]
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else:
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img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options)
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if control is not None: # Controlnet
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control_i = control.get("input")
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if i < len(control_i):
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add = control_i[i]
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if add is not None:
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img += add
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img = torch.cat((img, txt), 1)
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transformer_options["total_blocks"] = len(self.single_blocks)
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transformer_options["block_type"] = "single"
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for i, block in enumerate(self.single_blocks):
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transformer_options["block_index"] = i
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if ("single_block", i) in blocks_replace:
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def block_wrap(args):
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out = {}
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out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"], transformer_options=args["transformer_options"])
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return out
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out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {"original_block": block_wrap})
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img = out["img"]
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else:
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img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options)
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|
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if control is not None: # Controlnet
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control_o = control.get("output")
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if i < len(control_o):
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add = control_o[i]
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if add is not None:
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img[:, : img_len] += add
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|
|
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img = img[:, : img_len]
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if ref_latent is not None:
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img = img[:, ref_latent.shape[1]:]
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|
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img = self.final_layer(img, vec, modulation_dims=modulation_dims) # (N, T, patch_size ** 2 * out_channels)
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|
|
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shape = initial_shape[-len(self.patch_size):]
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for i in range(len(shape)):
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shape[i] = shape[i] // self.patch_size[i]
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img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size)
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if img.ndim == 8:
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|
img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
|
|
img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
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else:
|
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img = img.permute(0, 3, 1, 4, 2, 5)
|
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img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3])
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|
return img
|
|
|
|
def img_ids(self, x):
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|
bs, c, t, h, w = x.shape
|
|
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)
|
|
return repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
|
|
|
|
def img_ids_2d(self, x):
|
|
bs, c, h, w = x.shape
|
|
patch_size = self.patch_size
|
|
h_len = ((h + (patch_size[0] // 2)) // patch_size[0])
|
|
w_len = ((w + (patch_size[1] // 2)) // patch_size[1])
|
|
img_ids = torch.zeros((h_len, w_len, 2), device=x.device, dtype=x.dtype)
|
|
img_ids[:, :, 0] = img_ids[:, :, 0] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
|
|
img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
|
|
return repeat(img_ids, "h w c -> b (h w) c", b=bs)
|
|
|
|
def forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
|
|
return WrapperExecutor.new_class_executor(
|
|
self._forward,
|
|
self,
|
|
get_all_wrappers(WrappersMP.DIFFUSION_MODEL, transformer_options)
|
|
).execute(x, timestep, context, y, txt_byt5, clip_fea, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs)
|
|
|
|
def _forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fea=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
|
|
bs = x.shape[0]
|
|
if len(self.patch_size) == 3:
|
|
img_ids = self.img_ids(x)
|
|
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
|
|
else:
|
|
img_ids = self.img_ids_2d(x)
|
|
txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype)
|
|
out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, clip_fea, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options)
|
|
return out
|