Basic Hunyuan Video model support.
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comfyanonymous
330
comfy/ldm/hunyuan_video/model.py
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330
comfy/ldm/hunyuan_video/model.py
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#Based on Flux code because of weird hunyuan video code license.
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import torch
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import comfy.ldm.flux.layers
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import comfy.ldm.modules.diffusionmodules.mmdit
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from comfy.ldm.modules.attention import optimized_attention
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from dataclasses import dataclass
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from einops import repeat
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from torch import Tensor, nn
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from comfy.ldm.flux.layers import (
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DoubleStreamBlock,
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EmbedND,
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LastLayer,
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MLPEmbedder,
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SingleStreamBlock,
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timestep_embedding
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)
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import comfy.ldm.common_dit
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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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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):
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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)
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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):
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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)
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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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):
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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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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)
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return x
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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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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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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 = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(None, self.patch_size, self.in_channels, self.hidden_size, conv3d=True, 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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self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
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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 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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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,
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guidance: Tensor = None,
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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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vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
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if self.params.guidance_embed:
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if guidance is None:
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raise ValueError("Didn't get guidance strength for guidance distilled model.")
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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)
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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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for i, block in enumerate(self.double_blocks):
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if ("double_block", i) in blocks_replace:
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def block_wrap(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"])
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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}, {"original_block": block_wrap})
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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)
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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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for i, block in enumerate(self.single_blocks):
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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"])
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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}, {"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)
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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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img = img[:, : img_len]
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img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
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shape = initial_shape[-3:]
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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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img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
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img = img.reshape(initial_shape)
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return img
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def forward(self, x, timestep, context, y, guidance, attention_mask=None, control=None, transformer_options={}, **kwargs):
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bs, c, t, h, w = x.shape
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patch_size = self.patch_size
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t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
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h_len = ((h + (patch_size[1] // 2)) // patch_size[1])
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w_len = ((w + (patch_size[2] // 2)) // patch_size[2])
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img_ids = torch.zeros((t_len, h_len, w_len, 3), device=x.device, dtype=x.dtype)
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img_ids[:, :, :, 0] = img_ids[:, :, :, 0] + torch.linspace(0, t_len - 1, steps=t_len, device=x.device, dtype=x.dtype).reshape(-1, 1, 1)
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img_ids[:, :, :, 1] = img_ids[:, :, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).reshape(1, -1, 1)
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img_ids[:, :, :, 2] = img_ids[:, :, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).reshape(1, 1, -1)
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img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
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txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
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out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, control, transformer_options)
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return out
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