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#Based on Flux code because of weird hunyuan video code license.
import torch
import comfy.ldm.flux.layers
import comfy.ldm.modules.diffusionmodules.mmdit
from comfy.ldm.modules.attention import optimized_attention
from dataclasses import dataclass
from einops import repeat
from torch import Tensor, nn
from comfy.ldm.flux.layers import (
DoubleStreamBlock,
EmbedND,
LastLayer,
MLPEmbedder,
SingleStreamBlock,
timestep_embedding
)
import comfy.ldm.common_dit
@dataclass
class HunyuanVideoParams:
in_channels: int
out_channels: int
vec_in_dim: int
context_in_dim: int
hidden_size: int
mlp_ratio: float
num_heads: int
depth: int
depth_single_blocks: int
axes_dim: list
theta: int
patch_size: list
qkv_bias: bool
guidance_embed: bool
class SelfAttentionRef(nn.Module):
def __init__(self, dim: int, qkv_bias: bool = False, dtype=None, device=None, operations=None):
super().__init__()
self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
class TokenRefinerBlock(nn.Module):
def __init__(
self,
hidden_size,
heads,
dtype=None,
device=None,
operations=None
):
super().__init__()
self.heads = heads
mlp_hidden_dim = hidden_size * 4
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device),
)
self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
self.self_attn = SelfAttentionRef(hidden_size, True, dtype=dtype, device=device, operations=operations)
self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
self.mlp = nn.Sequential(
operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
nn.SiLU(),
operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
)
def forward(self, x, c, mask):
mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1)
norm_x = self.norm1(x)
qkv = self.self_attn.qkv(norm_x)
q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4)
attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True)
x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1)
x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1)
return x
class IndividualTokenRefiner(nn.Module):
def __init__(
self,
hidden_size,
heads,
num_blocks,
dtype=None,
device=None,
operations=None
):
super().__init__()
self.blocks = nn.ModuleList(
[
TokenRefinerBlock(
hidden_size=hidden_size,
heads=heads,
dtype=dtype,
device=device,
operations=operations
)
for _ in range(num_blocks)
]
)
def forward(self, x, c, mask):
m = None
if mask is not None:
m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1)
m = m + m.transpose(2, 3)
for block in self.blocks:
x = block(x, c, m)
return x
class TokenRefiner(nn.Module):
def __init__(
self,
text_dim,
hidden_size,
heads,
num_blocks,
dtype=None,
device=None,
operations=None
):
super().__init__()
self.input_embedder = operations.Linear(text_dim, hidden_size, bias=True, dtype=dtype, device=device)
self.t_embedder = MLPEmbedder(256, hidden_size, dtype=dtype, device=device, operations=operations)
self.c_embedder = MLPEmbedder(text_dim, hidden_size, dtype=dtype, device=device, operations=operations)
self.individual_token_refiner = IndividualTokenRefiner(hidden_size, heads, num_blocks, dtype=dtype, device=device, operations=operations)
def forward(
self,
x,
timesteps,
mask,
):
t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype))
# m = mask.float().unsqueeze(-1)
# 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
c = x.sum(dim=1) / x.shape[1]
c = t + self.c_embedder(c.to(x.dtype))
x = self.input_embedder(x)
x = self.individual_token_refiner(x, c, mask)
return x
class HunyuanVideo(nn.Module):
"""
Transformer model for flow matching on sequences.
"""
def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
super().__init__()
self.dtype = dtype
params = HunyuanVideoParams(**kwargs)
self.params = params
self.patch_size = params.patch_size
self.in_channels = params.in_channels
self.out_channels = params.out_channels
if params.hidden_size % params.num_heads != 0:
raise ValueError(
f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
)
pe_dim = params.hidden_size // params.num_heads
if sum(params.axes_dim) != pe_dim:
raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
self.hidden_size = params.hidden_size
self.num_heads = params.num_heads
self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
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)
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
self.guidance_in = (
MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
)
self.txt_in = TokenRefiner(params.context_in_dim, self.hidden_size, self.num_heads, 2, dtype=dtype, device=device, operations=operations)
self.double_blocks = nn.ModuleList(
[
DoubleStreamBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=params.mlp_ratio,
qkv_bias=params.qkv_bias,
flipped_img_txt=True,
dtype=dtype, device=device, operations=operations
)
for _ in range(params.depth)
]
)
self.single_blocks = nn.ModuleList(
[
SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
for _ in range(params.depth_single_blocks)
]
)
if final_layer:
self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)
def forward_orig(
self,
img: Tensor,
img_ids: Tensor,
txt: Tensor,
txt_ids: Tensor,
txt_mask: Tensor,
timesteps: Tensor,
y: Tensor,
guidance: Tensor = None,
control=None,
transformer_options={},
) -> Tensor:
patches_replace = transformer_options.get("patches_replace", {})
initial_shape = list(img.shape)
# running on sequences img
img = self.img_in(img)
vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))
vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
if self.params.guidance_embed:
if guidance is not None:
vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
if txt_mask is not None and not torch.is_floating_point(txt_mask):
txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
txt = self.txt_in(txt, timesteps, txt_mask)
ids = torch.cat((img_ids, txt_ids), dim=1)
pe = self.pe_embedder(ids)
img_len = img.shape[1]
if txt_mask is not None:
attn_mask_len = img_len + txt.shape[1]
attn_mask = torch.zeros((1, 1, attn_mask_len), dtype=img.dtype, device=img.device)
attn_mask[:, 0, img_len:] = txt_mask
else:
attn_mask = None
blocks_replace = patches_replace.get("dit", {})
for i, block in enumerate(self.double_blocks):
if ("double_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"])
return out
out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask}, {"original_block": block_wrap})
txt = out["txt"]
img = out["img"]
else:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask)
if control is not None: # Controlnet
control_i = control.get("input")
if i < len(control_i):
add = control_i[i]
if add is not None:
img += add
img = torch.cat((img, txt), 1)
for i, block in enumerate(self.single_blocks):
if ("single_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"])
return out
out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask}, {"original_block": block_wrap})
img = out["img"]
else:
img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
if control is not None: # Controlnet
control_o = control.get("output")
if i < len(control_o):
add = control_o[i]
if add is not None:
img[:, : img_len] += add
img = img[:, : img_len]
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
shape = initial_shape[-3:]
for i in range(len(shape)):
shape[i] = shape[i] // self.patch_size[i]
img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size)
img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
img = img.reshape(initial_shape)
return img
def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, control=None, transformer_options={}, **kwargs):
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)
img_ids = repeat(img_ids, "t h w c -> b (t h w) c", b=bs)
txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, guidance, control, transformer_options)
return out
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