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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math
import types
from copy import deepcopy
from typing import List
import numpy as np
import torch
import torch.cuda.amp as amp
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import PeftAdapterMixin
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils import is_torch_version, logging
from einops import rearrange
from .attention_utils import attention
from .wan_animate_adapter import FaceAdapter, FaceEncoder
from .wan_animate_motion_encoder import Generator
from .wan_transformer3d import (Head, MLPProj, WanAttentionBlock, WanLayerNorm,
WanRMSNorm, WanSelfAttention,
WanTransformer3DModel, rope_apply,
sinusoidal_embedding_1d)
from ..utils import cfg_skip
class Wan2_2Transformer3DModel_Animate(WanTransformer3DModel):
# _no_split_modules = ['WanAnimateAttentionBlock']
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
patch_size=(1, 2, 2),
text_len=512,
in_dim=36,
dim=5120,
ffn_dim=13824,
freq_dim=256,
text_dim=4096,
out_dim=16,
num_heads=40,
num_layers=40,
window_size=(-1, -1),
qk_norm=True,
cross_attn_norm=True,
eps=1e-6,
motion_encoder_dim=512,
use_img_emb=True
):
model_type = "i2v" # TODO: Hard code for both preview and official versions.
super().__init__(model_type, patch_size, text_len, in_dim, dim, ffn_dim, freq_dim, text_dim, out_dim,
num_heads, num_layers, window_size, qk_norm, cross_attn_norm, eps)
self.motion_encoder_dim = motion_encoder_dim
self.use_img_emb = use_img_emb
self.pose_patch_embedding = nn.Conv3d(
16, dim, kernel_size=patch_size, stride=patch_size
)
# initialize weights
self.init_weights()
self.motion_encoder = Generator(size=512, style_dim=512, motion_dim=20)
self.face_adapter = FaceAdapter(
heads_num=self.num_heads,
hidden_dim=self.dim,
num_adapter_layers=self.num_layers // 5,
)
self.face_encoder = FaceEncoder(
in_dim=motion_encoder_dim,
hidden_dim=self.dim,
num_heads=4,
)
def after_patch_embedding(self, x: List[torch.Tensor], pose_latents, face_pixel_values):
pose_latents = [self.pose_patch_embedding(u.unsqueeze(0)) for u in pose_latents]
for x_, pose_latents_ in zip(x, pose_latents):
x_[:, :, 1:] += pose_latents_
b,c,T,h,w = face_pixel_values.shape
face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w")
encode_bs = 8
face_pixel_values_tmp = []
for i in range(math.ceil(face_pixel_values.shape[0]/encode_bs)):
face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i*encode_bs:(i+1)*encode_bs]))
motion_vec = torch.cat(face_pixel_values_tmp)
motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T)
motion_vec = self.face_encoder(motion_vec)
B, L, H, C = motion_vec.shape
pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec)
motion_vec = torch.cat([pad_face, motion_vec], dim=1)
return x, motion_vec
def after_transformer_block(self, block_idx, x, motion_vec, motion_masks=None):
if block_idx % 5 == 0:
use_context_parallel = self.sp_world_size > 1
adapter_args = [x, motion_vec, motion_masks, use_context_parallel, self.all_gather, self.sp_world_size, self.sp_world_rank]
residual_out = self.face_adapter.fuser_blocks[block_idx // 5](*adapter_args)
x = residual_out + x
return x
@cfg_skip()
def forward(
self,
x,
t,
clip_fea,
context,
seq_len,
y=None,
pose_latents=None,
face_pixel_values=None,
cond_flag=True
):
# params
device = self.patch_embedding.weight.device
dtype = x.dtype
if self.freqs.device != device and torch.device(type="meta") != device:
self.freqs = self.freqs.to(device)
if y is not None:
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
# embeddings
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)
grid_sizes = torch.stack(
[torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
x = [u.flatten(2).transpose(1, 2) for u in x]
seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
if self.sp_world_size > 1:
seq_len = int(math.ceil(seq_len / self.sp_world_size)) * self.sp_world_size
assert seq_lens.max() <= seq_len
x = torch.cat([
torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))],
dim=1) for u in x
])
# time embeddings
with amp.autocast(dtype=torch.float32):
e = self.time_embedding(
sinusoidal_embedding_1d(self.freq_dim, t).float()
)
e0 = self.time_projection(e).unflatten(1, (6, self.dim))
assert e.dtype == torch.float32 and e0.dtype == torch.float32
# context
context_lens = None
context = self.text_embedding(
torch.stack([
torch.cat(
[u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
for u in context
]))
if self.use_img_emb:
context_clip = self.img_emb(clip_fea) # bs x 257 x dim
context = torch.concat([context_clip, context], dim=1)
# Context Parallel
if self.sp_world_size > 1:
x = torch.chunk(x, self.sp_world_size, dim=1)[self.sp_world_rank]
if t.dim() != 1:
e0 = torch.chunk(e0, self.sp_world_size, dim=1)[self.sp_world_rank]
e = torch.chunk(e, self.sp_world_size, dim=1)[self.sp_world_rank]
# TeaCache
if self.teacache is not None:
if cond_flag:
if t.dim() != 1:
modulated_inp = e0[0][:, -1, :]
else:
modulated_inp = e0[0]
skip_flag = self.teacache.cnt < self.teacache.num_skip_start_steps
if skip_flag:
self.should_calc = True
self.teacache.accumulated_rel_l1_distance = 0
else:
if cond_flag:
rel_l1_distance = self.teacache.compute_rel_l1_distance(self.teacache.previous_modulated_input, modulated_inp)
self.teacache.accumulated_rel_l1_distance += self.teacache.rescale_func(rel_l1_distance)
if self.teacache.accumulated_rel_l1_distance < self.teacache.rel_l1_thresh:
self.should_calc = False
else:
self.should_calc = True
self.teacache.accumulated_rel_l1_distance = 0
self.teacache.previous_modulated_input = modulated_inp
self.teacache.should_calc = self.should_calc
else:
self.should_calc = self.teacache.should_calc
# TeaCache
if self.teacache is not None:
if not self.should_calc:
previous_residual = self.teacache.previous_residual_cond if cond_flag else self.teacache.previous_residual_uncond
x = x + previous_residual.to(x.device)[-x.size()[0]:,]
else:
ori_x = x.clone().cpu() if self.teacache.offload else x.clone()
for idx, block in enumerate(self.blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
x,
e0,
seq_lens,
grid_sizes,
self.freqs,
context,
context_lens,
dtype,
t,
**ckpt_kwargs,
)
x, motion_vec = x.to(dtype), motion_vec.to(dtype)
x = self.after_transformer_block(idx, x, motion_vec)
else:
# arguments
kwargs = dict(
e=e0,
seq_lens=seq_lens,
grid_sizes=grid_sizes,
freqs=self.freqs,
context=context,
context_lens=context_lens,
dtype=dtype,
t=t
)
x = block(x, **kwargs)
x, motion_vec = x.to(dtype), motion_vec.to(dtype)
x = self.after_transformer_block(idx, x, motion_vec)
if cond_flag:
self.teacache.previous_residual_cond = x.cpu() - ori_x if self.teacache.offload else x - ori_x
else:
self.teacache.previous_residual_uncond = x.cpu() - ori_x if self.teacache.offload else x - ori_x
else:
for idx, block in enumerate(self.blocks):
if torch.is_grad_enabled() and self.gradient_checkpointing:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs)
return custom_forward
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block),
x,
e0,
seq_lens,
grid_sizes,
self.freqs,
context,
context_lens,
dtype,
t,
**ckpt_kwargs,
)
x, motion_vec = x.to(dtype), motion_vec.to(dtype)
x = self.after_transformer_block(idx, x, motion_vec)
else:
# arguments
kwargs = dict(
e=e0,
seq_lens=seq_lens,
grid_sizes=grid_sizes,
freqs=self.freqs,
context=context,
context_lens=context_lens,
dtype=dtype,
t=t
)
x = block(x, **kwargs)
x, motion_vec = x.to(dtype), motion_vec.to(dtype)
x = self.after_transformer_block(idx, x, motion_vec)
# head
x = self.head(x, e)
# Context Parallel
if self.sp_world_size > 1:
x = self.all_gather(x.contiguous(), dim=1)
# unpatchify
x = self.unpatchify(x, grid_sizes)
x = torch.stack(x)
return x |