# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math
import numpy as np
import os
import torch
import torch.cuda.amp as amp
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from diffusers import ModelMixin
from diffusers.configuration_utils import ConfigMixin, register_to_config
from .attention import flash_attention, SingleStreamMutiAttention
from ..utils.multitalk_utils import get_attn_map_with_target
__all__ = ['WanModel']
def sinusoidal_embedding_1d(dim, position):
# preprocess
assert dim % 2 == 0
half = dim // 2
position = position.type(torch.float64)
# calculation
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
@amp.autocast(enabled=False)
def rope_params(max_seq_len, dim, theta=10000):
assert dim % 2 == 0
freqs = torch.outer(
torch.arange(max_seq_len),
1.0 / torch.pow(theta,
torch.arange(0, dim, 2).to(torch.float64).div(dim)))
freqs = torch.polar(torch.ones_like(freqs), freqs)
return freqs
@amp.autocast(enabled=False)
def rope_apply(x, grid_sizes, freqs):
s, n, c = x.size(1), x.size(2), x.size(3) // 2
freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
output = []
for i, (f, h, w) in enumerate(grid_sizes.tolist()):
seq_len = f * h * w
x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape(
s, n, -1, 2))
freqs_i = torch.cat([
freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
],
dim=-1).reshape(seq_len, 1, -1)
freqs_i = freqs_i.to(device=x_i.device)
x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
x_i = torch.cat([x_i, x[i, seq_len:]])
output.append(x_i)
return torch.stack(output).float()
class WanRMSNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.dim = dim
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
r"""
Args:
x(Tensor): Shape [B, L, C]
"""
return self._norm(x.float()).type_as(x) * self.weight
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
class WanLayerNorm(nn.LayerNorm):
def __init__(self, dim, eps=1e-6, elementwise_affine=False):
super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
origin_dtype = inputs.dtype
out = F.layer_norm(
inputs.float(),
self.normalized_shape,
None if self.weight is None else self.weight.float(),
None if self.bias is None else self.bias.float() ,
self.eps
).to(origin_dtype)
return out
class WanSelfAttention(nn.Module):
def __init__(self,
dim,
num_heads,
window_size=(-1, -1),
qk_norm=True,
eps=1e-6):
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
# layers
self.q = nn.Linear(dim, dim)
self.k = nn.Linear(dim, dim)
self.v = nn.Linear(dim, dim)
self.o = nn.Linear(dim, dim)
self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
def forward(self, x, seq_lens, grid_sizes, freqs, ref_target_masks=None):
b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
# query, key, value function
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 = rope_apply(q, grid_sizes, freqs)
k = rope_apply(k, grid_sizes, freqs)
x = flash_attention(
q=q,
k=k,
v=v,
k_lens=seq_lens,
window_size=self.window_size
).type_as(x)
# output
x = x.flatten(2)
x = self.o(x)
with torch.no_grad():
x_ref_attn_map = get_attn_map_with_target(q.type_as(x), k.type_as(x), grid_sizes[0],
ref_target_masks=ref_target_masks)
return x, x_ref_attn_map
class WanI2VCrossAttention(WanSelfAttention):
def __init__(self,
dim,
num_heads,
window_size=(-1, -1),
qk_norm=True,
eps=1e-6):
super().__init__(dim, num_heads, window_size, qk_norm, eps)
self.k_img = nn.Linear(dim, dim)
self.v_img = nn.Linear(dim, dim)
self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
def forward(self, x, context, context_lens):
context_img = context[:, :257]
context = context[:, 257:]
b, n, d = x.size(0), self.num_heads, self.head_dim
# compute query, key, value
q = self.norm_q(self.q(x)).view(b, -1, n, d)
k = self.norm_k(self.k(context)).view(b, -1, n, d)
v = self.v(context).view(b, -1, n, d)
k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d)
v_img = self.v_img(context_img).view(b, -1, n, d)
img_x = flash_attention(q, k_img, v_img, k_lens=None)
# compute attention
x = flash_attention(q, k, v, k_lens=context_lens)
# output
x = x.flatten(2)
img_x = img_x.flatten(2)
x = x + img_x
x = self.o(x)
return x
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,
output_dim=768,
norm_input_visual=True,
class_range=24,
class_interval=4):
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
# layers
self.norm1 = WanLayerNorm(dim, eps)
self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps)
self.norm3 = WanLayerNorm(
dim, eps,
elementwise_affine=True) if cross_attn_norm else nn.Identity()
self.cross_attn = WanI2VCrossAttention(dim,
num_heads,
(-1, -1),
qk_norm,
eps)
self.norm2 = WanLayerNorm(dim, eps)
self.ffn = nn.Sequential(
nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
nn.Linear(ffn_dim, dim))
# modulation
self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
# init audio module
self.audio_cross_attn = SingleStreamMutiAttention(
dim=dim,
encoder_hidden_states_dim=output_dim,
num_heads=num_heads,
qk_norm=False,
qkv_bias=True,
eps=eps,
norm_layer=WanRMSNorm,
class_range=class_range,
class_interval=class_interval
)
self.norm_x = WanLayerNorm(dim, eps, elementwise_affine=True) if norm_input_visual else nn.Identity()
def forward(
self,
x,
e,
seq_lens,
grid_sizes,
freqs,
context,
context_lens,
audio_embedding=None,
ref_target_masks=None,
human_num=None,
):
dtype = x.dtype
assert e.dtype == torch.float32
with amp.autocast(dtype=torch.float32):
e = (self.modulation.to(e.device) + e).chunk(6, dim=1)
assert e[0].dtype == torch.float32
# self-attention
y, x_ref_attn_map = self.self_attn(
(self.norm1(x).float() * (1 + e[1]) + e[0]).type_as(x), seq_lens, grid_sizes,
freqs, ref_target_masks=ref_target_masks)
with amp.autocast(dtype=torch.float32):
x = x + y * e[2]
x = x.to(dtype)
# cross-attention of text
x = x + self.cross_attn(self.norm3(x), context, context_lens)
# cross attn of audio
x_a = self.audio_cross_attn(self.norm_x(x), encoder_hidden_states=audio_embedding,
shape=grid_sizes[0], x_ref_attn_map=x_ref_attn_map, human_num=human_num)
x = x + x_a
y = self.ffn((self.norm2(x).float() * (1 + e[4]) + e[3]).to(dtype))
with amp.autocast(dtype=torch.float32):
x = x + y * e[5]
x = x.to(dtype)
return x
class Head(nn.Module):
def __init__(self, dim, out_dim, patch_size, eps=1e-6):
super().__init__()
self.dim = dim
self.out_dim = out_dim
self.patch_size = patch_size
self.eps = eps
# layers
out_dim = math.prod(patch_size) * out_dim
self.norm = WanLayerNorm(dim, eps)
self.head = nn.Linear(dim, out_dim)
# modulation
self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
def forward(self, x, e):
r"""
Args:
x(Tensor): Shape [B, L1, C]
e(Tensor): Shape [B, C]
"""
assert e.dtype == torch.float32
with amp.autocast(dtype=torch.float32):
e = (self.modulation.to(e.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):
super().__init__()
self.proj = torch.nn.Sequential(
torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim),
torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim),
torch.nn.LayerNorm(out_dim))
def forward(self, image_embeds):
clip_extra_context_tokens = self.proj(image_embeds)
return clip_extra_context_tokens
class AudioProjModel(ModelMixin, ConfigMixin):
def __init__(
self,
seq_len=5,
seq_len_vf=12,
blocks=12,
channels=768,
intermediate_dim=512,
output_dim=768,
context_tokens=32,
norm_output_audio=False,
):
super().__init__()
self.seq_len = seq_len
self.blocks = blocks
self.channels = channels
self.input_dim = seq_len * blocks * channels
self.input_dim_vf = seq_len_vf * blocks * channels
self.intermediate_dim = intermediate_dim
self.context_tokens = context_tokens
self.output_dim = output_dim
# define multiple linear layers
self.proj1 = nn.Linear(self.input_dim, intermediate_dim)
self.proj1_vf = nn.Linear(self.input_dim_vf, intermediate_dim)
self.proj2 = nn.Linear(intermediate_dim, intermediate_dim)
self.proj3 = nn.Linear(intermediate_dim, context_tokens * output_dim)
self.norm = nn.LayerNorm(output_dim) if norm_output_audio else nn.Identity()
def forward(self, audio_embeds, audio_embeds_vf):
video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1]
B, _, _, S, C = audio_embeds.shape
# process audio of first frame
audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c")
batch_size, window_size, blocks, channels = audio_embeds.shape
audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)
# process audio of latter frame
audio_embeds_vf = rearrange(audio_embeds_vf, "bz f w b c -> (bz f) w b c")
batch_size_vf, window_size_vf, blocks_vf, channels_vf = audio_embeds_vf.shape
audio_embeds_vf = audio_embeds_vf.view(batch_size_vf, window_size_vf * blocks_vf * channels_vf)
# first projection
audio_embeds = torch.relu(self.proj1(audio_embeds))
audio_embeds_vf = torch.relu(self.proj1_vf(audio_embeds_vf))
audio_embeds = rearrange(audio_embeds, "(bz f) c -> bz f c", bz=B)
audio_embeds_vf = rearrange(audio_embeds_vf, "(bz f) c -> bz f c", bz=B)
audio_embeds_c = torch.concat([audio_embeds, audio_embeds_vf], dim=1)
batch_size_c, N_t, C_a = audio_embeds_c.shape
audio_embeds_c = audio_embeds_c.view(batch_size_c*N_t, C_a)
# second projection
audio_embeds_c = torch.relu(self.proj2(audio_embeds_c))
context_tokens = self.proj3(audio_embeds_c).reshape(batch_size_c*N_t, self.context_tokens, self.output_dim)
# normalization and reshape
context_tokens = self.norm(context_tokens)
context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length)
return context_tokens
class WanModel(ModelMixin, ConfigMixin):
r"""
Wan diffusion backbone supporting both text-to-video and image-to-video.
"""
ignore_for_config = [
'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size'
]
_no_split_modules = ['WanAttentionBlock']
@register_to_config
def __init__(self,
model_type='i2v',
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,
# audio params
audio_window=5,
intermediate_dim=512,
output_dim=768,
context_tokens=32,
vae_scale=4, # vae timedownsample scale
norm_input_visual=True,
norm_output_audio=True):
super().__init__()
assert model_type == 'i2v', 'MultiTalk model requires your model_type is 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.norm_output_audio = norm_output_audio
self.audio_window = audio_window
self.intermediate_dim = intermediate_dim
self.vae_scale = vae_scale
# embeddings
self.patch_embedding = nn.Conv3d(
in_dim, dim, kernel_size=patch_size, stride=patch_size)
self.text_embedding = nn.Sequential(
nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'),
nn.Linear(dim, dim))
self.time_embedding = nn.Sequential(
nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
# blocks
cross_attn_type = 'i2v_cross_attn'
self.blocks = nn.ModuleList([
WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
window_size, qk_norm, cross_attn_norm, eps,
output_dim=output_dim, norm_input_visual=norm_input_visual)
for _ in range(num_layers)
])
# head
self.head = Head(dim, out_dim, patch_size, eps)
assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
d = dim // num_heads
self.freqs = torch.cat([
rope_params(1024, d - 4 * (d // 6)),
rope_params(1024, 2 * (d // 6)),
rope_params(1024, 2 * (d // 6))
],
dim=1)
if model_type == 'i2v':
self.img_emb = MLPProj(1280, dim)
else:
raise NotImplementedError('Not supported model type.')
# init audio adapter
self.audio_proj = AudioProjModel(
seq_len=audio_window,
seq_len_vf=audio_window+vae_scale-1,
intermediate_dim=intermediate_dim,
output_dim=output_dim,
context_tokens=context_tokens,
norm_output_audio=norm_output_audio,
)
# initialize weights
self.init_weights()
def teacache_init(
self,
use_ret_steps=True,
teacache_thresh=0.2,
sample_steps=40,
model_scale='multitalk-480',
):
print("teacache_init")
self.enable_teacache = True
self.__class__.cnt = 0
self.__class__.num_steps = sample_steps*3
self.__class__.teacache_thresh = teacache_thresh
self.__class__.accumulated_rel_l1_distance_even = 0
self.__class__.accumulated_rel_l1_distance_odd = 0
self.__class__.previous_e0_even = None
self.__class__.previous_e0_odd = None
self.__class__.previous_residual_even = None
self.__class__.previous_residual_odd = None
self.__class__.use_ret_steps = use_ret_steps
if use_ret_steps:
if model_scale == 'multitalk-480':
self.__class__.coefficients = [ 2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01]
if model_scale == 'multitalk-720':
self.__class__.coefficients = [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02]
self.__class__.ret_steps = 5*3
self.__class__.cutoff_steps = sample_steps*3
else:
if model_scale == 'multitalk-480':
self.__class__.coefficients = [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01]
if model_scale == 'multitalk-720':
self.__class__.coefficients = [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683]
self.__class__.ret_steps = 1*3
self.__class__.cutoff_steps = sample_steps*3 - 3
print("teacache_init done")
def disable_teacache(self):
self.enable_teacache = False
def forward(
self,
x,
t,
context,
seq_len,
clip_fea=None,
y=None,
audio=None,
ref_target_masks=None,
):
assert clip_fea is not None and y is not None
_, T, H, W = x[0].shape
N_t = T // self.patch_size[0]
N_h = H // self.patch_size[1]
N_w = W // self.patch_size[2]
if y is not None:
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
x[0] = x[0].to(context[0].dtype)
# embeddings
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
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)
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
# text embedding
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
]))
# clip embedding
if clip_fea is not None:
context_clip = self.img_emb(clip_fea)
context = torch.concat([context_clip, context], dim=1).to(x.dtype)
audio_cond = audio.to(device=x.device, dtype=x.dtype)
first_frame_audio_emb_s = audio_cond[:, :1, ...]
latter_frame_audio_emb = audio_cond[:, 1:, ...]
latter_frame_audio_emb = rearrange(latter_frame_audio_emb, "b (n_t n) w s c -> b n_t n w s c", n=self.vae_scale)
middle_index = self.audio_window // 2
latter_first_frame_audio_emb = latter_frame_audio_emb[:, :, :1, :middle_index+1, ...]
latter_first_frame_audio_emb = rearrange(latter_first_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
latter_last_frame_audio_emb = latter_frame_audio_emb[:, :, -1:, middle_index:, ...]
latter_last_frame_audio_emb = rearrange(latter_last_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
latter_middle_frame_audio_emb = latter_frame_audio_emb[:, :, 1:-1, middle_index:middle_index+1, ...]
latter_middle_frame_audio_emb = rearrange(latter_middle_frame_audio_emb, "b n_t n w s c -> b n_t (n w) s c")
latter_frame_audio_emb_s = torch.concat([latter_first_frame_audio_emb, latter_middle_frame_audio_emb, latter_last_frame_audio_emb], dim=2)
audio_embedding = self.audio_proj(first_frame_audio_emb_s, latter_frame_audio_emb_s)
human_num = len(audio_embedding)
audio_embedding = torch.concat(audio_embedding.split(1), dim=2).to(x.dtype)
# convert ref_target_masks to token_ref_target_masks
if ref_target_masks is not None:
ref_target_masks = ref_target_masks.unsqueeze(0).to(torch.float32)
token_ref_target_masks = nn.functional.interpolate(ref_target_masks, size=(N_h, N_w), mode='nearest')
token_ref_target_masks = token_ref_target_masks.squeeze(0)
token_ref_target_masks = (token_ref_target_masks > 0)
token_ref_target_masks = token_ref_target_masks.view(token_ref_target_masks.shape[0], -1)
token_ref_target_masks = token_ref_target_masks.to(x.dtype)
# teacache
if self.enable_teacache:
modulated_inp = e0 if self.use_ret_steps else e
if self.cnt%3==0: # cond
if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:
should_calc_cond = True
self.accumulated_rel_l1_distance_cond = 0
else:
rescale_func = np.poly1d(self.coefficients)
self.accumulated_rel_l1_distance_cond += rescale_func(((modulated_inp-self.previous_e0_cond).abs().mean() / self.previous_e0_cond.abs().mean()).cpu().item())
if self.accumulated_rel_l1_distance_cond < self.teacache_thresh:
should_calc_cond = False
else:
should_calc_cond = True
self.accumulated_rel_l1_distance_cond = 0
self.previous_e0_cond = modulated_inp.clone()
elif self.cnt%3==1: # drop_text
if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:
should_calc_drop_text = True
self.accumulated_rel_l1_distance_drop_text = 0
else:
rescale_func = np.poly1d(self.coefficients)
self.accumulated_rel_l1_distance_drop_text += rescale_func(((modulated_inp-self.previous_e0_drop_text).abs().mean() / self.previous_e0_drop_text.abs().mean()).cpu().item())
if self.accumulated_rel_l1_distance_drop_text < self.teacache_thresh:
should_calc_drop_text = False
else:
should_calc_drop_text = True
self.accumulated_rel_l1_distance_drop_text = 0
self.previous_e0_drop_text = modulated_inp.clone()
else: # uncond
if self.cnt < self.ret_steps or self.cnt >= self.cutoff_steps:
should_calc_uncond = True
self.accumulated_rel_l1_distance_uncond = 0
else:
rescale_func = np.poly1d(self.coefficients)
self.accumulated_rel_l1_distance_uncond += rescale_func(((modulated_inp-self.previous_e0_uncond).abs().mean() / self.previous_e0_uncond.abs().mean()).cpu().item())
if self.accumulated_rel_l1_distance_uncond < self.teacache_thresh:
should_calc_uncond = False
else:
should_calc_uncond = True
self.accumulated_rel_l1_distance_uncond = 0
self.previous_e0_uncond = modulated_inp.clone()
# arguments
kwargs = dict(
e=e0,
seq_lens=seq_lens,
grid_sizes=grid_sizes,
freqs=self.freqs,
context=context,
context_lens=context_lens,
audio_embedding=audio_embedding,
ref_target_masks=token_ref_target_masks,
human_num=human_num,
)
if self.enable_teacache:
if self.cnt%3==0:
if not should_calc_cond:
x += self.previous_residual_cond
else:
ori_x = x.clone()
for block in self.blocks:
x = block(x, **kwargs)
self.previous_residual_cond = x - ori_x
elif self.cnt%3==1:
if not should_calc_drop_text:
x += self.previous_residual_drop_text
else:
ori_x = x.clone()
for block in self.blocks:
x = block(x, **kwargs)
self.previous_residual_drop_text = x - ori_x
else:
if not should_calc_uncond:
x += self.previous_residual_uncond
else:
ori_x = x.clone()
for block in self.blocks:
x = block(x, **kwargs)
self.previous_residual_uncond = x - ori_x
else:
for block in self.blocks:
x = block(x, **kwargs)
# head
x = self.head(x, e)
# unpatchify
x = self.unpatchify(x, grid_sizes)
if self.enable_teacache:
self.cnt += 1
if self.cnt >= self.num_steps:
self.cnt = 0
return torch.stack(x).float()
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 [C_out, F, H / 8, W / 8]
"""
c = self.out_dim
out = []
for u, v in zip(x, grid_sizes.tolist()):
u = u[:math.prod(v)].view(*v, *self.patch_size, c)
u = torch.einsum('fhwpqrc->cfphqwr', u)
u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
out.append(u)
return out
def init_weights(self):
r"""
Initialize model parameters using Xavier initialization.
"""
# basic init
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
# init embeddings
nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
for m in self.text_embedding.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=.02)
for m in self.time_embedding.modules():
if isinstance(m, nn.Linear):
nn.init.normal_(m.weight, std=.02)
# init output layer
nn.init.zeros_(self.head.head.weight)