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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math
from einops import rearrange
import torch
import torch.cuda.amp as amp
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.models.modeling_utils import ModelMixin
import numpy as np
from typing import Union,Optional
from mmgp import offload
from .attention import pay_attention
from torch.backends.cuda import sdp_kernel
__all__ = ['WanModel']
def sinusoidal_embedding_1d(dim, position):
# preprocess
assert dim % 2 == 0
half = dim // 2
position = position.type(torch.float32)
# 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
def reshape_latent(latent, latent_frames):
if latent_frames == latent.shape[0]:
return latent
return latent.reshape(latent_frames, -1, latent.shape[-1] )
def identify_k( b: float, d: int, N: int):
"""
This function identifies the index of the intrinsic frequency component in a RoPE-based pre-trained diffusion transformer.
Args:
b (`float`): The base frequency for RoPE.
d (`int`): Dimension of the frequency tensor
N (`int`): the first observed repetition frame in latent space
Returns:
k (`int`): the index of intrinsic frequency component
N_k (`int`): the period of intrinsic frequency component in latent space
Example:
In HunyuanVideo, b=256 and d=16, the repetition occurs approximately 8s (N=48 in latent space).
k, N_k = identify_k(b=256, d=16, N=48)
In this case, the intrinsic frequency index k is 4, and the period N_k is 50.
"""
# Compute the period of each frequency in RoPE according to Eq.(4)
periods = []
for j in range(1, d // 2 + 1):
theta_j = 1.0 / (b ** (2 * (j - 1) / d))
N_j = round(2 * torch.pi / theta_j)
periods.append(N_j)
# Identify the intrinsic frequency whose period is closed to N（see Eq.(7)）
diffs = [abs(N_j - N) for N_j in periods]
k = diffs.index(min(diffs)) + 1
N_k = periods[k-1]
return k, N_k
def rope_params_riflex(max_seq_len, dim, theta=10000, L_test=30, k=6):
assert dim % 2 == 0
exponents = torch.arange(0, dim, 2, dtype=torch.float64).div(dim)
inv_theta_pow = 1.0 / torch.pow(theta, exponents)
inv_theta_pow[k-1] = 0.9 * 2 * torch.pi / L_test
freqs = torch.outer(torch.arange(max_seq_len), inv_theta_pow)
if True:
freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float() # [S, D]
freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float() # [S, D]
return (freqs_cos, freqs_sin)
else:
freqs = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2]
return freqs
def relative_l1_distance(last_tensor, current_tensor):
l1_distance = torch.abs(last_tensor - current_tensor).mean()
norm = torch.abs(last_tensor).mean()
relative_l1_distance = l1_distance / norm
return relative_l1_distance.to(torch.float32)
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]
"""
y = x.float()
y.pow_(2)
y = y.mean(dim=-1, keepdim=True)
y += self.eps
y.rsqrt_()
x *= y
x *= self.weight
return x
# return self._norm(x).type_as(x) * self.weight
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
def my_LayerNorm(norm, x):
y = x.float()
y_m = y.mean(dim=-1, keepdim=True)
y -= y_m
del y_m
y.pow_(2)
y = y.mean(dim=-1, keepdim=True)
y += norm.eps
y.rsqrt_()
x = x * y
return x
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, x):
r"""
Args:
x(Tensor): Shape [B, L, C]
"""
# return F.layer_norm(
# input, self.normalized_shape, self.weight, self.bias, self.eps
# )
y = super().forward(x)
x = y.type_as(x)
return x
# return super().forward(x).type_as(x)
from wan.modules.posemb_layers import apply_rotary_emb
class WanSelfAttention(nn.Module):
def __init__(self,
dim,
num_heads,
window_size=(-1, -1),
qk_norm=True,
eps=1e-6,
block_no=0):
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
self.block_no = block_no
# 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, xlist, grid_sizes, freqs, block_mask = None):
r"""
Args:
x(Tensor): Shape [B, L, num_heads, C / num_heads]
grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
"""
x = xlist[0]
xlist.clear()
b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
# query, key, value function
q = self.q(x)
self.norm_q(q)
q = q.view(b, s, n, d) # !!!
k = self.k(x)
self.norm_k(k)
k = k.view(b, s, n, d)
v = self.v(x).view(b, s, n, d)
del x
qklist = [q,k]
del q,k
q,k = apply_rotary_emb(qklist, freqs, head_first=False)
chipmunk = offload.shared_state.get("_chipmunk", False)
if chipmunk and self.__class__ == WanSelfAttention:
q = q.transpose(1,2)
k = k.transpose(1,2)
v = v.transpose(1,2)
attn_layers = offload.shared_state["_chipmunk_layers"]
x = attn_layers[self.block_no](q, k, v)
x = x.transpose(1,2)
elif block_mask == None:
qkv_list = [q,k,v]
del q,k,v
x = pay_attention(
qkv_list,
window_size=self.window_size)
else:
with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
x = (
torch.nn.functional.scaled_dot_product_attention(
q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), attn_mask=block_mask
)
.transpose(1, 2)
.contiguous()
)
del q,k,v
# if not self._flag_ar_attention:
# q = rope_apply(q, grid_sizes, freqs)
# k = rope_apply(k, grid_sizes, freqs)
# x = flash_attention(q=q, k=k, v=v, window_size=self.window_size)
# else:
# q = rope_apply(q, grid_sizes, freqs)
# k = rope_apply(k, grid_sizes, freqs)
# q = q.to(torch.bfloat16)
# k = k.to(torch.bfloat16)
# v = v.to(torch.bfloat16)
# with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
# x = (
# torch.nn.functional.scaled_dot_product_attention(
# q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), attn_mask=block_mask
# )
# .transpose(1, 2)
# .contiguous()
# )
# output
x = x.flatten(2)
x = self.o(x)
return x
class WanT2VCrossAttention(WanSelfAttention):
def forward(self, xlist, context, grid_sizes, *args, **kwargs):
r"""
Args:
x(Tensor): Shape [B, L1, C]
context(Tensor): Shape [B, L2, C]
"""
x = xlist[0]
xlist.clear()
b, n, d = x.size(0), self.num_heads, self.head_dim
# compute query, key, value
q = self.q(x)
del x
self.norm_q(q)
q= q.view(b, -1, n, d)
k = self.k(context)
self.norm_k(k)
k = k.view(b, -1, n, d)
v = self.v(context).view(b, -1, n, d)
# compute attention
v = v.contiguous().clone()
qvl_list=[q, k, v]
del q, k, v
x = pay_attention(qvl_list, cross_attn= True)
# output
x = x.flatten(2)
x = self.o(x)
return x
class WanI2VCrossAttention(WanSelfAttention):
def __init__(self,
dim,
num_heads,
window_size=(-1, -1),
qk_norm=True,
eps=1e-6,
block_no=0):
super().__init__(dim, num_heads, window_size, qk_norm, eps, block_no)
self.k_img = nn.Linear(dim, dim)
self.v_img = nn.Linear(dim, dim)
# self.alpha = nn.Parameter(torch.zeros((1, )))
self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
def forward(self, xlist, context, grid_sizes, audio_proj, audio_scale, audio_context_lens ):
r"""
Args:
x(Tensor): Shape [B, L1, C]
context(Tensor): Shape [B, L2, C]
"""
##### Enjoy this spagheti VRAM optimizations done by DeepBeepMeep !
# I am sure you are a nice person and as you copy this code, you will give me officially proper credits:
# Please link to https://github.com/deepbeepmeep/Wan2GP and @deepbeepmeep on twitter
x = xlist[0]
xlist.clear()
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.q(x)
del x
self.norm_q(q)
q= q.view(b, -1, n, d)
k = self.k(context)
self.norm_k(k)
k = k.view(b, -1, n, d)
v = self.v(context).view(b, -1, n, d)
qkv_list = [q, k, v]
del k,v
x = pay_attention(qkv_list)
if audio_scale != None:
audio_x = self.processor(q, audio_proj, grid_sizes[0], audio_context_lens)
k_img = self.k_img(context_img)
self.norm_k_img(k_img)
k_img = k_img.view(b, -1, n, d)
v_img = self.v_img(context_img).view(b, -1, n, d)
qkv_list = [q, k_img, v_img]
del q, k_img, v_img
img_x = pay_attention(qkv_list)
# compute attention
# output
x = x.flatten(2)
img_x = img_x.flatten(2)
x += img_x
del img_x
if audio_scale != None:
x.add_(audio_x, alpha= audio_scale)
x = self.o(x)
return x
WAN_CROSSATTENTION_CLASSES = {
't2v_cross_attn': WanT2VCrossAttention,
'i2v_cross_attn': WanI2VCrossAttention,
}
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,
block_id=None,
block_no = 0
):
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
self.block_no = block_no
# layers
self.norm1 = WanLayerNorm(dim, eps)
self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,
eps, block_no= block_no)
self.norm3 = WanLayerNorm(
dim, eps,
elementwise_affine=True) if cross_attn_norm else nn.Identity()
self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim,
num_heads,
(-1, -1),
qk_norm,
eps,
block_no)
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)
self.block_id = block_id
def forward(
self,
x,
e,
grid_sizes,
freqs,
context,
hints= None,
context_scale=[1.0],
cam_emb= None,
block_mask = None,
audio_proj= None,
audio_context_lens= None,
audio_scale=None,
):
r"""
Args:
x(Tensor): Shape [B, L, C]
e(Tensor): Shape [B, 6, C]
grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
"""
hints_processed = None
attention_dtype = self.self_attn.q.weight.dtype
dtype = x.dtype
if self.block_id is not None and hints is not None:
kwargs = {
"grid_sizes" : grid_sizes,
"freqs" :freqs,
"context" : context,
"e" : e,
}
hints_processed= []
for scale, hint in zip(context_scale, hints):
if scale == 0:
hints_processed.append(None)
else:
hints_processed.append(self.vace(hint, x, **kwargs) if self.block_id == 0 else self.vace(hint, None, **kwargs))
latent_frames = e.shape[0]
e = (self.modulation + e).chunk(6, dim=1)
# self-attention
x_mod = self.norm1(x)
x_mod = reshape_latent(x_mod , latent_frames)
x_mod *= 1 + e[1]
x_mod += e[0]
x_mod = reshape_latent(x_mod , 1)
if cam_emb != None:
cam_emb = self.cam_encoder(cam_emb)
cam_emb = cam_emb.repeat(1, 2, 1)
cam_emb = cam_emb.unsqueeze(2).unsqueeze(3).repeat(1, 1, grid_sizes[1], grid_sizes[2], 1)
cam_emb = rearrange(cam_emb, 'b f h w d -> b (f h w) d')
x_mod += cam_emb
xlist = [x_mod.to(attention_dtype)]
del x_mod
y = self.self_attn( xlist, grid_sizes, freqs, block_mask)
y = y.to(dtype)
if cam_emb != None:
y = self.projector(y)
x, y = reshape_latent(x , latent_frames), reshape_latent(y , latent_frames)
x.addcmul_(y, e[2])
x, y = reshape_latent(x , 1), reshape_latent(y , 1)
del y
y = self.norm3(x)
y = y.to(attention_dtype)
ylist= [y]
del y
x += self.cross_attn(ylist, context, grid_sizes, audio_proj, audio_scale, audio_context_lens).to(dtype)
y = self.norm2(x)
y = reshape_latent(y , latent_frames)
y *= 1 + e[4]
y += e[3]
y = reshape_latent(y , 1)
y = y.to(attention_dtype)
ffn = self.ffn[0]
gelu = self.ffn[1]
ffn2= self.ffn[2]
y_shape = y.shape
y = y.view(-1, y_shape[-1])
chunk_size = int(y_shape[1]/2.7)
chunks =torch.split(y, chunk_size)
for y_chunk in chunks:
mlp_chunk = ffn(y_chunk)
mlp_chunk = gelu(mlp_chunk)
y_chunk[...] = ffn2(mlp_chunk)
del mlp_chunk
y = y.view(y_shape)
y = y.to(dtype)
x, y = reshape_latent(x , latent_frames), reshape_latent(y , latent_frames)
x.addcmul_(y, e[5])
x, y = reshape_latent(x , 1), reshape_latent(y , 1)
if hints_processed is not None:
for hint, scale in zip(hints_processed, context_scale):
if scale != 0:
if scale == 1:
x.add_(hint)
else:
x.add_(hint, alpha= scale)
return x
class VaceWanAttentionBlock(WanAttentionBlock):
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,
block_id=0
):
super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps)
self.block_id = block_id
if block_id == 0:
self.before_proj = nn.Linear(self.dim, self.dim)
nn.init.zeros_(self.before_proj.weight)
nn.init.zeros_(self.before_proj.bias)
self.after_proj = nn.Linear(self.dim, self.dim)
nn.init.zeros_(self.after_proj.weight)
nn.init.zeros_(self.after_proj.bias)
def forward(self, hints, x, **kwargs):
# behold dbm magic !
c = hints[0]
hints[0] = None
if self.block_id == 0:
c = self.before_proj(c)
c += x
c = super().forward(c, **kwargs)
c_skip = self.after_proj(c)
hints[0] = c
return c_skip
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
dtype = x.dtype
latent_frames = e.shape[0]
e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1)
x = self.norm(x).to(dtype)
x = reshape_latent(x , latent_frames)
x *= (1 + e[1])
x += e[0]
x = reshape_latent(x , 1)
x= x.to(self.head.weight.dtype)
x = self.head(x)
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 WanModel(ModelMixin, ConfigMixin):
def setup_chipmunk(self):
# from chipmunk.util import LayerCounter
# from chipmunk.modules import SparseDiffMlp, SparseDiffAttn
seq_shape = (21, 45, 80)
chipmunk_layers =[]
for i in range(self.num_layers):
layer_num, layer_counter = LayerCounter.build_for_layer(is_attn_sparse=True, is_mlp_sparse=False)
chipmunk_layers.append( SparseDiffAttn(layer_num, layer_counter))
offload.shared_state["_chipmunk_layers"] = chipmunk_layers
chipmunk_layers[0].initialize_static_mask(
seq_shape=seq_shape,
txt_len=0,
local_heads_num=self.num_heads,
device='cuda'
)
chipmunk_layers[0].layer_counter.reset()
def release_chipmunk(self):
offload.shared_state["_chipmunk_layers"] = None
def preprocess_loras(self, model_type, sd):
first = next(iter(sd), None)
if first == None:
return sd
if first.startswith("lora_unet_"):
new_sd = {}
print("Converting Lora Safetensors format to Lora Diffusers format")
alphas = {}
repl_list = ["cross_attn", "self_attn", "ffn"]
src_list = ["_" + k + "_" for k in repl_list]
tgt_list = ["." + k + "." for k in repl_list]
for k,v in sd.items():
k = k.replace("lora_unet_blocks_","diffusion_model.blocks.")
for s,t in zip(src_list, tgt_list):
k = k.replace(s,t)
k = k.replace("lora_up","lora_B")
k = k.replace("lora_down","lora_A")
if "alpha" in k:
alphas[k] = v
else:
new_sd[k] = v
new_alphas = {}
for k,v in new_sd.items():
if "lora_B" in k:
dim = v.shape[1]
elif "lora_A" in k:
dim = v.shape[0]
else:
continue
alpha_key = k[:-len("lora_X.weight")] +"alpha"
if alpha_key in alphas:
scale = alphas[alpha_key] / dim
new_alphas[alpha_key] = scale
else:
print(f"Lora alpha'{alpha_key}' is missing")
new_sd.update(new_alphas)
sd = new_sd
from wgp import test_class_i2v
if not test_class_i2v(model_type):
new_sd = {}
# convert loras for i2v to t2v
for k,v in sd.items():
if any(layer in k for layer in ["cross_attn.k_img", "cross_attn.v_img"]):
continue
new_sd[k] = v
sd = new_sd
return sd
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,
vace_layers=None,
vace_in_dim=None,
model_type='t2v',
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,
recammaster = False,
inject_sample_info = False,
fantasytalking_dim = 0,
):
r"""
Initialize the diffusion model backbone.
Args:
model_type (`str`, *optional*, defaults to 't2v'):
Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
text_len (`int`, *optional*, defaults to 512):
Fixed length for text embeddings
in_dim (`int`, *optional*, defaults to 16):
Input video channels (C_in)
dim (`int`, *optional*, defaults to 2048):
Hidden dimension of the transformer
ffn_dim (`int`, *optional*, defaults to 8192):
Intermediate dimension in feed-forward network
freq_dim (`int`, *optional*, defaults to 256):
Dimension for sinusoidal time embeddings
text_dim (`int`, *optional*, defaults to 4096):
Input dimension for text embeddings
out_dim (`int`, *optional*, defaults to 16):
Output video channels (C_out)
num_heads (`int`, *optional*, defaults to 16):
Number of attention heads
num_layers (`int`, *optional*, defaults to 32):
Number of transformer blocks
window_size (`tuple`, *optional*, defaults to (-1, -1)):
Window size for local attention (-1 indicates global attention)
qk_norm (`bool`, *optional*, defaults to True):
Enable query/key normalization
cross_attn_norm (`bool`, *optional*, defaults to False):
Enable cross-attention normalization
eps (`float`, *optional*, defaults to 1e-6):
Epsilon value for normalization layers
"""
super().__init__()
assert model_type in ['t2v', '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.num_frame_per_block = 1
self.flag_causal_attention = False
self.block_mask = None
self.inject_sample_info = inject_sample_info
# 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))
if inject_sample_info:
self.fps_embedding = nn.Embedding(2, dim)
self.fps_projection = nn.Sequential(nn.Linear(dim, dim), nn.SiLU(), nn.Linear(dim, dim * 6))
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
if vace_layers == None:
cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
self.blocks = nn.ModuleList([
WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
window_size, qk_norm, cross_attn_norm, eps, block_no =i)
for i in range(num_layers)
])
# head
self.head = Head(dim, out_dim, patch_size, eps)
# buffers (don't use register_buffer otherwise dtype will be changed in to())
if model_type == 'i2v':
self.img_emb = MLPProj(1280, dim)
# initialize weights
self.init_weights()
if vace_layers != None:
self.vace_layers = [i for i in range(0, self.num_layers, 2)] if vace_layers is None else vace_layers
self.vace_in_dim = self.in_dim if vace_in_dim is None else vace_in_dim
assert 0 in self.vace_layers
self.vace_layers_mapping = {i: n for n, i in enumerate(self.vace_layers)}
# blocks
self.blocks = nn.ModuleList([
WanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm,
self.cross_attn_norm, self.eps, block_no =i,
block_id=self.vace_layers_mapping[i] if i in self.vace_layers else None)
for i in range(self.num_layers)
])
# vace blocks
self.vace_blocks = nn.ModuleList([
VaceWanAttentionBlock('t2v_cross_attn', self.dim, self.ffn_dim, self.num_heads, self.window_size, self.qk_norm,
self.cross_attn_norm, self.eps, block_id=i)
for i in self.vace_layers
])
# vace patch embeddings
self.vace_patch_embedding = nn.Conv3d(
self.vace_in_dim, self.dim, kernel_size=self.patch_size, stride=self.patch_size
)
if recammaster :
dim=self.blocks[0].self_attn.q.weight.shape[0]
for block in self.blocks:
block.cam_encoder = nn.Linear(12, dim)
block.projector = nn.Linear(dim, dim)
block.cam_encoder.weight.data.zero_()
block.cam_encoder.bias.data.zero_()
block.projector.weight = nn.Parameter(torch.eye(dim))
block.projector.bias = nn.Parameter(torch.zeros(dim))
if fantasytalking_dim > 0:
from fantasytalking.model import WanCrossAttentionProcessor
for block in self.blocks:
block.cross_attn.processor = WanCrossAttentionProcessor(fantasytalking_dim, dim)
def lock_layers_dtypes(self, hybrid_dtype = None, dtype = torch.float32):
layer_list = [self.head, self.head.head, self.patch_embedding]
target_dype= dtype
layer_list2 = [ self.time_embedding, self.time_embedding[0], self.time_embedding[2],
self.time_projection, self.time_projection[1]] #, self.text_embedding, self.text_embedding[0], self.text_embedding[2] ]
for block in self.blocks:
layer_list2 += [block.norm3]
if hasattr(self, "fps_embedding"):
layer_list2 += [self.fps_embedding, self.fps_projection, self.fps_projection[0], self.fps_projection[2]]
if hasattr(self, "vace_patch_embedding"):
layer_list2 += [self.vace_patch_embedding]
layer_list2 += [self.vace_blocks[0].before_proj]
for block in self.vace_blocks:
layer_list2 += [block.after_proj, block.norm3]
target_dype2 = hybrid_dtype if hybrid_dtype != None else dtype
# cam master
if hasattr(self.blocks[0], "projector"):
for block in self.blocks:
layer_list2 += [block.projector]
for current_layer_list, current_dtype in zip([layer_list, layer_list2], [target_dype, target_dype2]):
for layer in current_layer_list:
layer._lock_dtype = dtype
if hasattr(layer, "weight") and layer.weight.dtype != current_dtype :
layer.weight.data = layer.weight.data.to(current_dtype)
if hasattr(layer, "bias"):
layer.bias.data = layer.bias.data.to(current_dtype)
self._lock_dtype = dtype
def compute_teacache_threshold(self, start_step, timesteps = None, speed_factor =0):
modulation_dtype = self.time_projection[1].weight.dtype
rescale_func = np.poly1d(self.coefficients)
e_list = []
for t in timesteps:
t = torch.stack([t])
time_emb = self.time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(modulation_dtype) ) # b, dim
e_list.append(time_emb)
best_deltas = None
best_threshold = 0.01
best_diff = 1000
best_signed_diff = 1000
target_nb_steps= int(len(timesteps) / speed_factor)
threshold = 0.01
while threshold <= 0.6:
accumulated_rel_l1_distance =0
nb_steps = 0
diff = 1000
deltas = []
for i, t in enumerate(timesteps):
skip = False
if not (i<=start_step or i== len(timesteps)-1):
delta = abs(rescale_func(((e_list[i]-e_list[i-1]).abs().mean() / e_list[i-1].abs().mean()).cpu().item()))
# deltas.append(delta)
accumulated_rel_l1_distance += delta
if accumulated_rel_l1_distance < threshold:
skip = True
# deltas.append("SKIP")
else:
accumulated_rel_l1_distance = 0
if not skip:
nb_steps += 1
signed_diff = target_nb_steps - nb_steps
diff = abs(signed_diff)
if diff < best_diff:
best_threshold = threshold
best_deltas = deltas
best_diff = diff
best_signed_diff = signed_diff
elif diff > best_diff:
break
threshold += 0.01
self.rel_l1_thresh = best_threshold
print(f"Tea Cache, best threshold found:{best_threshold:0.2f} with gain x{len(timesteps)/(target_nb_steps - best_signed_diff):0.2f} for a target of x{speed_factor}")
# print(f"deltas:{best_deltas}")
return best_threshold
def forward(
self,
x,
t,
context,
vace_context = None,
vace_context_scale=[1.0],
clip_fea=None,
y=None,
freqs = None,
pipeline = None,
current_step = 0,
x_id= 0,
max_steps = 0,
slg_layers=None,
callback = None,
cam_emb: torch.Tensor = None,
fps = None,
causal_block_size = 1,
causal_attention = False,
audio_proj=None,
audio_context_lens=None,
audio_scale=None,
):
# patch_dtype = self.patch_embedding.weight.dtype
modulation_dtype = self.time_projection[1].weight.dtype
if self.model_type == 'i2v':
assert clip_fea is not None and y is not None
# params
device = self.patch_embedding.weight.device
if torch.is_tensor(freqs) and freqs.device != device:
freqs = freqs.to(device)
chipmunk = offload.shared_state.get("_chipmunk", False)
if chipmunk:
# from chipmunk.ops.voxel import voxel_chunk_no_padding, reverse_voxel_chunk_no_padding
voxel_shape = (4, 6, 8)
x_list = x
joint_pass = len(x_list) > 1
is_source_x = [ x.data_ptr() == x_list[0].data_ptr() and i > 0 for i, x in enumerate(x_list) ]
last_x_idx = 0
for i, (is_source, x) in enumerate(zip(is_source_x, x_list)):
if is_source:
x_list[i] = x_list[0].clone()
last_x_idx = i
else:
# image source
if y is not None:
x = torch.cat([x, y], dim=0)
# embeddings
x = self.patch_embedding(x.unsqueeze(0)).to(modulation_dtype)
grid_sizes = x.shape[2:]
if chipmunk:
x = x.unsqueeze(-1)
x_og_shape = x.shape
x = voxel_chunk_no_padding(x, voxel_shape).squeeze(-1).transpose(1, 2)
else:
x = x.flatten(2).transpose(1, 2)
x_list[i] = x
x, y = None, None
block_mask = None
if causal_attention and causal_block_size > 0 and False: # NEVER WORKED
frame_num = grid_sizes[0]
height = grid_sizes[1]
width = grid_sizes[2]
block_num = frame_num // causal_block_size
range_tensor = torch.arange(block_num).view(-1, 1)
range_tensor = range_tensor.repeat(1, causal_block_size).flatten()
causal_mask = range_tensor.unsqueeze(0) <= range_tensor.unsqueeze(1) # f, f
causal_mask = causal_mask.view(frame_num, 1, 1, frame_num, 1, 1).to(x[0].device)
causal_mask = causal_mask.repeat(1, height, width, 1, height, width)
causal_mask = causal_mask.reshape(frame_num * height * width, frame_num * height * width)
block_mask = causal_mask.unsqueeze(0).unsqueeze(0)
del causal_mask
offload.shared_state["embed_sizes"] = grid_sizes
offload.shared_state["step_no"] = current_step
offload.shared_state["max_steps"] = max_steps
_flag_df = t.dim() == 2
e = self.time_embedding(
sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(modulation_dtype) # self.patch_embedding.weight.dtype)
) # b, dim
e0 = self.time_projection(e).unflatten(1, (6, self.dim)).to(e.dtype)
if self.inject_sample_info:
fps = torch.tensor(fps, dtype=torch.long, device=device)
fps_emb = self.fps_embedding(fps).to(e.dtype)
if _flag_df:
e0 = e0 + self.fps_projection(fps_emb).unflatten(1, (6, self.dim)).repeat(t.shape[1], 1, 1)
else:
e0 = e0 + self.fps_projection(fps_emb).unflatten(1, (6, self.dim))
# context
context = [self.text_embedding( torch.cat( [u, u.new_zeros(self.text_len - u.size(0), u.size(1))] ).unsqueeze(0) ) for u in context ]
if clip_fea is not None:
context_clip = self.img_emb(clip_fea) # bs x 257 x dim
context = [ torch.cat( [context_clip, u ], dim=1 ) for u in context ]
context_list = context
if audio_scale != None:
audio_scale_list = audio_scale
else:
audio_scale_list = [None] * len(x_list)
# arguments
kwargs = dict(
grid_sizes=grid_sizes,
freqs=freqs,
cam_emb = cam_emb,
block_mask = block_mask,
audio_proj=audio_proj,
audio_context_lens=audio_context_lens,
)
if vace_context == None:
hints_list = [None ] *len(x_list)
else:
# Vace embeddings
c = [self.vace_patch_embedding(u.to(self.vace_patch_embedding.weight.dtype).unsqueeze(0)) for u in vace_context]
c = [u.flatten(2).transpose(1, 2) for u in c]
kwargs['context_scale'] = vace_context_scale
hints_list = [ [ [sub_c] for sub_c in c] for _ in range(len(x_list)) ]
del c
should_calc = True
if self.enable_cache:
if x_id != 0:
should_calc = self.should_calc
else:
if current_step <= self.cache_start_step or current_step == self.num_steps-1:
should_calc = True
self.accumulated_rel_l1_distance = 0
else:
rescale_func = np.poly1d(self.coefficients)
delta = abs(rescale_func(((e-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()))
self.accumulated_rel_l1_distance += delta
if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
should_calc = False
self.teacache_skipped_steps += 1
# print(f"Teacache Skipped Step no {current_step} ({self.teacache_skipped_steps}/{current_step}), delta={delta}" )
else:
should_calc = True
self.accumulated_rel_l1_distance = 0
self.previous_modulated_input = e
self.should_calc = should_calc
if not should_calc:
if joint_pass:
for i, x in enumerate(x_list):
x += self.previous_residual[i]
else:
x = x_list[0]
x += self.previous_residual[x_id]
x = None
else:
if self.enable_cache:
if joint_pass:
self.previous_residual = [ None ] * len(self.previous_residual)
else:
self.previous_residual[x_id] = None
ori_hidden_states = [ None ] * len(x_list)
ori_hidden_states[0] = x_list[0].clone()
for i in range(1, len(x_list)):
ori_hidden_states[i] = ori_hidden_states[0] if is_source_x[i] else x_list[i].clone()
for block_idx, block in enumerate(self.blocks):
offload.shared_state["layer"] = block_idx
if callback != None:
callback(-1, None, False, True)
if pipeline._interrupt:
return [None] * len(x_list)
if (x_id != 0 or joint_pass) and slg_layers is not None and block_idx in slg_layers:
if not joint_pass:
continue
x_list[0] = block(x_list[0], context = context_list[0], e= e0, **kwargs)
else:
for i, (x, context, hints, audio_scale) in enumerate(zip(x_list, context_list, hints_list, audio_scale_list)):
x_list[i] = block(x, context = context, hints= hints, audio_scale= audio_scale, e= e0, **kwargs)
del x
del context, hints
if self.enable_cache:
if joint_pass:
for i, (x, ori, is_source) in enumerate(zip(x_list, ori_hidden_states, is_source_x)) :
if i == 0 or is_source and i != last_x_idx :
self.previous_residual[i] = torch.sub(x, ori)
else:
self.previous_residual[i] = ori
torch.sub(x, ori, out=self.previous_residual[i])
ori_hidden_states[i] = None
x , ori = None, None
else:
residual = ori_hidden_states[0] # just to have a readable code
torch.sub(x_list[0], ori_hidden_states[0], out=residual)
self.previous_residual[x_id] = residual
residual, ori_hidden_states = None, None
for i, x in enumerate(x_list):
if chipmunk:
x = reverse_voxel_chunk_no_padding(x.transpose(1, 2).unsqueeze(-1), x_og_shape, voxel_shape).squeeze(-1)
x = x.flatten(2).transpose(1, 2)
# head
x = self.head(x, e)
# unpatchify
x_list[i] = self.unpatchify(x, grid_sizes)
del x
return [x[0].float() for x in x_list]
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 in x:
u = u[:math.prod(grid_sizes)].view(*grid_sizes, *self.patch_size, c)
u = torch.einsum('fhwpqrc->cfphqwr', u)
u = u.reshape(c, *[i * j for i, j in zip(grid_sizes, 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)
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