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Support for Hunyuan Video Avatar

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DeepBeepMeep
2025-06-05 15:35:58 +02:00
parent 7670af9610
commit 1976868f6a
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hyvideo/modules/audio_adapters.py Normal file
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@@ -0,0 +1,220 @@
"""
This module provides the implementation of an Audio Projection Model, which is designed for
audio processing tasks. The model takes audio embeddings as input and outputs context tokens
that can be used for various downstream applications, such as audio analysis or synthesis.
The AudioProjModel class is based on the ModelMixin class from the diffusers library, which
provides a foundation for building custom models. This implementation includes multiple linear
layers with ReLU activation functions and a LayerNorm for normalization.
Key Features:
- Audio embedding input with flexible sequence length and block structure.
- Multiple linear layers for feature transformation.
- ReLU activation for non-linear transformation.
- LayerNorm for stabilizing and speeding up training.
- Rearrangement of input embeddings to match the model's expected input shape.
- Customizable number of blocks, channels, and context tokens for adaptability.
The module is structured to be easily integrated into larger systems or used as a standalone
component for audio feature extraction and processing.
Classes:
- AudioProjModel: A class representing the audio projection model with configurable parameters.
Functions:
- (none)
Dependencies:
- torch: For tensor operations and neural network components.
- diffusers: For the ModelMixin base class.
- einops: For tensor rearrangement operations.
"""
import torch
from diffusers import ModelMixin
from einops import rearrange
import math
import torch.nn as nn
class AudioProjNet2(ModelMixin):
"""Audio Projection Model
This class defines an audio projection model that takes audio embeddings as input
and produces context tokens as output. The model is based on the ModelMixin class
and consists of multiple linear layers and activation functions. It can be used
for various audio processing tasks.
Attributes:
seq_len (int): The length of the audio sequence.
blocks (int): The number of blocks in the audio projection model.
channels (int): The number of channels in the audio projection model.
intermediate_dim (int): The intermediate dimension of the model.
context_tokens (int): The number of context tokens in the output.
output_dim (int): The output dimension of the context tokens.
Methods:
__init__(self, seq_len=5, blocks=12, channels=768, intermediate_dim=512, context_tokens=32, output_dim=768):
Initializes the AudioProjModel with the given parameters.
forward(self, audio_embeds):
Defines the forward pass for the AudioProjModel.
Parameters:
audio_embeds (torch.Tensor): The input audio embeddings with shape (batch_size, video_length, blocks, channels).
Returns:
context_tokens (torch.Tensor): The output context tokens with shape (batch_size, video_length, context_tokens, output_dim).
"""
def __init__(
self,
seq_len=5,
blocks=12, # add a new parameter blocks
channels=768, # add a new parameter channels
intermediate_dim=512,
output_dim=768,
context_tokens=4,
):
super().__init__()
self.seq_len = seq_len
self.blocks = blocks
self.channels = channels
self.input_dim = (
seq_len * 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.proj2 = nn.Linear(intermediate_dim, intermediate_dim)
self.proj3 = nn.Linear(intermediate_dim, context_tokens * output_dim)
self.norm = nn.LayerNorm(output_dim)
def forward(self, audio_embeds):
video_length = audio_embeds.shape[1]
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)
audio_embeds = torch.relu(self.proj1(audio_embeds))
audio_embeds = torch.relu(self.proj2(audio_embeds))
context_tokens = self.proj3(audio_embeds).reshape(
batch_size, self.context_tokens, self.output_dim
)
context_tokens = self.norm(context_tokens)
out_all = rearrange(
context_tokens, "(bz f) m c -> bz f m c", f=video_length
)
return out_all
def reshape_tensor(x, heads):
bs, length, width = x.shape
# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
x = x.view(bs, length, heads, -1)
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
x = x.transpose(1, 2)
# (bs, n_heads, length, dim_per_head)
x = x.reshape(bs, heads, length, -1)
return x
class PerceiverAttentionCA(nn.Module):
def __init__(self, *, dim=3072, dim_head=1024, heads=33):
super().__init__()
self.scale = dim_head ** -0.5
self.dim_head = dim_head
self.heads = heads
inner_dim = dim_head #* heads
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
self.to_out = nn.Linear(inner_dim, dim, bias=False)
import torch.nn.init as init
init.zeros_(self.to_out.weight)
if self.to_out.bias is not None:
init.zeros_(self.to_out.bias)
def forward(self, x, latents):
"""
Args:
x (torch.Tensor): image features
shape (b, t, aa, D)
latent (torch.Tensor): latent features
shape (b, t, hw, D)
"""
x = self.norm1(x)
latents = self.norm2(latents)
# print("latents shape: ", latents.shape)
# print("x shape: ", x.shape)
q = self.to_q(latents)
k, v = self.to_kv(x).chunk(2, dim=-1)
# attention
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
out = weight @ v
# out = out.permute(0, 2, 1, 3)
return self.to_out(out)
#def forward(self, x, latents):
# """
# Args:
# x (torch.Tensor): image features
# shape (b, t, aa, D)
# latent (torch.Tensor): latent features
# shape (b, t, hw, D)
# """
# if get_sequence_parallel_state():
# sp_size = nccl_info.sp_size
# sp_rank = nccl_info.rank_within_group
# print("rank:", latents.shape, sp_size, sp_rank)
# latents = torch.chunk(latents, sp_size, dim=1)[sp_rank]
# x = self.norm1(x)
# latents = self.norm2(latents)
# # print("latents shape: ", latents.shape)
# # print("x shape: ", x.shape)
# q = self.to_q(latents)
# k, v = self.to_kv(x).chunk(2, dim=-1)
# # print("q, k, v: ", q.shape, k.shape, v.shape)
# # attention
# #scale = 1 / math.sqrt(math.sqrt(self.dim_head))
# #weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
# #weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
# #out = weight @ v
# def shrink_head(encoder_state, dim):
# local_heads = encoder_state.shape[dim] // nccl_info.sp_size
# return encoder_state.narrow(dim, nccl_info.rank_within_group * local_heads, local_heads)
# if get_sequence_parallel_state():
# # batch_size, seq_len, attn_heads, head_dim
# q = all_to_all_4D(q, scatter_dim=2, gather_dim=1) # [2, 32256, 24, 128]
# k = shrink_head(k ,dim=2)
# v = shrink_head(v ,dim=2)
# qkv = torch.stack([query, key, value], dim=2)
# attn = flash_attn_no_pad(qkv, causal=False, dropout_p=0.0, softmax_scale=None)
# # out = out.permute(0, 2, 1, 3)
# #b, s, a, d = attn.shape
# #attn = attn.reshape(b, s, -1)
#
# out = self.to_out(attn)
# if get_sequence_parallel_state():
# out = all_gather(out, dim=1)
# return out

3
hyvideo/modules/embed_layers.py
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@@ -53,10 +53,11 @@ class PatchEmbed(nn.Module):
def forward(self, x):
x = self.proj(x)
shape = x.shape
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
return x, shape
class TextProjection(nn.Module):

118
hyvideo/modules/models.py
View File

@@ -19,6 +19,7 @@ from .token_refiner import SingleTokenRefiner
import numpy as np
from mmgp import offload
from wan.modules.attention import pay_attention
from .audio_adapters import AudioProjNet2, PerceiverAttentionCA
def get_linear_split_map():
hidden_size = 3072
@@ -589,7 +590,8 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
use_attention_mask: bool = True,
dtype: Optional[torch.dtype] = None,
device: Optional[torch.device] = None,
attention_mode: Optional[str] = "sdpa"
attention_mode: Optional[str] = "sdpa",
avatar = False,
):
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
@@ -708,6 +710,45 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
get_activation_layer("silu"),
**factory_kwargs,
)
avatar_audio = avatar
if avatar_audio:
self.ref_in = PatchEmbed(
self.patch_size, self.in_channels, self.hidden_size, **factory_kwargs
)
# -------------------- audio_proj_model --------------------
self.audio_proj = AudioProjNet2(seq_len=10, blocks=5, channels=384, intermediate_dim=1024, output_dim=3072, context_tokens=4)
# -------------------- motion-embeder --------------------
self.motion_exp = TimestepEmbedder(
self.hidden_size // 4,
get_activation_layer("silu"),
**factory_kwargs
)
self.motion_pose = TimestepEmbedder(
self.hidden_size // 4,
get_activation_layer("silu"),
**factory_kwargs
)
self.fps_proj = TimestepEmbedder(
self.hidden_size,
get_activation_layer("silu"),
**factory_kwargs
)
self.before_proj = nn.Linear(self.hidden_size, self.hidden_size)
# -------------------- audio_insert_model --------------------
self.double_stream_list = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
self.single_stream_list = []
self.double_stream_map = {str(i): j for j, i in enumerate(self.double_stream_list)}
self.single_stream_map = {str(i): j+len(self.double_stream_list) for j, i in enumerate(self.single_stream_list)}
self.audio_adapter_blocks = nn.ModuleList([
PerceiverAttentionCA(dim=3072, dim_head=1024, heads=33) for _ in range(len(self.double_stream_list) + len(self.single_stream_list))
])
def lock_layers_dtypes(self, dtype = torch.float32):
@@ -750,11 +791,17 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
guidance: torch.Tensor = None, # Guidance for modulation, should be cfg_scale x 1000.
pipeline=None,
x_id = 0,
step_no = 0,
callback = None,
audio_prompts = None,
motion_exp = None,
motion_pose = None,
fps = None,
face_mask = None,
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
img = x
batch_no, _, ot, oh, ow = x.shape
bsz, _, ot, oh, ow = x.shape
del x
txt = text_states
tt, th, tw = (
@@ -765,6 +812,17 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
# Prepare modulation vectors.
vec = self.time_in(t)
if motion_exp != None:
vec += self.motion_exp(motion_exp.view(-1)).view(bsz, -1) # (b, 3072)
if motion_pose != None:
vec += self.motion_pose(motion_pose.view(-1)).view(bsz, -1) # (b, 3072)
if fps != None:
vec += self.fps_proj(fps) # (b, 3072)
if audio_prompts != None:
audio_feature_all = self.audio_proj(audio_prompts)
audio_feature_pad = audio_feature_all[:,:1].repeat(1,3,1,1)
audio_feature_all_insert = torch.cat([audio_feature_pad, audio_feature_all], dim=1).view(bsz, ot, 16, 3072)
audio_feature_all = None
if self.i2v_condition_type == "token_replace":
token_replace_t = torch.zeros_like(t)
@@ -777,7 +835,6 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
# token_replace_mask_txt = None
# text modulation
# vec = vec + self.vector_in(text_states_2)
vec_2 = self.vector_in(text_states_2)
del text_states_2
vec += vec_2
@@ -793,12 +850,17 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
)
# our timestep_embedding is merged into guidance_in(TimestepEmbedder)
vec = vec + self.guidance_in(guidance)
vec += self.guidance_in(guidance)
# Embed image and text.
img = self.img_in(img)
if ref_latents != None:
ref_latents = self.img_in(ref_latents)
img, shape_mask = self.img_in(img)
if audio_prompts != None:
ref_latents_first = ref_latents[:, :, :1].clone()
ref_latents,_ = self.ref_in(ref_latents)
ref_latents_first,_ = self.img_in(ref_latents_first)
elif ref_latents != None:
ref_latents, _ = self.img_in(ref_latents)
if self.text_projection == "linear":
txt = self.txt_in(txt)
elif self.text_projection == "single_refiner":
@@ -808,7 +870,18 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
f"Unsupported text_projection: {self.text_projection}"
)
if ref_latents == None:
if audio_prompts != None:
img += self.before_proj(ref_latents)
ref_length = ref_latents_first.shape[-2] # [b s c]
img = torch.cat([ref_latents_first, img], dim=-2) # t c
img_len = img.shape[1]
mask_len = img_len - ref_length
if face_mask.shape[2] == 1:
face_mask = face_mask.repeat(1,1,ot,1,1) # repeat if number of mask frame is 1
face_mask = torch.nn.functional.interpolate(face_mask, size=[ot, shape_mask[-2], shape_mask[-1]], mode="nearest")
# face_mask = face_mask.view(-1,mask_len,1).repeat(1,1,img.shape[-1]).type_as(img)
face_mask = face_mask.view(-1,mask_len,1).type_as(img)
elif ref_latents == None:
ref_length = None
else:
ref_length = ref_latents.shape[-2]
@@ -828,13 +901,13 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
if x_id == 0:
self.should_calc = True
inp = img[0:1]
vec_ = vec
vec_ = vec[0:1]
( img_mod1_shift, img_mod1_scale, _ , _ , _ , _ , ) = self.double_blocks[0].img_mod(vec_).chunk(6, dim=-1)
normed_inp = self.double_blocks[0].img_norm1(inp)
normed_inp = normed_inp.to(torch.bfloat16)
modulated_inp = modulate( normed_inp, shift=img_mod1_shift, scale=img_mod1_scale )
del normed_inp, img_mod1_shift, img_mod1_scale
if self.teacache_counter <= self.teacache_start_step or self.teacache_counter == self.num_steps-1:
if step_no <= self.teacache_start_step or step_no == self.num_steps-1:
self.accumulated_rel_l1_distance = 0
else:
coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02]
@@ -846,9 +919,6 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
else:
self.accumulated_rel_l1_distance = 0
self.previous_modulated_input = modulated_inp
self.teacache_counter += 1
if self.teacache_counter == self.num_steps:
self.teacache_counter = 0
else:
self.should_calc = True
@@ -859,7 +929,7 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
self.previous_residual[x_id] = None
ori_img = img[0:1].clone()
# --------------------- Pass through DiT blocks ------------------------
for _, block in enumerate(self.double_blocks):
for layer_num, block in enumerate(self.double_blocks):
for i in range(len(img)):
if callback != None:
callback(-1, None, False, True)
@@ -880,6 +950,16 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
img[i], txt[i] = block(*double_block_args)
double_block_args = None
# insert audio feature to img
if audio_prompts != None:
audio_adapter = getattr(self.double_blocks[layer_num], "audio_adapter", None)
if audio_adapter != None:
real_img = img[i:i+1,ref_length:].view(1, ot, -1, 3072)
real_img = audio_adapter(audio_feature_all_insert[i:i+1], real_img).view(1, -1, 3072)
real_img *= face_mask[i:i+1]
img[i:i+1, ref_length:] += real_img
real_img = None
for _, block in enumerate(self.single_blocks):
for i in range(len(img)):
@@ -932,6 +1012,7 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
img = torch.cat(img_list)
img_list = None
# img = self.unpatchify(img, tt, th, tw)
img = self.unpatchify(img, tt, th, tw)
return img
@@ -1015,5 +1096,14 @@ HUNYUAN_VIDEO_CONFIG = {
"heads_num": 24,
"mlp_width_ratio": 4,
},
'HYVideo-T/2-avatar': { # 9.0B / 12.5B
'mm_double_blocks_depth': 20,
'mm_single_blocks_depth': 40,
'rope_dim_list': [16, 56, 56],
'hidden_size': 3072,
'heads_num': 24,
'mlp_width_ratio': 4,
'avatar': True,
},
}