634 lines
24 KiB
Python
634 lines
24 KiB
Python
# Modified from https://github.com/Fantasy-AMAP/fantasy-talking/blob/main/diffsynth/models
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# and https://github.com/Soul-AILab/SoulX-FlashHead/blob/main/flash_head/src/modules/flash_head_model.py
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# Copyright Alibaba Inc. All Rights Reserved.
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import math
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from einops import rearrange
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from typing import Any, Dict, Tuple
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import torch
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import torch.cuda.amp as amp
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import torch.nn as nn
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import torch.nn.functional as F
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from diffusers.configuration_utils import register_to_config
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from diffusers.utils import is_torch_version
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from .attention_utils import attention
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from .wan_transformer3d import (WanLayerNorm, WanRMSNorm,
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WanSelfAttention, WanTransformer3DModel,
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sinusoidal_embedding_1d)
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class AudioMLP(nn.Module):
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r"""
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MLP matching official flash_head_model.py MLP class structure.
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"""
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def __init__(self, in_dim, out_dim):
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super().__init__()
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self.proj = nn.Sequential(
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nn.LayerNorm(in_dim),
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nn.Linear(in_dim, in_dim),
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nn.GELU(),
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nn.Linear(in_dim, out_dim),
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nn.LayerNorm(out_dim),
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)
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def forward(self, x):
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r"""
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Args:
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x (`Tensor`):
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Input tensor
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Returns:
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`Tensor`:
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Projected output tensor
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"""
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return self.proj(x)
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class AudioProjModel(nn.Module):
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r"""
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Multi-stage audio projection model.
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"""
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def __init__(
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self,
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seq_len=5,
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seq_len_vf=8,
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blocks=12,
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channels=768,
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intermediate_dim=512,
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output_dim=1536,
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context_tokens=32,
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norm_output_audio=True,
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):
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super().__init__()
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self.seq_len = seq_len
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self.blocks = blocks
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self.channels = channels
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self.input_dim = seq_len * blocks * channels
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self.input_dim_vf = seq_len_vf * blocks * channels
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self.intermediate_dim = intermediate_dim
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self.context_tokens = context_tokens
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self.output_dim = output_dim
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self.proj1 = nn.Linear(self.input_dim, intermediate_dim)
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self.proj1_vf = nn.Linear(self.input_dim_vf, intermediate_dim)
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self.proj2 = nn.Linear(intermediate_dim, intermediate_dim)
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self.proj3 = nn.Linear(intermediate_dim, context_tokens * output_dim)
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self.norm = nn.LayerNorm(output_dim) if norm_output_audio else nn.Identity()
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def forward(self, audio_embeds, audio_embeds_vf, dtype=torch.bfloat16):
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r"""
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Args:
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audio_embeds (`Tensor`):
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First frame audio with shape [B, 1, seq_len, blocks, channels]
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audio_embeds_vf (`Tensor`):
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Subsequent frames audio with shape [B, F-1, seq_len_vf, blocks, channels]
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dtype (`torch.dtype`, *optional*, defaults to torch.bfloat16):
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Output dtype to match transformer precision
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Returns:
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`Tensor`:
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Context tokens with shape [B, F, context_tokens, output_dim]
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"""
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# Ensure input dtype matches target dtype
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if audio_embeds.dtype != dtype:
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audio_embeds = audio_embeds.to(dtype=dtype)
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if audio_embeds_vf.dtype != dtype:
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audio_embeds_vf = audio_embeds_vf.to(dtype=dtype)
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video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1]
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B = audio_embeds.shape[0]
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# Process first frame audio
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audio_embeds = rearrange(audio_embeds, "b f w s c -> (b f) w s c")
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bf, w, s, c = audio_embeds.shape
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audio_embeds = audio_embeds.view(bf, w * s * c)
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# Process subsequent frames audio
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audio_embeds_vf = rearrange(audio_embeds_vf, "b f w s c -> (b f) w s c")
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bf_vf, w_vf, s_vf, c_vf = audio_embeds_vf.shape
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audio_embeds_vf = audio_embeds_vf.view(bf_vf, w_vf * s_vf * c_vf)
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# First projection
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audio_embeds = torch.relu(self.proj1(audio_embeds))
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audio_embeds_vf = torch.relu(self.proj1_vf(audio_embeds_vf))
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audio_embeds = rearrange(audio_embeds, "(b f) c -> b f c", b=B)
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audio_embeds_vf = rearrange(audio_embeds_vf, "(b f) c -> b f c", b=B)
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audio_embeds_c = torch.cat([audio_embeds, audio_embeds_vf], dim=1)
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b_c, n_t, c_a = audio_embeds_c.shape
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audio_embeds_c = audio_embeds_c.view(b_c * n_t, c_a)
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# Second projection
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audio_embeds_c = torch.relu(self.proj2(audio_embeds_c))
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context_tokens = self.proj3(audio_embeds_c).reshape(b_c * n_t, self.context_tokens, self.output_dim)
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# Normalization and reshape
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context_tokens = self.norm(context_tokens)
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context_tokens = rearrange(context_tokens, "(b f) m c -> b f m c", f=video_length)
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# Ensure output dtype matches transformer precision
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if context_tokens.dtype != dtype:
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context_tokens = context_tokens.to(dtype=dtype)
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return context_tokens # [B, F, context_tokens, output_dim]
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class AudioCrossAttention(WanSelfAttention):
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r"""
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Cross-attention module for audio context.
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"""
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def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6):
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super().__init__(dim, num_heads, window_size, qk_norm, eps)
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def forward(self, x, context, dtype=torch.bfloat16, **kwargs):
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r"""
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Args:
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x (`Tensor`):
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Query tensor with shape [(B*F), L_x, C] (per-frame patch tokens)
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context (`Tensor`):
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Key/value tensor with shape [F, context_tokens, C] (per-frame audio context tokens)
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dtype (`torch.dtype`, *optional*, defaults to torch.bfloat16):
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Output dtype to match transformer precision
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"""
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b, n, d = x.size(0), self.num_heads, self.head_dim
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q = self.norm_q(self.q(x)).to(dtype=dtype).view(b, -1, n, d)
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k = self.norm_k(self.k(context)).to(dtype=dtype).view(b, -1, n, d)
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v = self.v(context.to(dtype=dtype)).view(b, -1, n, d)
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out = attention(q, k, v, k_lens=None)
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out = out.flatten(2)
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out = self.o(out).to(dtype=dtype)
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return out
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class FlashHeadAttentionBlock(nn.Module):
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r"""
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Attention block with audio cross-attention support.
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"""
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def __init__(
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self,
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cross_attn_type, # Useless
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dim,
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ffn_dim,
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num_heads,
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window_size=(-1, -1),
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qk_norm=True,
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cross_attn_norm=False,
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eps=1e-6,
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):
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r"""
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Args:
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cross_attn_type (`str`):
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Cross-attention type (unused)
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dim (`int`):
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Transformer dimension
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ffn_dim (`int`):
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Feed-forward network dimension
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num_heads (`int`):
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Number of attention heads
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window_size (`tuple`, *optional*, defaults to (-1, -1)):
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Window size for windowed attention
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qk_norm (`bool`, *optional*, defaults to True):
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Whether to apply QK normalization
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cross_attn_norm (`bool`, *optional*, defaults to False):
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Whether to apply cross-attention normalization
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eps (`float`, *optional*, defaults to 1e-6):
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Epsilon for layer normalization
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"""
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super().__init__()
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self.dim = dim
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self.ffn_dim = ffn_dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.qk_norm = qk_norm
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self.cross_attn_norm = cross_attn_norm
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self.eps = eps
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# Layers
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self.norm1 = WanLayerNorm(dim, eps)
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self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm, eps)
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self.norm3 = (
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WanLayerNorm(dim, eps, elementwise_affine=True)
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if cross_attn_norm
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else nn.Identity()
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)
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self.cross_attn = AudioCrossAttention(
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dim, num_heads, (-1, -1), qk_norm, eps
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)
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self.norm2 = WanLayerNorm(dim, eps)
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self.ffn = nn.Sequential(
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nn.Linear(dim, ffn_dim),
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nn.GELU(approximate="tanh"),
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nn.Linear(ffn_dim, dim),
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)
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# Modulation
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self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
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def forward(
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self,
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x,
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e,
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seq_lens,
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grid_sizes,
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freqs,
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context,
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dtype=torch.bfloat16,
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t=0,
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):
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r"""
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Args:
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x (`Tensor`):
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Input tensor with shape [B, L, C]
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e (`Tensor`):
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Time embedding modulation with shape [B, 6, C]
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seq_lens (`Tensor`):
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Sequence lengths with shape [B]
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grid_sizes (`Tensor`):
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Grid sizes with shape [B, 3]
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freqs (`Tensor`):
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RoPE frequencies
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context (`Tensor`):
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Audio context embeddings
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dtype (`torch.dtype`, *optional*, defaults to torch.bfloat16):
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Output dtype to match transformer precision
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t (`int`, *optional*, defaults to 0):
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Timestep (unused, kept for API compatibility)
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"""
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e = (self.modulation + e).chunk(6, dim=1)
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# Self-attention
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y = self.self_attn(
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self.norm1(x) * (1 + e[1]) + e[0], seq_lens, grid_sizes, freqs, dtype, t=t
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)
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x = x + y * e[2]
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# Cross-attention: distribute context per latent frame
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# Context shape: [B, F, context_tokens, dim]
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if hasattr(self, 'sp_world_size') and self.sp_world_size > 1 and self.all_gather is not None:
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# All gather x to get full sequence for audio cross attention
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x_full = self.all_gather(x, dim=1)
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x_norm_full = self.norm3(x_full)
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num_latent_frames = context.shape[1]
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x_1_full = rearrange(x_norm_full, 'b (f l) c -> (b f) l c', f=num_latent_frames)
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context_1 = context.squeeze(0)
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x_a_full = self.cross_attn(x_1_full, context_1, dtype=dtype)
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# Chunk result back to local rank
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x_a = torch.chunk(x_a_full.flatten(0, 1).unsqueeze(0), self.sp_world_size, dim=1)[self.sp_world_rank]
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x = x + x_a
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else:
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num_latent_frames = context.shape[1]
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x_norm = self.norm3(x)
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x_1 = rearrange(x_norm, 'b (f l) c -> (b f) l c', f=num_latent_frames)
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context_1 = context.squeeze(0)
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x = x + self.cross_attn(
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x_1, context_1, dtype=dtype,
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).flatten(0, 1).unsqueeze(0)
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y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
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x = x + y * e[5]
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return x
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class FlashHeadTransformer3DModel(WanTransformer3DModel):
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r"""
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FlashHead Transformer 3D model with audio integration.
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"""
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@register_to_config
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def __init__(self,
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model_type='i2v',
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patch_size=(1, 2, 2),
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text_len=512,
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in_dim=16,
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dim=2048,
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ffn_dim=8192,
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freq_dim=256,
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text_dim=4096,
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out_dim=16,
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num_heads=16,
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num_layers=32,
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window_size=(-1, -1),
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qk_norm=True,
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cross_attn_norm=True,
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eps=1e-6,
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cross_attn_type=None,
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# Audio proj params
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audio_window=5,
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vae_scale=4,
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audio_blocks=12,
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audio_channels=768,
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intermediate_dim=512,
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context_tokens=32,
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audio_output_dim=1536,
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norm_output_audio=True
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):
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r"""
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Initialize the FlashHead diffusion model backbone.
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Args:
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model_type (`str`, *optional*, defaults to 'i2v'):
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Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
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patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
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3D patch dimensions for video embedding
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text_len (`int`, *optional*, defaults to 512):
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Fixed length for text embeddings
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in_dim (`int`, *optional*, defaults to 16):
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Input video channels
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dim (`int`, *optional*, defaults to 2048):
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Hidden dimension of the transformer
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ffn_dim (`int`, *optional*, defaults to 8192):
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Intermediate dimension in feed-forward network
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freq_dim (`int`, *optional*, defaults to 256):
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Dimension for sinusoidal time embeddings
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text_dim (`int`, *optional*, defaults to 4096):
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Input dimension for text embeddings
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out_dim (`int`, *optional*, defaults to 16):
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Output video channels
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num_heads (`int`, *optional*, defaults to 16):
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Number of attention heads
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num_layers (`int`, *optional*, defaults to 32):
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Number of transformer blocks
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window_size (`tuple`, *optional*, defaults to (-1, -1)):
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Window size for local attention
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qk_norm (`bool`, *optional*, defaults to True):
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Enable query/key normalization
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cross_attn_norm (`bool`, *optional*, defaults to True):
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Enable cross-attention normalization
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eps (`float`, *optional*, defaults to 1e-6):
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Epsilon value for normalization layers
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cross_attn_type (`str`, *optional*, defaults to None):
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Cross-attention type
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audio_window (`int`, *optional*, defaults to 5):
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Audio window size
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vae_scale (`int`, *optional*, defaults to 4):
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VAE temporal downsample factor
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audio_blocks (`int`, *optional*, defaults to 12):
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Number of wav2vec blocks
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audio_channels (`int`, *optional*, defaults to 768):
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Number of channels per audio block
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intermediate_dim (`int`, *optional*, defaults to 512):
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Intermediate dimension for audio projection
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context_tokens (`int`, *optional*, defaults to 32):
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Number of context tokens for audio
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audio_output_dim (`int`, *optional*, defaults to 1536):
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Output dimension for audio projection
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norm_output_audio (`bool`, *optional*, defaults to True):
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Whether to normalize audio output
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"""
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super().__init__(
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model_type=model_type,
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patch_size=patch_size,
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text_len=text_len,
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in_dim=in_dim,
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dim=dim,
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ffn_dim=ffn_dim,
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freq_dim=freq_dim,
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text_dim=text_dim,
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out_dim=out_dim,
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num_heads=num_heads,
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num_layers=num_layers,
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window_size=window_size,
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qk_norm=qk_norm,
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cross_attn_norm=cross_attn_norm,
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eps=eps,
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cross_attn_type=cross_attn_type,
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)
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if cross_attn_type is None:
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cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
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self.blocks = nn.ModuleList([
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FlashHeadAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
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window_size, qk_norm, cross_attn_norm, eps)
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for _ in range(num_layers)
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])
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for layer_idx, block in enumerate(self.blocks):
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block.self_attn.layer_idx = layer_idx
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block.self_attn.num_layers = self.num_layers
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# Audio window params
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self.audio_window = audio_window
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self.vae_scale = vae_scale
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# Seq_len_vf for subsequent frames
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seq_len_vf = audio_window + vae_scale - 1
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self.audio_proj = AudioProjModel(
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seq_len=audio_window,
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seq_len_vf=seq_len_vf,
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blocks=audio_blocks,
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channels=audio_channels,
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intermediate_dim=intermediate_dim,
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output_dim=audio_output_dim,
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context_tokens=context_tokens,
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norm_output_audio=norm_output_audio,
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)
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# Audio_emb: MLP for direct audio embedding
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self.audio_emb = AudioMLP(audio_channels, dim)
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def prepare_audio_context(self, audio_wav2vec_fea: torch.Tensor, num_latent_frames: int, dtype=torch.bfloat16):
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r"""
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Prepare per-latent-frame audio context from raw wav2vec features.
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Args:
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audio_wav2vec_fea (`Tensor`):
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Raw wav2vec features with shape [B, num_video_frames, audio_window, blocks, channels]
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num_latent_frames (`int`):
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Number of latent frames
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dtype (`torch.dtype`, *optional*, defaults to torch.bfloat16):
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Output dtype to match transformer precision
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Returns:
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`Tensor`:
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Audio context with shape [B, num_latent_frames, context_tokens, audio_output_dim]
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"""
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audio_cond = audio_wav2vec_fea # [B, total_video_frames, audio_window, blocks, channels]
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# First frame: directly use the full audio window
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first_frame_audio = audio_cond[:, :1, ...] # [B, 1, audio_window, blocks, channels]
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# Subsequent frames: rearrange into (n_latent, vae_scale) groups
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latter_frames_audio = rearrange(
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audio_cond[:, 1:, ...],
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"b (n_latent n_frame) w s c -> b n_latent n_frame w s c",
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n_frame=self.vae_scale
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) # [B, num_latent_frames-1, vae_scale, audio_window, blocks, channels]
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mid_idx = self.audio_window // 2
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# Select audio window per sub-frame position within each latent group
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first_of_group = latter_frames_audio[:, :, :1, :mid_idx + 1, ...] # [B, F-1, 1, mid_idx+1, S, C]
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middle_of_group = latter_frames_audio[:, :, 1:-1, mid_idx:mid_idx + 1, ...] # [B, F-1, vae_scale-2, 1, S, C]
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last_of_group = latter_frames_audio[:, :, -1:, mid_idx:, ...] # [B, F-1, 1, audio_window-mid_idx, S, C]
|
|
|
|
# Flatten sub-window dim: (n_frame, window) -> (n_frame * window)
|
|
latter_frames_processed = torch.cat([
|
|
rearrange(first_of_group, "b f nf w s c -> b f (nf w) s c"),
|
|
rearrange(middle_of_group, "b f nf w s c -> b f (nf w) s c"),
|
|
rearrange(last_of_group, "b f nf w s c -> b f (nf w) s c"),
|
|
], dim=2) # [B, num_latent_frames-1, seq_len_vf, blocks, channels]
|
|
|
|
# Project to context tokens: [B, num_latent_frames, context_tokens, audio_output_dim]
|
|
context = self.audio_proj(first_frame_audio, latter_frames_processed, dtype=dtype)
|
|
return context
|
|
|
|
def enable_multi_gpus_inference(self,):
|
|
r"""
|
|
Enable multi-GPU inference using sequence parallel.
|
|
"""
|
|
from ..dist import (get_sequence_parallel_rank,
|
|
get_sequence_parallel_world_size, get_sp_group,
|
|
usp_attn_flashhead_forward)
|
|
import types
|
|
|
|
self.sp_world_size = get_sequence_parallel_world_size()
|
|
self.sp_world_rank = get_sequence_parallel_rank()
|
|
self.all_gather = get_sp_group().all_gather
|
|
|
|
# Replace self_attn forward with xfuser version for all blocks
|
|
for block in self.blocks:
|
|
block.self_attn.forward = types.MethodType(
|
|
usp_attn_flashhead_forward, block.self_attn)
|
|
# Pass sp parameters to block for audio cross_attn multi-GPU support
|
|
block.sp_world_size = self.sp_world_size
|
|
block.sp_world_rank = self.sp_world_rank
|
|
block.all_gather = self.all_gather
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
t,
|
|
seq_len,
|
|
audio_wav2vec_fea=None,
|
|
y=None,
|
|
):
|
|
r"""
|
|
Forward pass through the diffusion model.
|
|
|
|
Args:
|
|
x (`List[Tensor]`):
|
|
List of input video tensors, each with shape [C_in, F, H, W]
|
|
t (`Tensor`):
|
|
Diffusion timesteps tensor of shape [B]
|
|
seq_len (`int`):
|
|
Maximum sequence length for positional encoding
|
|
audio_wav2vec_fea (`Tensor`, *optional*):
|
|
Raw wav2vec audio features
|
|
y (`List[Tensor]`, *optional*):
|
|
Conditional video inputs for image-to-video mode, same shape as x
|
|
|
|
Returns:
|
|
`Tensor`:
|
|
Denoised video tensor of shape [B, C_out, F, H/8, W/8]
|
|
"""
|
|
# Get device and dtype
|
|
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)
|
|
|
|
# Concatenate condition video to input (for I2V)
|
|
if y is not None:
|
|
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
|
|
|
|
# Patch embedding: convert video to sequence of patches
|
|
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]
|
|
|
|
# Padding for multi-gpu inference
|
|
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 sinusoidal encoding
|
|
if t.dim() != 1:
|
|
if t.size(1) < seq_len:
|
|
pad_size = seq_len - t.size(1)
|
|
last_elements = t[:, -1].unsqueeze(1)
|
|
padding = last_elements.repeat(1, pad_size)
|
|
t = torch.cat([t, padding], dim=1)
|
|
bt = t.size(0)
|
|
ft = t.flatten()
|
|
e = self.time_embedding(
|
|
sinusoidal_embedding_1d(self.freq_dim, ft).unflatten(0, (bt, seq_len)).float()).to(dtype)
|
|
e0 = self.time_projection(e).unflatten(2, (6, self.dim))
|
|
else:
|
|
e = self.time_embedding(
|
|
sinusoidal_embedding_1d(self.freq_dim, t).float()).to(dtype)
|
|
e0 = self.time_projection(e).unflatten(1, (6, self.dim))
|
|
|
|
# Audio context: replaces text context for cross-attention
|
|
num_latent_frames = int(grid_sizes[0][0].item())
|
|
audio_context = self.prepare_audio_context(
|
|
audio_wav2vec_fea.to(device=x.device, dtype=x.dtype),
|
|
num_latent_frames=num_latent_frames,
|
|
dtype=x.dtype,
|
|
)
|
|
|
|
# Context Parallel: split input across GPUs
|
|
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]
|
|
|
|
# Prepare checkpointing utilities
|
|
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 {}
|
|
|
|
# Main transformer loop
|
|
for block in self.blocks:
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
x = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
x, e0, seq_lens, grid_sizes, self.freqs,
|
|
audio_context,
|
|
dtype, t,
|
|
**ckpt_kwargs,
|
|
)
|
|
else:
|
|
# Arguments
|
|
x = block(
|
|
x,
|
|
e=e0,
|
|
seq_lens=seq_lens,
|
|
grid_sizes=grid_sizes,
|
|
freqs=self.freqs,
|
|
context=audio_context,
|
|
dtype=dtype,
|
|
t=t,
|
|
)
|
|
|
|
# Head: project to output space
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
x = torch.utils.checkpoint.checkpoint(create_custom_forward(self.head), x, e, **ckpt_kwargs)
|
|
else:
|
|
x = self.head(x, e)
|
|
|
|
# Context Parallel: gather results from all GPUs
|
|
if self.sp_world_size > 1:
|
|
x = self.all_gather(x, dim=1)
|
|
|
|
# Unpatchify: reconstruct video from patches
|
|
x = self.unpatchify(x, grid_sizes)
|
|
x = torch.stack(x)
|
|
return x
|
|
|
|
|
|
# Alias for backward compatibility
|
|
WanModelAudioProject = FlashHeadTransformer3DModel |