mirror of
https://github.com/Wan-Video/Wan2.1.git
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799 lines
29 KiB
Python
799 lines
29 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import math
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import numpy as np
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import os
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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 einops import rearrange
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from diffusers import ModelMixin
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from .attention import flash_attention, SingleStreamMutiAttention
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from ..utils.multitalk_utils import get_attn_map_with_target
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__all__ = ['WanModel']
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def sinusoidal_embedding_1d(dim, position):
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# preprocess
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assert dim % 2 == 0
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half = dim // 2
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position = position.type(torch.float64)
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# calculation
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sinusoid = torch.outer(
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position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
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x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
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return x
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@amp.autocast(enabled=False)
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def rope_params(max_seq_len, dim, theta=10000):
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assert dim % 2 == 0
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freqs = torch.outer(
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torch.arange(max_seq_len),
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1.0 / torch.pow(theta,
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torch.arange(0, dim, 2).to(torch.float64).div(dim)))
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freqs = torch.polar(torch.ones_like(freqs), freqs)
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return freqs
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@amp.autocast(enabled=False)
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def rope_apply(x, grid_sizes, freqs):
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s, n, c = x.size(1), x.size(2), x.size(3) // 2
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freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
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output = []
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for i, (f, h, w) in enumerate(grid_sizes.tolist()):
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seq_len = f * h * w
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x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape(
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s, n, -1, 2))
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freqs_i = torch.cat([
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freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
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freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
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freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
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],
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dim=-1).reshape(seq_len, 1, -1)
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freqs_i = freqs_i.to(device=x_i.device)
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x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
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x_i = torch.cat([x_i, x[i, seq_len:]])
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output.append(x_i)
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return torch.stack(output).float()
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class WanRMSNorm(nn.Module):
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def __init__(self, dim, eps=1e-5):
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super().__init__()
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self.dim = dim
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def forward(self, x):
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r"""
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Args:
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x(Tensor): Shape [B, L, C]
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"""
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return self._norm(x.float()).type_as(x) * self.weight
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
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class WanLayerNorm(nn.LayerNorm):
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def __init__(self, dim, eps=1e-6, elementwise_affine=False):
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super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
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def forward(self, inputs: torch.Tensor) -> torch.Tensor:
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origin_dtype = inputs.dtype
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out = F.layer_norm(
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inputs.float(),
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self.normalized_shape,
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None if self.weight is None else self.weight.float(),
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None if self.bias is None else self.bias.float() ,
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self.eps
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).to(origin_dtype)
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return out
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class WanSelfAttention(nn.Module):
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def __init__(self,
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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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eps=1e-6):
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assert dim % num_heads == 0
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.head_dim = dim // 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.eps = eps
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# layers
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self.q = nn.Linear(dim, dim)
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self.k = nn.Linear(dim, dim)
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self.v = nn.Linear(dim, dim)
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self.o = nn.Linear(dim, dim)
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self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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def forward(self, x, seq_lens, grid_sizes, freqs, ref_target_masks=None):
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b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
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# query, key, value function
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def qkv_fn(x):
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q = self.norm_q(self.q(x)).view(b, s, n, d)
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k = self.norm_k(self.k(x)).view(b, s, n, d)
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v = self.v(x).view(b, s, n, d)
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return q, k, v
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q, k, v = qkv_fn(x)
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q = rope_apply(q, grid_sizes, freqs)
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k = rope_apply(k, grid_sizes, freqs)
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x = flash_attention(
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q=q,
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k=k,
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v=v,
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k_lens=seq_lens,
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window_size=self.window_size
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).type_as(x)
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# output
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x = x.flatten(2)
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x = self.o(x)
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with torch.no_grad():
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x_ref_attn_map = get_attn_map_with_target(q.type_as(x), k.type_as(x), grid_sizes[0],
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ref_target_masks=ref_target_masks)
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return x, x_ref_attn_map
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class WanI2VCrossAttention(WanSelfAttention):
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def __init__(self,
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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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eps=1e-6):
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super().__init__(dim, num_heads, window_size, qk_norm, eps)
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self.k_img = nn.Linear(dim, dim)
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self.v_img = nn.Linear(dim, dim)
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self.norm_k_img = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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def forward(self, x, context, context_lens):
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context_img = context[:, :257]
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context = context[:, 257:]
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b, n, d = x.size(0), self.num_heads, self.head_dim
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# compute query, key, value
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q = self.norm_q(self.q(x)).view(b, -1, n, d)
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k = self.norm_k(self.k(context)).view(b, -1, n, d)
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v = self.v(context).view(b, -1, n, d)
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k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d)
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v_img = self.v_img(context_img).view(b, -1, n, d)
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img_x = flash_attention(q, k_img, v_img, k_lens=None)
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# compute attention
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x = flash_attention(q, k, v, k_lens=context_lens)
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# output
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x = x.flatten(2)
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img_x = img_x.flatten(2)
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x = x + img_x
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x = self.o(x)
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return x
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class WanAttentionBlock(nn.Module):
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def __init__(self,
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cross_attn_type,
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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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output_dim=768,
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norm_input_visual=True,
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class_range=24,
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class_interval=4):
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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 = WanLayerNorm(
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dim, eps,
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elementwise_affine=True) if cross_attn_norm else nn.Identity()
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self.cross_attn = WanI2VCrossAttention(dim,
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num_heads,
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(-1, -1),
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qk_norm,
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eps)
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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), nn.GELU(approximate='tanh'),
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nn.Linear(ffn_dim, dim))
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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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# init audio module
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self.audio_cross_attn = SingleStreamMutiAttention(
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dim=dim,
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encoder_hidden_states_dim=output_dim,
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num_heads=num_heads,
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qk_norm=False,
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qkv_bias=True,
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eps=eps,
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norm_layer=WanRMSNorm,
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class_range=class_range,
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class_interval=class_interval
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)
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self.norm_x = WanLayerNorm(dim, eps, elementwise_affine=True) if norm_input_visual else nn.Identity()
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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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context_lens,
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audio_embedding=None,
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ref_target_masks=None,
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human_num=None,
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):
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dtype = x.dtype
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assert e.dtype == torch.float32
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with amp.autocast(dtype=torch.float32):
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e = (self.modulation.to(e.device) + e).chunk(6, dim=1)
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assert e[0].dtype == torch.float32
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# self-attention
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y, x_ref_attn_map = self.self_attn(
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(self.norm1(x).float() * (1 + e[1]) + e[0]).type_as(x), seq_lens, grid_sizes,
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freqs, ref_target_masks=ref_target_masks)
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with amp.autocast(dtype=torch.float32):
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x = x + y * e[2]
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x = x.to(dtype)
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# cross-attention of text
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x = x + self.cross_attn(self.norm3(x), context, context_lens)
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# cross attn of audio
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x_a = self.audio_cross_attn(self.norm_x(x), encoder_hidden_states=audio_embedding,
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shape=grid_sizes[0], x_ref_attn_map=x_ref_attn_map, human_num=human_num)
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x = x + x_a
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y = self.ffn((self.norm2(x).float() * (1 + e[4]) + e[3]).to(dtype))
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with amp.autocast(dtype=torch.float32):
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x = x + y * e[5]
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x = x.to(dtype)
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return x
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class Head(nn.Module):
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def __init__(self, dim, out_dim, patch_size, eps=1e-6):
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super().__init__()
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self.dim = dim
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self.out_dim = out_dim
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self.patch_size = patch_size
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self.eps = eps
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# layers
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out_dim = math.prod(patch_size) * out_dim
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self.norm = WanLayerNorm(dim, eps)
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self.head = nn.Linear(dim, out_dim)
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# modulation
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self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
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def forward(self, x, e):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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e(Tensor): Shape [B, C]
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"""
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assert e.dtype == torch.float32
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with amp.autocast(dtype=torch.float32):
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e = (self.modulation.to(e.device) + e.unsqueeze(1)).chunk(2, dim=1)
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x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
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return x
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class MLPProj(torch.nn.Module):
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def __init__(self, in_dim, out_dim):
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super().__init__()
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self.proj = torch.nn.Sequential(
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torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim),
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torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim),
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torch.nn.LayerNorm(out_dim))
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def forward(self, image_embeds):
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clip_extra_context_tokens = self.proj(image_embeds)
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return clip_extra_context_tokens
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class AudioProjModel(ModelMixin, ConfigMixin):
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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=12,
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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=768,
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context_tokens=32,
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norm_output_audio=False,
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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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# define multiple linear layers
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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):
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video_length = audio_embeds.shape[1] + audio_embeds_vf.shape[1]
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B, _, _, S, C = audio_embeds.shape
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# process audio of first frame
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audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c")
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batch_size, window_size, blocks, channels = audio_embeds.shape
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audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)
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# process audio of latter frame
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audio_embeds_vf = rearrange(audio_embeds_vf, "bz f w b c -> (bz f) w b c")
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batch_size_vf, window_size_vf, blocks_vf, channels_vf = audio_embeds_vf.shape
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audio_embeds_vf = audio_embeds_vf.view(batch_size_vf, window_size_vf * blocks_vf * channels_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, "(bz f) c -> bz f c", bz=B)
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audio_embeds_vf = rearrange(audio_embeds_vf, "(bz f) c -> bz f c", bz=B)
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audio_embeds_c = torch.concat([audio_embeds, audio_embeds_vf], dim=1)
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batch_size_c, N_t, C_a = audio_embeds_c.shape
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audio_embeds_c = audio_embeds_c.view(batch_size_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(batch_size_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, "(bz f) m c -> bz f m c", f=video_length)
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return context_tokens
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class WanModel(ModelMixin, ConfigMixin):
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r"""
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Wan diffusion backbone supporting both text-to-video and image-to-video.
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"""
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ignore_for_config = [
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'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size'
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]
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_no_split_modules = ['WanAttentionBlock']
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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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# audio params
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audio_window=5,
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intermediate_dim=512,
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output_dim=768,
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context_tokens=32,
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vae_scale=4, # vae timedownsample scale
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norm_input_visual=True,
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norm_output_audio=True):
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super().__init__()
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assert model_type == 'i2v', 'MultiTalk model requires your model_type is i2v.'
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self.model_type = model_type
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self.patch_size = patch_size
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self.text_len = text_len
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self.in_dim = in_dim
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self.dim = dim
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self.ffn_dim = ffn_dim
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self.freq_dim = freq_dim
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self.text_dim = text_dim
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self.out_dim = out_dim
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self.num_heads = num_heads
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self.num_layers = num_layers
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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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self.norm_output_audio = norm_output_audio
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self.audio_window = audio_window
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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) |