mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 23:08:13 +00:00
651 lines
18 KiB
Python
651 lines
18 KiB
Python
import torch
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import torch.nn as nn
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from torch.nn import functional as F
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import math
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from typing import Tuple, Optional, List
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from einops import rearrange
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MEMORY_LAYOUT = {
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"flash": (
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lambda x: x.view(x.shape[0] * x.shape[1], *x.shape[2:]),
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lambda x: x,
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),
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"torch": (
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lambda x: x.transpose(1, 2),
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lambda x: x.transpose(1, 2),
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),
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"vanilla": (
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lambda x: x.transpose(1, 2),
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lambda x: x.transpose(1, 2),
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),
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}
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def attention(
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q,
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k,
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v,
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mode="torch",
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drop_rate=0,
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attn_mask=None,
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causal=False,
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max_seqlen_q=None,
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batch_size=1,
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):
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pre_attn_layout, post_attn_layout = MEMORY_LAYOUT[mode]
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if mode == "torch":
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if attn_mask is not None and attn_mask.dtype != torch.bool:
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attn_mask = attn_mask.to(q.dtype)
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x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal)
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x = post_attn_layout(x)
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b, s, a, d = x.shape
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out = x.reshape(b, s, -1)
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return out
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class CausalConv1d(nn.Module):
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def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", **kwargs):
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super().__init__()
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self.pad_mode = pad_mode
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padding = (kernel_size - 1, 0) # T
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self.time_causal_padding = padding
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self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
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def forward(self, x):
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x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
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return self.conv(x)
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class FaceEncoder(nn.Module):
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def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None):
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factory_kwargs = {"dtype": dtype, "device": device}
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super().__init__()
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self.num_heads = num_heads
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self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1)
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self.norm1 = nn.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.act = nn.SiLU()
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self.conv2 = CausalConv1d(1024, 1024, 3, stride=2)
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self.conv3 = CausalConv1d(1024, 1024, 3, stride=2)
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self.out_proj = nn.Linear(1024, hidden_dim)
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self.norm1 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.norm2 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.norm3 = nn.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.padding_tokens = nn.Parameter(torch.zeros(1, 1, 1, hidden_dim))
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def forward(self, x):
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x = rearrange(x, "b t c -> b c t")
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b, c, t = x.shape
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x = self.conv1_local(x)
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x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads)
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x = self.norm1(x)
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x = self.act(x)
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x = rearrange(x, "b t c -> b c t")
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x = self.conv2(x)
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x = rearrange(x, "b c t -> b t c")
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x = self.norm2(x)
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x = self.act(x)
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x = rearrange(x, "b t c -> b c t")
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x = self.conv3(x)
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x = rearrange(x, "b c t -> b t c")
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x = self.norm3(x)
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x = self.act(x)
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x = self.out_proj(x)
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x = rearrange(x, "(b n) t c -> b t n c", b=b)
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padding = self.padding_tokens.repeat(b, x.shape[1], 1, 1)
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x = torch.cat([x, padding], dim=-2)
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x_local = x.clone()
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return x_local
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class RMSNorm(nn.Module):
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def __init__(
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self,
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dim: int,
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elementwise_affine=True,
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eps: float = 1e-6,
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device=None,
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dtype=None,
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):
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"""
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Initialize the RMSNorm normalization layer.
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Args:
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dim (int): The dimension of the input tensor.
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eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
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Attributes:
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eps (float): A small value added to the denominator for numerical stability.
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weight (nn.Parameter): Learnable scaling parameter.
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"""
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.eps = eps
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if elementwise_affine:
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self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
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def _norm(self, x):
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"""
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Apply the RMSNorm normalization to the input tensor.
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Args:
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x (torch.Tensor): The input tensor.
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Returns:
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torch.Tensor: The normalized tensor.
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"""
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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"""
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Forward pass through the RMSNorm layer.
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Args:
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x (torch.Tensor): The input tensor.
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Returns:
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torch.Tensor: The output tensor after applying RMSNorm.
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"""
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output = self._norm(x.float()).type_as(x)
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if hasattr(self, "weight"):
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output = output * self.weight
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return output
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def get_norm_layer(norm_layer):
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"""
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Get the normalization layer.
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Args:
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norm_layer (str): The type of normalization layer.
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Returns:
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norm_layer (nn.Module): The normalization layer.
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"""
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if norm_layer == "layer":
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return nn.LayerNorm
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elif norm_layer == "rms":
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return RMSNorm
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else:
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raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
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class FaceAdapter(nn.Module):
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def __init__(
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self,
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hidden_dim: int,
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heads_num: int,
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qk_norm: bool = True,
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qk_norm_type: str = "rms",
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num_adapter_layers: int = 1,
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dtype=None,
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device=None,
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):
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factory_kwargs = {"dtype": dtype, "device": device}
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super().__init__()
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self.hidden_size = hidden_dim
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self.heads_num = heads_num
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self.fuser_blocks = nn.ModuleList(
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[
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FaceBlock(
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self.hidden_size,
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self.heads_num,
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qk_norm=qk_norm,
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qk_norm_type=qk_norm_type,
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**factory_kwargs,
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)
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for _ in range(num_adapter_layers)
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]
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)
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def forward(
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self,
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x: torch.Tensor,
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motion_embed: torch.Tensor,
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idx: int,
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freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None,
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freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None,
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) -> torch.Tensor:
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return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k)
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class FaceBlock(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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heads_num: int,
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qk_norm: bool = True,
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qk_norm_type: str = "rms",
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qk_scale: float = None,
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dtype: Optional[torch.dtype] = None,
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device: Optional[torch.device] = None,
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):
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.deterministic = False
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self.hidden_size = hidden_size
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self.heads_num = heads_num
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head_dim = hidden_size // heads_num
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self.scale = qk_scale or head_dim**-0.5
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self.linear1_kv = nn.Linear(hidden_size, hidden_size * 2, **factory_kwargs)
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self.linear1_q = nn.Linear(hidden_size, hidden_size, **factory_kwargs)
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self.linear2 = nn.Linear(hidden_size, hidden_size, **factory_kwargs)
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qk_norm_layer = get_norm_layer(qk_norm_type)
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self.q_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
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)
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self.k_norm = (
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qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
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)
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self.pre_norm_feat = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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self.pre_norm_motion = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
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def forward(
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self,
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x: torch.Tensor,
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motion_vec: torch.Tensor,
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motion_mask: Optional[torch.Tensor] = None,
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use_context_parallel=False,
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) -> torch.Tensor:
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B, T, N, C = motion_vec.shape
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T_comp = T
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x_motion = self.pre_norm_motion(motion_vec)
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x_feat = self.pre_norm_feat(x)
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kv = self.linear1_kv(x_motion)
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q = self.linear1_q(x_feat)
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k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num)
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q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num)
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# Apply QK-Norm if needed.
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q = self.q_norm(q).to(v)
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k = self.k_norm(k).to(v)
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k = rearrange(k, "B L N H D -> (B L) H N D")
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v = rearrange(v, "B L N H D -> (B L) H N D")
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q = rearrange(q, "B (L S) H D -> (B L) H S D", L=T_comp)
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# Compute attention.
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attn = F.scaled_dot_product_attention(q, k, v)
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attn = rearrange(attn, "(B L) H S D -> B (L S) (H D)", L=T_comp)
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output = self.linear2(attn)
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if motion_mask is not None:
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output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1)
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return output
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def custom_qr(input_tensor):
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original_dtype = input_tensor.dtype
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if original_dtype == torch.bfloat16:
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q, r = torch.linalg.qr(input_tensor.to(torch.float32))
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return q.to(original_dtype), r.to(original_dtype)
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return torch.linalg.qr(input_tensor)
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def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
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return F.leaky_relu(input + bias, negative_slope) * scale
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def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
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_, minor, in_h, in_w = input.shape
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kernel_h, kernel_w = kernel.shape
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out = input.view(-1, minor, in_h, 1, in_w, 1)
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out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
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out = out.view(-1, minor, in_h * up_y, in_w * up_x)
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out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
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out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0),
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max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0), ]
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out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
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w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
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out = F.conv2d(out, w)
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out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
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in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, )
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return out[:, :, ::down_y, ::down_x]
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def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
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return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
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def make_kernel(k):
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k = torch.tensor(k, dtype=torch.float32)
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if k.ndim == 1:
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k = k[None, :] * k[:, None]
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k /= k.sum()
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return k
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class FusedLeakyReLU(nn.Module):
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def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
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super().__init__()
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self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1))
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self.negative_slope = negative_slope
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self.scale = scale
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def forward(self, input):
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out = fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
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return out
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class Blur(nn.Module):
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def __init__(self, kernel, pad, upsample_factor=1):
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super().__init__()
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kernel = make_kernel(kernel)
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if upsample_factor > 1:
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kernel = kernel * (upsample_factor ** 2)
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self.kernel = torch.nn.Parameter(kernel)
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self.pad = pad
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def forward(self, input):
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return upfirdn2d(input, self.kernel, pad=self.pad)
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class ScaledLeakyReLU(nn.Module):
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def __init__(self, negative_slope=0.2):
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super().__init__()
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self.negative_slope = negative_slope
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def forward(self, input):
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return F.leaky_relu(input, negative_slope=self.negative_slope)
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class EqualConv2d(nn.Module):
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def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True):
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super().__init__()
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self.weight = nn.Parameter(torch.randn(out_channel, in_channel, kernel_size, kernel_size))
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self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
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self.stride = stride
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self.padding = padding
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if bias:
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self.bias = nn.Parameter(torch.zeros(out_channel))
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else:
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self.bias = None
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def forward(self, input):
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return F.conv2d(input, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding)
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def __repr__(self):
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return (
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f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
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f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
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)
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class EqualLinear(nn.Module):
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def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None):
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super().__init__()
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self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
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if bias:
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self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
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else:
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self.bias = None
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self.activation = activation
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self.scale = (1 / math.sqrt(in_dim)) * lr_mul
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self.lr_mul = lr_mul
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def forward(self, input):
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if self.activation:
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out = F.linear(input, self.weight * self.scale)
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out = fused_leaky_relu(out, self.bias * self.lr_mul)
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else:
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out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul)
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return out
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def __repr__(self):
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return (f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})')
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class ConvLayer(nn.Sequential):
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def __init__(
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self,
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in_channel,
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out_channel,
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kernel_size,
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downsample=False,
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blur_kernel=[1, 3, 3, 1],
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bias=True,
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activate=True,
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):
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layers = []
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if downsample:
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factor = 2
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p = (len(blur_kernel) - factor) + (kernel_size - 1)
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pad0 = (p + 1) // 2
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pad1 = p // 2
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layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
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stride = 2
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self.padding = 0
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else:
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stride = 1
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self.padding = kernel_size // 2
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layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=self.padding, stride=stride,
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bias=bias and not activate))
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if activate:
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if bias:
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layers.append(FusedLeakyReLU(out_channel))
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else:
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layers.append(ScaledLeakyReLU(0.2))
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super().__init__(*layers)
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class ResBlock(nn.Module):
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def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
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super().__init__()
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self.conv1 = ConvLayer(in_channel, in_channel, 3)
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self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
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self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False)
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|
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def forward(self, input):
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out = self.conv1(input)
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out = self.conv2(out)
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skip = self.skip(input)
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out = (out + skip) / math.sqrt(2)
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return out
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|
|
|
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class EncoderApp(nn.Module):
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def __init__(self, size, w_dim=512):
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super(EncoderApp, self).__init__()
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|
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channels = {
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4: 512,
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8: 512,
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16: 512,
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32: 512,
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64: 256,
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128: 128,
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256: 64,
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512: 32,
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1024: 16
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}
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|
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self.w_dim = w_dim
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log_size = int(math.log(size, 2))
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|
|
|
self.convs = nn.ModuleList()
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self.convs.append(ConvLayer(3, channels[size], 1))
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|
|
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in_channel = channels[size]
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for i in range(log_size, 2, -1):
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out_channel = channels[2 ** (i - 1)]
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self.convs.append(ResBlock(in_channel, out_channel))
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in_channel = out_channel
|
|
|
|
self.convs.append(EqualConv2d(in_channel, self.w_dim, 4, padding=0, bias=False))
|
|
|
|
def forward(self, x):
|
|
|
|
res = []
|
|
h = x
|
|
for conv in self.convs:
|
|
h = conv(h)
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|
res.append(h)
|
|
|
|
return res[-1].squeeze(-1).squeeze(-1), res[::-1][2:]
|
|
|
|
|
|
class Encoder(nn.Module):
|
|
def __init__(self, size, dim=512, dim_motion=20):
|
|
super(Encoder, self).__init__()
|
|
|
|
# appearance netmork
|
|
self.net_app = EncoderApp(size, dim)
|
|
|
|
# motion network
|
|
fc = [EqualLinear(dim, dim)]
|
|
for i in range(3):
|
|
fc.append(EqualLinear(dim, dim))
|
|
|
|
fc.append(EqualLinear(dim, dim_motion))
|
|
self.fc = nn.Sequential(*fc)
|
|
|
|
def enc_app(self, x):
|
|
h_source = self.net_app(x)
|
|
return h_source
|
|
|
|
def enc_motion(self, x):
|
|
h, _ = self.net_app(x)
|
|
h_motion = self.fc(h)
|
|
return h_motion
|
|
|
|
|
|
class Direction(nn.Module):
|
|
def __init__(self, motion_dim):
|
|
super(Direction, self).__init__()
|
|
self.weight = nn.Parameter(torch.randn(512, motion_dim))
|
|
|
|
def forward(self, input):
|
|
|
|
weight = self.weight + 1e-8
|
|
Q, R = custom_qr(weight)
|
|
if input is None:
|
|
return Q
|
|
else:
|
|
input_diag = torch.diag_embed(input) # alpha, diagonal matrix
|
|
out = torch.matmul(input_diag, Q.T)
|
|
out = torch.sum(out, dim=1)
|
|
return out
|
|
|
|
|
|
class Synthesis(nn.Module):
|
|
def __init__(self, motion_dim):
|
|
super(Synthesis, self).__init__()
|
|
self.direction = Direction(motion_dim)
|
|
|
|
|
|
class Generator(nn.Module):
|
|
def __init__(self, size, style_dim=512, motion_dim=20):
|
|
super().__init__()
|
|
|
|
self.enc = Encoder(size, style_dim, motion_dim)
|
|
self.dec = Synthesis(motion_dim)
|
|
|
|
def get_motion(self, img):
|
|
#motion_feat = self.enc.enc_motion(img)
|
|
motion_feat = torch.utils.checkpoint.checkpoint((self.enc.enc_motion), img, use_reentrant=True)
|
|
motion = self.dec.direction(motion_feat)
|
|
return motion
|
|
|
|
|
|
class WanAnimateAdapter(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.pose_patch_embedding = torch.nn.Conv3d(16, 5120, kernel_size=(1, 2, 2), stride=(1, 2, 2))
|
|
self.motion_encoder = Generator(size=512, style_dim=512, motion_dim=20)
|
|
self.face_adapter = FaceAdapter(heads_num=40, hidden_dim=5120, num_adapter_layers=40 // 5)
|
|
self.face_encoder = FaceEncoder(in_dim=512, hidden_dim=5120, num_heads=4)
|
|
|
|
def after_patch_embedding(self, x: List[torch.Tensor], pose_latents, face_pixel_values):
|
|
pose_latents = self.pose_patch_embedding(pose_latents)
|
|
x[:, :, 1:] += pose_latents
|
|
|
|
b,c,T,h,w = face_pixel_values.shape
|
|
face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w")
|
|
|
|
encode_bs = 8
|
|
face_pixel_values_tmp = []
|
|
for i in range(math.ceil(face_pixel_values.shape[0]/encode_bs)):
|
|
face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i*encode_bs:(i+1)*encode_bs]))
|
|
|
|
motion_vec = torch.cat(face_pixel_values_tmp)
|
|
|
|
motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T)
|
|
motion_vec = self.face_encoder(motion_vec)
|
|
|
|
B, L, H, C = motion_vec.shape
|
|
pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec)
|
|
motion_vec = torch.cat([pad_face, motion_vec], dim=1)
|
|
return x, motion_vec
|
|
|
|
def after_transformer_block(self, block_idx, x, motion_vec, motion_masks=None):
|
|
if block_idx % 5 == 0:
|
|
adapter_args = [x, motion_vec, motion_masks, False]
|
|
residual_out = self.face_adapter.fuser_blocks[block_idx // 5](*adapter_args)
|
|
x = residual_out + x
|
|
return x
|