mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 14:16:57 +00:00
510 lines
18 KiB
Python
510 lines
18 KiB
Python
import math
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from dataclasses import dataclass
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import torch
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from einops import rearrange
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from torch import Tensor, nn
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from ..math import attention, rope
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def get_linear_split_map():
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hidden_size = 3072
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split_linear_modules_map = {
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"qkv" : {"mapped_modules" : ["q", "k", "v"] , "split_sizes": [hidden_size, hidden_size, hidden_size]},
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"linear1" : {"mapped_modules" : ["linear1_attn_q", "linear1_attn_k", "linear1_attn_v", "linear1_mlp"] , "split_sizes": [hidden_size, hidden_size, hidden_size, 7*hidden_size- 3*hidden_size]},
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"linear1_qkv" : {"mapped_modules" : ["linear1_attn_q", "linear1_attn_k", "linear1_attn_v"] , "split_sizes": [hidden_size, hidden_size, hidden_size]},
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}
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return split_linear_modules_map
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class EmbedND(nn.Module):
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def __init__(self, dim: int, theta: int, axes_dim: list[int]):
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super().__init__()
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self.dim = dim
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self.theta = theta
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self.axes_dim = axes_dim
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def forward(self, ids: Tensor) -> Tensor:
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n_axes = ids.shape[-1]
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emb = torch.cat(
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[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
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dim=-3,
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)
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return emb.unsqueeze(1)
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def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
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"""
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Create sinusoidal timestep embeddings.
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:param t: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, D) Tensor of positional embeddings.
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"""
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t = time_factor * t
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half = dim // 2
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freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
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t.device
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)
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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if torch.is_floating_point(t):
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embedding = embedding.to(t)
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return embedding
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class MLPEmbedder(nn.Module):
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def __init__(self, in_dim: int, hidden_dim: int):
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super().__init__()
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self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
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self.silu = nn.SiLU()
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self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
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def forward(self, x: Tensor) -> Tensor:
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return self.out_layer(self.silu(self.in_layer(x)))
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int):
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super().__init__()
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self.scale = nn.Parameter(torch.ones(dim))
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def forward(self, x: Tensor):
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x_dtype = x.dtype
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x = x.float()
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rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
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return (x * rrms).to(dtype=x_dtype) * self.scale
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class QKNorm(torch.nn.Module):
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def __init__(self, dim: int):
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super().__init__()
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self.query_norm = RMSNorm(dim)
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self.key_norm = RMSNorm(dim)
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def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
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if k != None:
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return self.key_norm(k).to(v)
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else:
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return self.query_norm(q).to(v)
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# q = self.query_norm(q)
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# k = self.key_norm(k)
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# return q.to(v), k.to(v)
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class SelfAttention(nn.Module):
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def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.norm = QKNorm(head_dim)
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self.proj = nn.Linear(dim, dim)
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def forward(self, x: Tensor, pe: Tensor) -> Tensor:
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raise Exception("not implemented")
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@dataclass
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class ModulationOut:
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shift: Tensor
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scale: Tensor
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gate: Tensor
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class ChromaModulationOut(ModulationOut):
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@classmethod
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def from_offset(cls, tensor: torch.Tensor, offset: int = 0):
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return cls(
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shift=tensor[:, offset : offset + 1, :],
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scale=tensor[:, offset + 1 : offset + 2, :],
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gate=tensor[:, offset + 2 : offset + 3, :],
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)
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def split_mlp(mlp, x, divide = 8):
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x_shape = x.shape
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x = x.view(-1, x.shape[-1])
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chunk_size = int(x.shape[0]/divide)
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chunk_size = int(x_shape[1]/divide)
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x_chunks = torch.split(x, chunk_size)
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for i, x_chunk in enumerate(x_chunks):
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mlp_chunk = mlp[0](x_chunk)
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mlp_chunk = mlp[1](mlp_chunk)
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x_chunk[...] = mlp[2](mlp_chunk)
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return x.reshape(x_shape)
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class Modulation(nn.Module):
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def __init__(self, dim: int, double: bool):
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super().__init__()
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self.is_double = double
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self.multiplier = 6 if double else 3
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self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
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def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
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out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
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return (
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ModulationOut(*out[:3]),
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ModulationOut(*out[3:]) if self.is_double else None,
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)
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class DoubleStreamBlock(nn.Module):
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def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, chroma_modulation = False):
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super().__init__()
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mlp_hidden_dim = int(hidden_size * mlp_ratio)
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self.num_heads = num_heads
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self.hidden_size = hidden_size
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self.chroma_modulation = chroma_modulation
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if not chroma_modulation:
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self.img_mod = Modulation(hidden_size, double=True)
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self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
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self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.img_mlp = nn.Sequential(
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nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
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nn.GELU(approximate="tanh"),
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nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
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)
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if not chroma_modulation:
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self.txt_mod = Modulation(hidden_size, double=True)
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self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
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self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.txt_mlp = nn.Sequential(
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nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
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nn.GELU(approximate="tanh"),
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nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
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)
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def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
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if self.chroma_modulation:
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(img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
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else:
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img_mod1, img_mod2 = self.img_mod(vec)
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txt_mod1, txt_mod2 = self.txt_mod(vec)
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# prepare image for attention
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img_modulated = self.img_norm1(img)
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img_modulated.mul_(1 + img_mod1.scale)
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img_modulated.add_(img_mod1.shift)
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shape = (*img_modulated.shape[:2], self.num_heads, int(img_modulated.shape[-1] / self.num_heads) )
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img_q = self.img_attn.q(img_modulated).view(*shape).transpose(1,2)
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img_k = self.img_attn.k(img_modulated).view(*shape).transpose(1,2)
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img_v = self.img_attn.v(img_modulated).view(*shape).transpose(1,2)
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del img_modulated
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img_q= self.img_attn.norm(img_q, None, img_v)
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img_k = self.img_attn.norm(None, img_k, img_v)
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# prepare txt for attention
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txt_modulated = self.txt_norm1(txt)
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txt_modulated.mul_(1 + txt_mod1.scale)
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txt_modulated.add_(txt_mod1.shift)
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shape = (*txt_modulated.shape[:2], self.num_heads, int(txt_modulated.shape[-1] / self.num_heads) )
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txt_q = self.txt_attn.q(txt_modulated).view(*shape).transpose(1,2)
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txt_k = self.txt_attn.k(txt_modulated).view(*shape).transpose(1,2)
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txt_v = self.txt_attn.v(txt_modulated).view(*shape).transpose(1,2)
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del txt_modulated
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txt_q = self.txt_attn.norm(txt_q, None, txt_v)
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txt_k = self.txt_attn.norm(None, txt_k, txt_v)
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# run actual attention
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q = torch.cat((txt_q, img_q), dim=2)
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del txt_q, img_q
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k = torch.cat((txt_k, img_k), dim=2)
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del txt_k, img_k
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v = torch.cat((txt_v, img_v), dim=2)
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del txt_v, img_v
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qkv_list = [q, k, v]
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del q, k, v
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attn = attention(qkv_list, pe=pe)
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txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
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# calculate the img blocks
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img.addcmul_(self.img_attn.proj(img_attn), img_mod1.gate)
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mod_img = self.img_norm2(img)
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mod_img.mul_(1 + img_mod2.scale)
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mod_img.add_(img_mod2.shift)
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mod_img = split_mlp(self.img_mlp, mod_img)
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# mod_img = self.img_mlp(mod_img)
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img.addcmul_( mod_img, img_mod2.gate)
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mod_img = None
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# calculate the txt blocks
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txt.addcmul_(self.txt_attn.proj(txt_attn), txt_mod1.gate)
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txt.addcmul_(self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift), txt_mod2.gate)
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return img, txt
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class SingleStreamBlock(nn.Module):
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"""
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A DiT block with parallel linear layers as described in
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https://arxiv.org/abs/2302.05442 and adapted modulation interface.
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"""
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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mlp_ratio: float = 4.0,
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qk_scale: float | None = None,
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chroma_modulation = False,
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):
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super().__init__()
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self.hidden_dim = hidden_size
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self.num_heads = num_heads
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self.chroma_modulation = chroma_modulation
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head_dim = hidden_size // num_heads
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self.scale = qk_scale or head_dim**-0.5
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self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
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# qkv and mlp_in
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self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
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# proj and mlp_out
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self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
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self.norm = QKNorm(head_dim)
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self.hidden_size = hidden_size
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self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.mlp_act = nn.GELU(approximate="tanh")
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if not chroma_modulation:
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self.modulation = Modulation(hidden_size, double=False)
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def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
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if self.chroma_modulation:
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mod = vec
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else:
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mod, _ = self.modulation(vec)
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x_mod = self.pre_norm(x)
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x_mod.mul_(1 + mod.scale)
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x_mod.add_(mod.shift)
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##### More spagheti VRAM optimizations done by DeepBeepMeep !
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# I am sure you are a nice person and as you copy this code, you will give me proper credits:
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# Please link to https://github.com/deepbeepmeep/Wan2GP and @deepbeepmeep on twitter
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# x_mod = (1 + mod.scale) * x + mod.shift
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shape = (*x_mod.shape[:2], self.num_heads, int(x_mod.shape[-1] / self.num_heads) )
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q = self.linear1_attn_q(x_mod).view(*shape).transpose(1,2)
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k = self.linear1_attn_k(x_mod).view(*shape).transpose(1,2)
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v = self.linear1_attn_v(x_mod).view(*shape).transpose(1,2)
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q = self.norm(q, None, v)
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k = self.norm(None, k, v)
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# compute attention
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qkv_list = [q, k, v]
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del q, k, v
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attn = attention(qkv_list, pe=pe)
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# compute activation in mlp stream, cat again and run second linear layer
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x_mod_shape = x_mod.shape
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x_mod = x_mod.view(-1, x_mod.shape[-1])
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chunk_size = int(x_mod_shape[1]/6)
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x_chunks = torch.split(x_mod, chunk_size)
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attn = attn.view(-1, attn.shape[-1])
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attn_chunks =torch.split(attn, chunk_size)
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for x_chunk, attn_chunk in zip(x_chunks, attn_chunks):
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mlp_chunk = self.linear1_mlp(x_chunk)
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mlp_chunk = self.mlp_act(mlp_chunk)
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attn_mlp_chunk = torch.cat((attn_chunk, mlp_chunk), -1)
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del attn_chunk, mlp_chunk
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x_chunk[...] = self.linear2(attn_mlp_chunk)
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del attn_mlp_chunk
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x_mod = x_mod.view(x_mod_shape)
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x.addcmul_(x_mod, mod.gate)
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return x
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class LastLayer(nn.Module):
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def __init__(self, hidden_size: int, patch_size: int, out_channels: int, chroma_modulation = False):
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super().__init__()
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self.chroma_modulation = chroma_modulation
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self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
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if not chroma_modulation:
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self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
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def forward(self, x: Tensor, vec: Tensor) -> Tensor:
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if self.chroma_modulation:
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shift, scale = vec
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shift = shift.squeeze(1)
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scale = scale.squeeze(1)
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else:
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shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
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# x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
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x = torch.addcmul(shift[:, None, :], 1 + scale[:, None, :], self.norm_final(x))
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x = self.linear(x)
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return x
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class DistilledGuidance(nn.Module):
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def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5):
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super().__init__()
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self.in_proj = nn.Linear(in_dim, hidden_dim, bias=True)
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self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim) for x in range( n_layers)])
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self.norms = nn.ModuleList([RMSNorm(hidden_dim) for x in range( n_layers)])
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self.out_proj = nn.Linear(hidden_dim, out_dim)
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def forward(self, x: Tensor) -> Tensor:
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x = self.in_proj(x)
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for layer, norms in zip(self.layers, self.norms):
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x = x + layer(norms(x))
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x = self.out_proj(x)
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return x
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class SigLIPMultiFeatProjModel(torch.nn.Module):
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"""
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SigLIP Multi-Feature Projection Model for processing style features from different layers
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and projecting them into a unified hidden space.
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Args:
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siglip_token_nums (int): Number of SigLIP tokens, default 257
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style_token_nums (int): Number of style tokens, default 256
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siglip_token_dims (int): Dimension of SigLIP tokens, default 1536
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hidden_size (int): Hidden layer size, default 3072
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context_layer_norm (bool): Whether to use context layer normalization, default False
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"""
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def __init__(
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self,
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siglip_token_nums: int = 257,
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style_token_nums: int = 256,
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siglip_token_dims: int = 1536,
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hidden_size: int = 3072,
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context_layer_norm: bool = False,
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):
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super().__init__()
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# High-level feature processing (layer -2)
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self.high_embedding_linear = nn.Sequential(
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nn.Linear(siglip_token_nums, style_token_nums),
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nn.SiLU()
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)
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self.high_layer_norm = (
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nn.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity()
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)
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self.high_projection = nn.Linear(siglip_token_dims, hidden_size, bias=True)
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# Mid-level feature processing (layer -11)
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self.mid_embedding_linear = nn.Sequential(
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nn.Linear(siglip_token_nums, style_token_nums),
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nn.SiLU()
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)
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self.mid_layer_norm = (
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nn.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity()
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)
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self.mid_projection = nn.Linear(siglip_token_dims, hidden_size, bias=True)
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# Low-level feature processing (layer -20)
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self.low_embedding_linear = nn.Sequential(
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nn.Linear(siglip_token_nums, style_token_nums),
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nn.SiLU()
|
|
)
|
|
self.low_layer_norm = (
|
|
nn.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity()
|
|
)
|
|
self.low_projection = nn.Linear(siglip_token_dims, hidden_size, bias=True)
|
|
|
|
def forward(self, siglip_outputs):
|
|
"""
|
|
Forward pass function
|
|
|
|
Args:
|
|
siglip_outputs: Output from SigLIP model, containing hidden_states
|
|
|
|
Returns:
|
|
torch.Tensor: Concatenated multi-layer features with shape [bs, 3*style_token_nums, hidden_size]
|
|
"""
|
|
dtype = next(self.high_embedding_linear.parameters()).dtype
|
|
|
|
# Process high-level features (layer -2)
|
|
high_embedding = self._process_layer_features(
|
|
siglip_outputs.hidden_states[-2],
|
|
self.high_embedding_linear,
|
|
self.high_layer_norm,
|
|
self.high_projection,
|
|
dtype
|
|
)
|
|
|
|
# Process mid-level features (layer -11)
|
|
mid_embedding = self._process_layer_features(
|
|
siglip_outputs.hidden_states[-11],
|
|
self.mid_embedding_linear,
|
|
self.mid_layer_norm,
|
|
self.mid_projection,
|
|
dtype
|
|
)
|
|
|
|
# Process low-level features (layer -20)
|
|
low_embedding = self._process_layer_features(
|
|
siglip_outputs.hidden_states[-20],
|
|
self.low_embedding_linear,
|
|
self.low_layer_norm,
|
|
self.low_projection,
|
|
dtype
|
|
)
|
|
|
|
# Concatenate features from all layers
|
|
return torch.cat((high_embedding, mid_embedding, low_embedding), dim=1)
|
|
|
|
def _process_layer_features(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
embedding_linear: nn.Module,
|
|
layer_norm: nn.Module,
|
|
projection: nn.Module,
|
|
dtype: torch.dtype
|
|
) -> torch.Tensor:
|
|
"""
|
|
Helper function to process features from a single layer
|
|
|
|
Args:
|
|
hidden_states: Input hidden states [bs, seq_len, dim]
|
|
embedding_linear: Embedding linear layer
|
|
layer_norm: Layer normalization
|
|
projection: Projection layer
|
|
dtype: Target data type
|
|
|
|
Returns:
|
|
torch.Tensor: Processed features [bs, style_token_nums, hidden_size]
|
|
"""
|
|
# Transform dimensions: [bs, seq_len, dim] -> [bs, dim, seq_len] -> [bs, dim, style_token_nums] -> [bs, style_token_nums, dim]
|
|
embedding = embedding_linear(
|
|
hidden_states.to(dtype).transpose(1, 2)
|
|
).transpose(1, 2)
|
|
|
|
# Apply layer normalization
|
|
embedding = layer_norm(embedding)
|
|
|
|
# Project to target hidden space
|
|
embedding = projection(embedding)
|
|
|
|
return embedding
|