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e84ba1f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | """UMT5 text encoder used by Wan2.2 (custom diffsynth weight layout).
Architecture is a stripped UMT5: tied vocab, GELU-gated FFN, T5-style relative
position bias, RMS-style layer norm. Matches the keys in
`models_t5_umt5-xxl-enc-bf16.pth`.
"""
import html
import math
import re
import ftfy
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer
def _fp16_clamp(x):
if x.dtype == torch.float16 and torch.isinf(x).any():
c = torch.finfo(x.dtype).max - 1000
x = torch.clamp(x, min=-c, max=c)
return x
class _GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(
math.sqrt(2.0 / math.pi) * (x + 0.044715 * x.pow(3))))
class _T5LayerNorm(nn.Module):
def __init__(self, dim, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x):
x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) + self.eps)
if self.weight.dtype in (torch.float16, torch.bfloat16):
x = x.type_as(self.weight)
return self.weight * x
class _T5Attention(nn.Module):
def __init__(self, dim, dim_attn, num_heads):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim_attn // num_heads
self.q = nn.Linear(dim, dim_attn, bias=False)
self.k = nn.Linear(dim, dim_attn, bias=False)
self.v = nn.Linear(dim, dim_attn, bias=False)
self.o = nn.Linear(dim_attn, dim, bias=False)
def forward(self, x, mask=None, pos_bias=None):
b, n, c = x.size(0), self.num_heads, self.head_dim
q = self.q(x).view(b, -1, n, c)
k = self.k(x).view(b, -1, n, c)
v = self.v(x).view(b, -1, n, c)
attn_bias = x.new_zeros(b, n, q.size(1), k.size(1))
if pos_bias is not None:
attn_bias = attn_bias + pos_bias
if mask is not None:
mask = mask.view(b, 1, 1, -1) if mask.ndim == 2 else mask.unsqueeze(1)
attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min)
attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias
attn = F.softmax(attn.float(), dim=-1).type_as(attn)
out = torch.einsum('bnij,bjnc->binc', attn, v).reshape(b, -1, n * c)
return self.o(out)
class _T5FeedForward(nn.Module):
def __init__(self, dim, dim_ffn):
super().__init__()
self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), _GELU())
self.fc1 = nn.Linear(dim, dim_ffn, bias=False)
self.fc2 = nn.Linear(dim_ffn, dim, bias=False)
def forward(self, x):
return self.fc2(self.fc1(x) * self.gate(x))
class _T5SelfAttention(nn.Module):
def __init__(self, dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos):
super().__init__()
self.norm1 = _T5LayerNorm(dim)
self.attn = _T5Attention(dim, dim_attn, num_heads)
self.norm2 = _T5LayerNorm(dim)
self.ffn = _T5FeedForward(dim, dim_ffn)
self.pos_embedding = None if shared_pos else _T5RelativeEmbedding(
num_buckets, num_heads, bidirectional=True)
def forward(self, x, mask=None, pos_bias=None):
e = pos_bias if self.pos_embedding is None else self.pos_embedding(x.size(1), x.size(1))
x = _fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e))
x = _fp16_clamp(x + self.ffn(self.norm2(x)))
return x
class _T5RelativeEmbedding(nn.Module):
def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128):
super().__init__()
self.num_buckets = num_buckets
self.bidirectional = bidirectional
self.max_dist = max_dist
self.embedding = nn.Embedding(num_buckets, num_heads)
def forward(self, lq, lk):
device = self.embedding.weight.device
rel = torch.arange(lk, device=device).unsqueeze(0) - torch.arange(lq, device=device).unsqueeze(1)
rel = self._bucket(rel)
return self.embedding(rel).permute(2, 0, 1).unsqueeze(0).contiguous()
def _bucket(self, rel):
if self.bidirectional:
n = self.num_buckets // 2
buckets = (rel > 0).long() * n
rel = rel.abs()
else:
n = self.num_buckets
buckets = 0
rel = -torch.min(rel, torch.zeros_like(rel))
max_exact = n // 2
large = max_exact + (torch.log(rel.float() / max_exact) /
math.log(self.max_dist / max_exact) * (n - max_exact)).long()
large = torch.min(large, torch.full_like(large, n - 1))
buckets += torch.where(rel < max_exact, rel, large)
return buckets
class WanTextEncoder(nn.Module):
"""UMT5 encoder used by Wan2.2-TI2V-5B; loads `models_t5_umt5-xxl-enc-bf16.pth`."""
def __init__(self, vocab=256384, dim=4096, dim_attn=4096, dim_ffn=10240,
num_heads=64, num_layers=24, num_buckets=32, shared_pos=False):
super().__init__()
self.token_embedding = nn.Embedding(vocab, dim)
self.pos_embedding = _T5RelativeEmbedding(
num_buckets, num_heads, bidirectional=True) if shared_pos else None
self.blocks = nn.ModuleList([
_T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets, shared_pos)
for _ in range(num_layers)
])
self.norm = _T5LayerNorm(dim)
self.shared_pos = shared_pos
def forward(self, ids, mask=None):
x = self.token_embedding(ids)
e = self.pos_embedding(x.size(1), x.size(1)) if self.shared_pos else None
for blk in self.blocks:
x = blk(x, mask=mask, pos_bias=e)
return self.norm(x)
def _whitespace_clean(text: str) -> str:
"""Bit-identical port of `diffsynth.models.wan_video_text_encoder.
whitespace_clean(basic_clean(text))`. Run on every prompt before tokenizing
— both the training-time pipeline and the cache precompute do this, so
skipping it makes the negative prompt's T5 embedding diverge (e.g. the
Chinese fullwidth comma `，` U+FF0C → ASCII `,` U+002C swap maps to a
completely different UMT5 token id). Don't drop this."""
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
text = text.strip()
text = re.sub(r"\s+", " ", text)
return text.strip()
class WanTokenizer:
"""Wraps HF AutoTokenizer with the (return_mask, max_length) interface used by Wan."""
def __init__(self, path: str, seq_len: int = 512):
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.seq_len = seq_len
def __call__(self, text):
if isinstance(text, str):
text = [text]
text = [_whitespace_clean(t) for t in text]
out = self.tokenizer(text, return_tensors='pt', padding='max_length',
truncation=True, max_length=self.seq_len,
add_special_tokens=True)
return out.input_ids, out.attention_mask
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