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Merge 10ff863b8a into 1dae34b0af

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johnr14
2026-04-01 13:10:21 +00:00
committed by GitHub
parent 1dae34b0af 10ff863b8a
commit 5fc26ed2ea
3 changed files with 93 additions and 31 deletions
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29
library/strategy_flux.py
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@@ -21,19 +21,25 @@ T5_XXL_TOKENIZER_ID = "google/t5-v1_1-xxl"
class FluxTokenizeStrategy(TokenizeStrategy):
def __init__(self, t5xxl_max_length: int = 512, tokenizer_cache_dir: Optional[str] = None) -> None:
def __init__(self, t5xxl_max_length: int = 512, tokenizer_cache_dir: Optional[str] = None, use_clip_l: bool = True) -> None:
self.t5xxl_max_length = t5xxl_max_length
self.clip_l = self._load_tokenizer(CLIPTokenizer, CLIP_L_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
self.use_clip_l = use_clip_l
if self.use_clip_l:
self.clip_l = self._load_tokenizer(CLIPTokenizer, CLIP_L_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
self.t5xxl = self._load_tokenizer(T5TokenizerFast, T5_XXL_TOKENIZER_ID, tokenizer_cache_dir=tokenizer_cache_dir)
def tokenize(self, text: Union[str, List[str]]) -> List[torch.Tensor]:
text = [text] if isinstance(text, str) else text
l_tokens = self.clip_l(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
t5_tokens = self.t5xxl(text, max_length=self.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt")
if self.use_clip_l:
l_tokens = self.clip_l(text, max_length=77, padding="max_length", truncation=True, return_tensors="pt")
l_tokens = l_tokens["input_ids"]
else:
# For Chroma, return None for CLIP-L tokens
l_tokens = None
t5_tokens = self.t5xxl(text, max_length=self.t5xxl_max_length, padding="max_length", truncation=True, return_tensors="pt")
t5_attn_mask = t5_tokens["attention_mask"]
l_tokens = l_tokens["input_ids"]
t5_tokens = t5_tokens["input_ids"]
return [l_tokens, t5_tokens, t5_attn_mask]
@@ -63,24 +69,27 @@ class FluxTextEncodingStrategy(TextEncodingStrategy):
l_tokens, t5_tokens = tokens[:2]
t5_attn_mask = tokens[2] if len(tokens) > 2 else None
# clip_l is None when using T5 only
# Handle Chroma case where CLIP-L is not used
if clip_l is not None and l_tokens is not None:
l_pooled = clip_l(l_tokens.to(clip_l.device))["pooler_output"]
else:
l_pooled = None
# For Chroma, create a dummy tensor with the right shape
if t5_tokens is not None:
batch_size = t5_tokens.shape[0]
l_pooled = torch.zeros(batch_size, 768, device=t5_tokens.device, dtype=torch.float32)
else:
l_pooled = None
# t5xxl is None when using CLIP only
if t5xxl is not None and t5_tokens is not None:
# t5_out is [b, max length, 4096]
attention_mask = None if not apply_t5_attn_mask else t5_attn_mask.to(t5xxl.device)
t5_out, _ = t5xxl(t5_tokens.to(t5xxl.device), attention_mask, return_dict=False, output_hidden_states=True)
# if zero_pad_t5_output:
# t5_out = t5_out * t5_attn_mask.to(t5_out.device).unsqueeze(-1)
txt_ids = torch.zeros(t5_out.shape[0], t5_out.shape[1], 3, device=t5_out.device)
else:
t5_out = None
txt_ids = None
t5_attn_mask = None # caption may be dropped/shuffled, so t5_attn_mask should not be used to make sure the mask is same as the cached one
t5_attn_mask = None
return [l_pooled, t5_out, txt_ids, t5_attn_mask] # returns t5_attn_mask for attention mask in transformer