fix apply_t5_attn_mask to work

README.md

@@ -4,6 +4,8 @@ This repository contains training, generation and utility scripts for Stable Dif
 
 This feature is experimental. The options and the training script may change in the future. Please let us know if you have any idea to improve the training.
 
+Aug 11, 2024: Fix `--apply_t5_attn_mask` option to work. Please remove and re-generate the latents cache file if you have used the option before.
+
 Aug 10, 2024:  LoRA key prefix is changed to `lora_unet` from `lora_flex` to make it compatible with ComfyUI.
 
 Please update PyTorch to 2.4.0. We have tested with PyTorch 2.4.0 with CUDA 12.4. We also updated `accelerate` to 0.33.0 just to be safe. 

flux_train_network.py

@@ -67,14 +67,16 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
         return latents_caching_strategy
 
     def get_text_encoding_strategy(self, args):
-        return strategy_flux.FluxTextEncodingStrategy()
+        return strategy_flux.FluxTextEncodingStrategy(apply_t5_attn_mask=args.apply_t5_attn_mask)
 
     def get_models_for_text_encoding(self, args, accelerator, text_encoders):
         return text_encoders  # + [accelerator.unwrap_model(text_encoders[-1])]
 
     def get_text_encoder_outputs_caching_strategy(self, args):
         if args.cache_text_encoder_outputs:
-            return strategy_flux.FluxTextEncoderOutputsCachingStrategy(args.cache_text_encoder_outputs_to_disk, None, False)
+            return strategy_flux.FluxTextEncoderOutputsCachingStrategy(
+                args.cache_text_encoder_outputs_to_disk, None, False, apply_t5_attn_mask=args.apply_t5_attn_mask
+            )
         else:
             return None
 

library/strategy_flux.py

@@ -41,17 +41,24 @@ class FluxTokenizeStrategy(TokenizeStrategy):
 
 
 class FluxTextEncodingStrategy(TextEncodingStrategy):
-    def __init__(self) -> None:
-        pass
+    def __init__(self, apply_t5_attn_mask: Optional[bool] = None) -> None:
+        """
+        Args:
+            apply_t5_attn_mask: Default value for apply_t5_attn_mask.
+        """
+        self.apply_t5_attn_mask = apply_t5_attn_mask
 
     def encode_tokens(
         self,
         tokenize_strategy: TokenizeStrategy,
         models: List[Any],
         tokens: List[torch.Tensor],
-        apply_t5_attn_mask: bool = False,
+        apply_t5_attn_mask: Optional[bool] = None,
     ) -> List[torch.Tensor]:
-        # supports single model inference only
+        # supports single model inference
+
+        if apply_t5_attn_mask is None:
+            apply_t5_attn_mask = self.apply_t5_attn_mask
 
         clip_l, t5xxl = models
         l_tokens, t5_tokens = tokens[:2]
@@ -137,8 +144,9 @@ class FluxTextEncoderOutputsCachingStrategy(TextEncoderOutputsCachingStrategy):
 
         tokens_and_masks = tokenize_strategy.tokenize(captions)
         with torch.no_grad():
+            # attn_mask is not applied when caching to disk: it is applied when loading from disk
             l_pooled, t5_out, txt_ids = flux_text_encoding_strategy.encode_tokens(
-                tokenize_strategy, models, tokens_and_masks, self.apply_t5_attn_mask
+                tokenize_strategy, models, tokens_and_masks, not self.cache_to_disk
             )
 
         if l_pooled.dtype == torch.bfloat16: