Fix Qwen attention mask crash with diffusers >=0.37 (#748)

* Fix Qwen Image mask handling

* Fix Qwen attention mask crash with diffusers >=0.37

diffusers v0.37 (PR #12987) optimizes all-ones attention masks to None
in encode_prompt() when there is no padding. This breaks ai-toolkit's
Qwen extensions which call .to() on the mask unconditionally.

Fix: reconstruct the all-ones mask at the boundary (get_prompt_embeds)
right after encode_prompt() returns. This keeps the rest of the code
unchanged and works with both old and new diffusers versions.

Also removes redundant duplicate mask assignments in qwen_image_edit
and qwen_image_edit_plus.

Fixes #740

extensions_built_in/diffusion_models/qwen_image/qwen_image.py

@@ -355,12 +355,16 @@ class QwenImageModel(BaseModel):
         if self.pipeline.text_encoder.device != self.device_torch:
             self.pipeline.text_encoder.to(self.device_torch)
         
-        max_sequence_length = 1024
-        
-        prompt_embeds, prompt_embeds_mask = self.pipeline._get_qwen_prompt_embeds(prompt, self.device_torch)
-        prompt_embeds = prompt_embeds[:, :max_sequence_length]
-        prompt_embeds_mask = prompt_embeds_mask[:, :max_sequence_length]
-
+        prompt_embeds, prompt_embeds_mask = self.pipeline.encode_prompt(
+            prompt,
+            device=self.device_torch,
+            num_images_per_prompt=1,
+        )
+        # diffusers >=0.37 returns None when all tokens are valid (no padding)
+        if prompt_embeds_mask is None:
+            prompt_embeds_mask = torch.ones(
+                prompt_embeds.shape[:2], device=prompt_embeds.device, dtype=torch.int64
+            )
         pe = PromptEmbeds(prompt_embeds)
         pe.attention_mask = prompt_embeds_mask
         return pe

extensions_built_in/diffusion_models/qwen_image/qwen_image_edit.py

@@ -197,6 +197,11 @@ class QwenImageEditModel(QwenImageModel):
             device=self.device_torch,
             num_images_per_prompt=1,
         )
+        # diffusers >=0.37 returns None when all tokens are valid (no padding)
+        if prompt_embeds_mask is None:
+            prompt_embeds_mask = torch.ones(
+                prompt_embeds.shape[:2], device=prompt_embeds.device, dtype=torch.int64
+            )
         pe = PromptEmbeds(prompt_embeds)
         pe.attention_mask = prompt_embeds_mask
         return pe
@@ -251,14 +256,13 @@ class QwenImageEditModel(QwenImageModel):
         )
         txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist()
         enc_hs = text_embeddings.text_embeds.to(self.device_torch, self.torch_dtype)
-        prompt_embeds_mask = text_embeddings.attention_mask.to(self.device_torch, dtype=torch.int64)
 
         noise_pred = self.transformer(
             hidden_states=latent_model_input.to(self.device_torch, self.torch_dtype),
             timestep=timestep / 1000,
             guidance=None,
             encoder_hidden_states=enc_hs,
-            encoder_hidden_states_mask=prompt_embeds_mask,
+            encoder_hidden_states_mask=prompt_embeds_mask.detach(),
             img_shapes=img_shapes,
             txt_seq_lens=txt_seq_lens,
             return_dict=False,

extensions_built_in/diffusion_models/qwen_image/qwen_image_edit_plus.py

@@ -192,6 +192,11 @@ class QwenImageEditPlusModel(QwenImageModel):
             device=self.device_torch,
             num_images_per_prompt=1,
         )
+        # diffusers >=0.37 returns None when all tokens are valid (no padding)
+        if prompt_embeds_mask is None:
+            prompt_embeds_mask = torch.ones(
+                prompt_embeds.shape[:2], device=prompt_embeds.device, dtype=torch.int64
+            )
         pe = PromptEmbeds(prompt_embeds)
         pe.attention_mask = prompt_embeds_mask
         return pe
@@ -323,9 +328,6 @@ class QwenImageEditPlusModel(QwenImageModel):
             )
             txt_seq_lens = prompt_embeds_mask.sum(dim=1).tolist()
             enc_hs = text_embeddings.text_embeds.to(self.device_torch, self.torch_dtype)
-            prompt_embeds_mask = text_embeddings.attention_mask.to(
-                self.device_torch, dtype=torch.int64
-            )
 
         noise_pred = self.transformer(
             hidden_states=latent_model_input.to(