Update pipeline.py

pipeline.py

@@ -163,7 +163,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
         prompt: Union[str, List[str]],
         num_images_per_prompt: int = 1,
         device: Optional[torch.device] = None,
-        clip_skip: Optional[int] = None,
     ):
         device = device or self._execution_device
 
@@ -190,11 +189,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
             )
         prompt_embeds = self.text_encoder(text_input_ids.to(device), output_hidden_states=False)
 
-        if clip_skip is None:
-            prompt_embeds = prompt_embeds.hidden_states[-2]
-        else:
-            prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
-
         # Use pooled output of CLIPTextModel
         prompt_embeds = prompt_embeds.pooler_output
         prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
@@ -220,7 +214,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
         pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
         negative_pooled_prompt_2_embed: Optional[torch.FloatTensor] = None,
         negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        clip_skip: Optional[int] = None,
         max_sequence_length: int = 512,
         lora_scale: Optional[float] = None,
     ): 
@@ -258,7 +251,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
                 prompt=prompt,
                 device=device,
                 num_images_per_prompt=num_images_per_prompt,
-                clip_skip=clip_skip,
             )
             prompt_embeds = self._get_t5_prompt_embeds(
                 prompt=prompt_2,
@@ -293,7 +285,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
             negative_prompt,
             device=device,
             num_images_per_prompt=num_images_per_prompt,
-            clip_skip=None,
             )
         negative_clip_prompt_embeds = torch.cat([negative_prompt_embed, negative_prompt_2_embed], dim=-1)
 
@@ -539,7 +530,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        clip_skip: Optional[int] = None,
         max_sequence_length: int = 300,
         **kwargs,
     ):
@@ -562,7 +552,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
         )
 
         self._guidance_scale = guidance_scale
-        self._clip_skip = clip_skip
         self._joint_attention_kwargs = joint_attention_kwargs
         self._interrupt = False
 
@@ -597,7 +586,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
             pooled_prompt_embeds=pooled_prompt_embeds,
             negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
             device=device,
-            clip_skip=self.clip_skip,
             num_images_per_prompt=num_images_per_prompt,
             max_sequence_length=max_sequence_length,
             lora_scale=lora_scale,
@@ -723,7 +711,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-        clip_skip: Optional[int] = None,
         max_sequence_length: int = 300,
         **kwargs,
     ):
@@ -746,7 +733,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
         )
 
         self._guidance_scale = guidance_scale
-        self._clip_skip = clip_skip
         self._joint_attention_kwargs = joint_attention_kwargs
         self._interrupt = False
 
@@ -781,7 +767,6 @@ class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFile
             pooled_prompt_embeds=pooled_prompt_embeds,
             negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
             device=device,
-            clip_skip=self.clip_skip,
             num_images_per_prompt=num_images_per_prompt,
             max_sequence_length=max_sequence_length,
             lora_scale=lora_scale,