make fix-copies

Author: a-r-r-o-w

src/diffusers/pipelines/pag/pipeline_pag_sana.py

@@ -180,7 +180,7 @@ def __init__(
             pag_attn_processors=(PAGCFGSanaLinearAttnProcessor2_0(), PAGIdentitySanaLinearAttnProcessor2_0()),
         )
 
-    # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.encode_prompt with 120->300
+    # Copied from diffusers.pipelines.sana.pipeline_sana.SanaPipeline.encode_prompt
     def encode_prompt(
         self,
         prompt: Union[str, List[str]],
@@ -194,7 +194,6 @@ def encode_prompt(
         negative_prompt_attention_mask: Optional[torch.Tensor] = None,
         clean_caption: bool = False,
         max_sequence_length: int = 300,
-        **kwargs,
     ):
         r"""
         Encodes the prompt into text encoder hidden states.
@@ -223,10 +222,6 @@ def encode_prompt(
             max_sequence_length (`int`, defaults to 300): Maximum sequence length to use for the prompt.
         """
 
-        if "mask_feature" in kwargs:
-            deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version."
-            deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False)
-
         if device is None:
             device = self._execution_device
 
@@ -251,16 +246,6 @@ def encode_prompt(
                 return_tensors="pt",
             )
             text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1])
-                logger.warning(
-                    "The following part of your input was truncated because T5 can only handle sequences up to"
-                    f" {max_length} tokens: {removed_text}"
-                )
 
             prompt_attention_mask = text_inputs.attention_mask
             prompt_attention_mask = prompt_attention_mask.to(device)
@@ -568,6 +553,8 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype
         else:
             latents = latents.to(device)
 
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
         return latents
 
     @torch.no_grad()

src/diffusers/pipelines/sana/pipeline_sana.py

@@ -319,7 +319,6 @@ def prepare_extra_step_kwargs(self, generator, eta):
             extra_step_kwargs["generator"] = generator
         return extra_step_kwargs
 
-    # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.check_inputs
     def check_inputs(
         self,
         prompt,
@@ -537,6 +536,8 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype
         else:
             latents = latents.to(device)
 
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
         return latents
 
     @torch.no_grad()