Update pipeline.py

pipeline.py

@@ -15,7 +15,6 @@ from diffusers.utils import deprecate, logging
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 
-
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 re_attention = re.compile(r"""
@@ -387,14 +386,14 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
     """
 
     def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+            self,
+            vae: AutoencoderKL,
+            text_encoder: CLIPTextModel,
+            tokenizer: CLIPTokenizer,
+            unet: UNet2DConditionModel,
+            scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+            safety_checker: StableDiffusionSafetyChecker,
+            feature_extractor: CLIPFeatureExtractor,
     ):
         super().__init__()
 
@@ -461,23 +460,23 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
 
     @torch.no_grad()
     def text2img(
-        self,
-        prompt: Union[str, List[str]],
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        max_embeddings_multiples: Optional[int] = 3,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: Optional[int] = 1,
-        **kwargs,
+            self,
+            prompt: Union[str, List[str]],
+            height: int = 512,
+            width: int = 512,
+            num_inference_steps: int = 50,
+            guidance_scale: float = 7.5,
+            negative_prompt: Optional[Union[str, List[str]]] = None,
+            num_images_per_prompt: Optional[int] = 1,
+            eta: float = 0.0,
+            generator: Optional[torch.Generator] = None,
+            latents: Optional[torch.FloatTensor] = None,
+            max_embeddings_multiples: Optional[int] = 3,
+            output_type: Optional[str] = "pil",
+            return_dict: bool = True,
+            callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+            callback_steps: Optional[int] = 1,
+            **kwargs,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -547,7 +546,7 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
             raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
 
         if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+                callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
         ):
             raise ValueError(
                 f"`callback_steps` has to be a positive integer but is {callback_steps} of type"