fix make style

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -900,7 +900,12 @@ def convert_paint_by_example_checkpoint(checkpoint, local_files_only=False):
 
 
 def convert_open_clip_checkpoint(
-    checkpoint, config_name, prefix="cond_stage_model.model.", has_projection=False, local_files_only=False, **config_kwargs
+    checkpoint,
+    config_name,
+    prefix="cond_stage_model.model.",
+    has_projection=False,
+    local_files_only=False,
+    **config_kwargs,
 ):
     # text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
     # text_model = CLIPTextModelWithProjection.from_pretrained(
@@ -989,13 +994,17 @@ def stable_unclip_image_encoder(original_config, local_files_only=False):
 
         if clip_model_name == "ViT-L/14":
             feature_extractor = CLIPImageProcessor()
-            image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
+            image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+                "openai/clip-vit-large-patch14", local_files_only=local_files_only
+            )
         else:
             raise NotImplementedError(f"Unknown CLIP checkpoint name in stable diffusion checkpoint {clip_model_name}")
 
     elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder":
         feature_extractor = CLIPImageProcessor()
-        image_encoder = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K", local_files_only=local_files_only)
+        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", local_files_only=local_files_only
+        )
     else:
         raise NotImplementedError(
             f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}"
@@ -1178,8 +1187,7 @@ def download_from_original_stable_diffusion_ckpt(
             needed.
         config_files (`Dict[str, str]`, *optional*, defaults to `None`):
             A dictionary mapping from config file names to their contents. If this parameter is `None`, the function
-            will load the config files by itself, if needed.
-            Valid keys are:
+            will load the config files by itself, if needed. Valid keys are:
                 - `v1`: Config file for Stable Diffusion v1
                 - `v2`: Config file for Stable Diffusion v2
                 - `xl`: Config file for Stable Diffusion XL
@@ -1412,7 +1420,9 @@ def download_from_original_stable_diffusion_ckpt(
         config_kwargs = {"subfolder": "text_encoder"}
 
         text_model = convert_open_clip_checkpoint(checkpoint, config_name, **config_kwargs)
-        tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer", local_files_only=local_files_only)
+        tokenizer = CLIPTokenizer.from_pretrained(
+            "stabilityai/stable-diffusion-2", subfolder="tokenizer", local_files_only=local_files_only
+        )
 
         if stable_unclip is None:
             if controlnet:
@@ -1464,12 +1474,20 @@ def download_from_original_stable_diffusion_ckpt(
             elif stable_unclip == "txt2img":
                 if stable_unclip_prior is None or stable_unclip_prior == "karlo":
                     karlo_model = "kakaobrain/karlo-v1-alpha"
-                    prior = PriorTransformer.from_pretrained(karlo_model, subfolder="prior", local_files_only=local_files_only)
-
-                    prior_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
-                    prior_text_model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
-
-                    prior_scheduler = UnCLIPScheduler.from_pretrained(karlo_model, subfolder="prior_scheduler", local_files_only=local_files_only)
+                    prior = PriorTransformer.from_pretrained(
+                        karlo_model, subfolder="prior", local_files_only=local_files_only
+                    )
+
+                    prior_tokenizer = CLIPTokenizer.from_pretrained(
+                        "openai/clip-vit-large-patch14", local_files_only=local_files_only
+                    )
+                    prior_text_model = CLIPTextModelWithProjection.from_pretrained(
+                        "openai/clip-vit-large-patch14", local_files_only=local_files_only
+                    )
+
+                    prior_scheduler = UnCLIPScheduler.from_pretrained(
+                        karlo_model, subfolder="prior_scheduler", local_files_only=local_files_only
+                    )
                     prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
                 else:
                     raise NotImplementedError(f"unknown prior for stable unclip model: {stable_unclip_prior}")
@@ -1496,7 +1514,9 @@ def download_from_original_stable_diffusion_ckpt(
     elif model_type == "PaintByExample":
         vision_model = convert_paint_by_example_checkpoint(checkpoint)
         tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
-        feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only)
+        feature_extractor = AutoFeatureExtractor.from_pretrained(
+            "CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
+        )
         pipe = PaintByExamplePipeline(
             vae=vae,
             image_encoder=vision_model,
@@ -1509,11 +1529,19 @@ def download_from_original_stable_diffusion_ckpt(
         text_model = convert_ldm_clip_checkpoint(
             checkpoint, local_files_only=local_files_only, text_encoder=text_encoder
         )
-        tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only) if tokenizer is None else tokenizer
+        tokenizer = (
+            CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
+            if tokenizer is None
+            else tokenizer
+        )
 
         if load_safety_checker:
-            safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only)
-            feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only)
+            safety_checker = StableDiffusionSafetyChecker.from_pretrained(
+                "CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
+            )
+            feature_extractor = AutoFeatureExtractor.from_pretrained(
+                "CompVis/stable-diffusion-safety-checker", local_files_only=local_files_only
+            )
         else:
             safety_checker = None
             feature_extractor = None
@@ -1541,9 +1569,13 @@ def download_from_original_stable_diffusion_ckpt(
             )
     elif model_type in ["SDXL", "SDXL-Refiner"]:
         if model_type == "SDXL":
-            tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
+            tokenizer = CLIPTokenizer.from_pretrained(
+                "openai/clip-vit-large-patch14", local_files_only=local_files_only
+            )
             text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
-            tokenizer_2 = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only)
+            tokenizer_2 = CLIPTokenizer.from_pretrained(
+                "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
+            )
 
             config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
             config_kwargs = {"projection_dim": 1280}
@@ -1564,7 +1596,9 @@ def download_from_original_stable_diffusion_ckpt(
         else:
             tokenizer = None
             text_encoder = None
-            tokenizer_2 = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only)
+            tokenizer_2 = CLIPTokenizer.from_pretrained(
+                "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!", local_files_only=local_files_only
+            )
 
             config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
             config_kwargs = {"projection_dim": 1280}