style

Author: SunMarc

src/diffusers/loaders/single_file_model.py

@@ -362,12 +362,14 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
 
         if is_accelerate_available():
             param_device = torch.device(device) if device else torch.device("cpu")
-            unexpected_keys = [param_name for param_name in diffusers_format_checkpoint if param_name not in model.state_dict()]
+            unexpected_keys = [
+                param_name for param_name in diffusers_format_checkpoint if param_name not in model.state_dict()
+            ]
             load_model_dict_into_meta(
                 model,
                 diffusers_format_checkpoint,
                 dtype=torch_dtype,
-                device_map={"":param_device},
+                device_map={"": param_device},
                 hf_quantizer=hf_quantizer,
                 keep_in_fp32_modules=keep_in_fp32_modules,
                 unexpected_keys=unexpected_keys,

src/diffusers/pipelines/consisid/pipeline_consisid.py

@@ -48,9 +48,14 @@
         >>> from huggingface_hub import snapshot_download
 
         >>> snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir="BestWishYsh/ConsisID-preview")
-        >>> face_helper_1, face_helper_2, face_clip_model, face_main_model, eva_transform_mean, eva_transform_std = (
-        ...     prepare_face_models("BestWishYsh/ConsisID-preview", device="cuda", dtype=torch.bfloat16)
-        ... )
+        >>> (
+        ...     face_helper_1,
+        ...     face_helper_2,
+        ...     face_clip_model,
+        ...     face_main_model,
+        ...     eva_transform_mean,
+        ...     eva_transform_std,
+        ... ) = prepare_face_models("BestWishYsh/ConsisID-preview", device="cuda", dtype=torch.bfloat16)
         >>> pipe = ConsisIDPipeline.from_pretrained("BestWishYsh/ConsisID-preview", torch_dtype=torch.bfloat16)
         >>> pipe.to("cuda")