update

src/diffusers/loaders/single_file.py

@@ -20,6 +20,7 @@ import requests
 import torch
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import validate_hf_hub_args
+from safetensors.torch import load_file as safe_load
 
 from ..utils import (
     deprecate,
@@ -55,7 +56,7 @@ MODEL_TYPE_FROM_PIPELINE_CLASS = {
 }
 
 
-def extract_pipeline_compoments(pipeline_class):
+def extract_pipeline_component_names(pipeline_class):
     components = inspect.signature(pipeline_class).parameters.keys()
     return components
 
@@ -97,10 +98,31 @@ def fetch_model_checkpoint(ckpt_path, cache_dir=None, resume_download=False, for
     return path
 
 
+def load_checkpoint(checkpoint_path_or_dict, device=None, from_safetensors=True):
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    if isinstance(checkpoint_path_or_dict, str):
+        if from_safetensors:
+            checkpoint = safe_load(checkpoint_path_or_dict, device="cpu")
+
+        else:
+            checkpoint = torch.load(checkpoint_path_or_dict, map_location=device)
+
+    elif isinstance(checkpoint_path_or_dict, dict):
+        checkpoint = checkpoint_path_or_dict
+
+    return checkpoint
+
+
 def infer_model_type(pipeline_class_name):
     return MODEL_TYPE_FROM_PIPELINE_CLASS.get(pipeline_class_name, None)
 
 
+def build_component(component_name, **kwargs):
+    return
+
+
 class FromSingleFileMixin:
     """
     Load model weights saved in the `.ckpt` format into a [`DiffusionPipeline`].
@@ -289,7 +311,13 @@ class FromSingleFileMixin:
                 f"The provided path is either not a file or a valid huggingface URL was not provided. Valid URLs begin with {', '.join(VALID_URL_PREFIXES)}"
             )
         pretrained_model_link_or_path = fetch_model_checkpoint(ckpt_path, cache_dir=cache_dir, resume_download=resume_download, proxies=proxies, local_files_only=local_files_only, token=token, revision=revision)
-        components = extract_pipeline_compoments(cls)
+        checkpoint = load_checkpoint(pretrained_model_link_or_path, from_safetensors=from_safetensors)
+
+        component_names = extract_pipeline_component_names(cls)
+
+        pipeline_components = {}
+        for component in component_names:
+            pipeline_components[component] = build_component(component, checkpoint, **kwargs)
 
         pipe = download_from_original_stable_diffusion_ckpt(
             pretrained_model_link_or_path,