update

Author: DN6

src/diffusers/loaders/single_file_model.py

@@ -156,6 +156,10 @@
 }
 
 
+def _should_convert_state_dict_to_diffusers(model_state_dict, checkpoint_state_dict):
+    return not set(model_state_dict.keys()).issubset(set(checkpoint_state_dict.keys()))
+
+
 def _get_single_file_loadable_mapping_class(cls):
     diffusers_module = importlib.import_module(__name__.split(".")[0])
     for loadable_class_str in SINGLE_FILE_LOADABLE_CLASSES:
@@ -381,19 +385,23 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
             model_kwargs = {k: kwargs.get(k) for k in kwargs if k in expected_kwargs or k in optional_kwargs}
             diffusers_model_config.update(model_kwargs)
 
+        ctx = init_empty_weights if is_accelerate_available() else nullcontext
+        with ctx():
+            model = cls.from_config(diffusers_model_config)
+
         checkpoint_mapping_kwargs = _get_mapping_function_kwargs(checkpoint_mapping_fn, **kwargs)
-        diffusers_format_checkpoint = checkpoint_mapping_fn(
-            config=diffusers_model_config, checkpoint=checkpoint, **checkpoint_mapping_kwargs
-        )
+
+        if _should_convert_state_dict_to_diffusers(model.state_dict(), checkpoint):
+            diffusers_format_checkpoint = checkpoint_mapping_fn(
+                config=diffusers_model_config, checkpoint=checkpoint, **checkpoint_mapping_kwargs
+            )
+        else:
+            diffusers_format_checkpoint = checkpoint
+
         if not diffusers_format_checkpoint:
             raise SingleFileComponentError(
                 f"Failed to load {mapping_class_name}. Weights for this component appear to be missing in the checkpoint."
             )
-
-        ctx = init_empty_weights if is_accelerate_available() else nullcontext
-        with ctx():
-            model = cls.from_config(diffusers_model_config)
-
         # Check if `_keep_in_fp32_modules` is not None
         use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
             (torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")