diff --git a/src/diffusers/loaders/single_file.py b/src/diffusers/loaders/single_file.py
index 881ff9b96a4c..960b5b24ed7e 100644
--- a/src/diffusers/loaders/single_file.py
+++ b/src/diffusers/loaders/single_file.py
@@ -270,17 +270,18 @@ class FromSingleFileMixin:
 
     @classmethod
     @validate_hf_hub_args
-    def from_single_file(cls, pretrained_model_link_or_path, **kwargs) -> Self:
+    def from_single_file(cls, pretrained_model_link_or_path_or_dict, **kwargs) -> Self:
         r"""
         Instantiate a [`DiffusionPipeline`] from pretrained pipeline weights saved in the `.ckpt` or `.safetensors`
         format. The pipeline is set in evaluation mode (`model.eval()`) by default.
 
         Parameters:
-            pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
+            pretrained_model_link_or_path_or_dict (`str`, *optional*):
                 Can be either:
-                    - A link to the `.ckpt` file (for example
-                      `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
-                    - A path to a *file* containing all pipeline weights.
+                    - A link to the `.safetensors` or `.ckpt` file (for example
+                      `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.safetensors"`) on the Hub.
+                    - A path to a local *file* containing the weights of the component model.
+                    - A state dict containing the component model weights.
             dtype (`str` or `torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
@@ -389,17 +390,19 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs) -> Self:
         from ..pipelines.pipeline_utils import _get_pipeline_class
 
         pipeline_class = _get_pipeline_class(cls, config=None)
-
-        checkpoint = load_single_file_checkpoint(
-            pretrained_model_link_or_path,
-            force_download=force_download,
-            proxies=proxies,
-            token=token,
-            cache_dir=cache_dir,
-            local_files_only=local_files_only,
-            revision=revision,
-            disable_mmap=disable_mmap,
-        )
+        if isinstance(pretrained_model_link_or_path_or_dict, dict):
+            checkpoint = pretrained_model_link_or_path_or_dict
+        else:
+            checkpoint = load_single_file_checkpoint(
+                pretrained_model_link_or_path_or_dict,
+                force_download=force_download,
+                proxies=proxies,
+                token=token,
+                cache_dir=cache_dir,
+                local_files_only=local_files_only,
+                revision=revision,
+                disable_mmap=disable_mmap,
+            )
 
         if config is None:
             config = fetch_diffusers_config(checkpoint)