Merge branch 'main' into fix-run-pr-workflow

Author: sayakpaul

src/diffusers/loaders/single_file.py

@@ -19,6 +19,7 @@
 from huggingface_hub import snapshot_download
 from huggingface_hub.utils import LocalEntryNotFoundError, validate_hf_hub_args
 from packaging import version
+from typing_extensions import Self
 
 from ..utils import deprecate, is_transformers_available, logging
 from .single_file_utils import (
@@ -269,7 +270,7 @@ class FromSingleFileMixin:
 
     @classmethod
     @validate_hf_hub_args
-    def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
+    def from_single_file(cls, pretrained_model_link_or_path, **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.

src/diffusers/loaders/single_file_model.py

@@ -19,6 +19,7 @@
 
 import torch
 from huggingface_hub.utils import validate_hf_hub_args
+from typing_extensions import Self
 
 from ..quantizers import DiffusersAutoQuantizer
 from ..utils import deprecate, is_accelerate_available, logging
@@ -148,7 +149,7 @@ class FromOriginalModelMixin:
 
     @classmethod
     @validate_hf_hub_args
-    def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] = None, **kwargs):
+    def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] = None, **kwargs) -> Self:
         r"""
         Instantiate a model from pretrained weights saved in the original `.ckpt` or `.safetensors` format. The model
         is set in evaluation mode (`model.eval()`) by default.

src/diffusers/pipelines/pipeline_utils.py

@@ -324,7 +324,7 @@ def is_saveable_module(name, value):
                 create_pr=create_pr,
             )
 
-    def to(self, *args, **kwargs):
+    def to(self, *args, **kwargs) -> Self:
         r"""
         Performs Pipeline dtype and/or device conversion. A torch.dtype and torch.device are inferred from the
         arguments of `self.to(*args, **kwargs).`