Merge branch 'main' into chroma_docs

Author: SahilCarterr

examples/server/README.md

@@ -9,8 +9,8 @@ This guide will show you how to use the [`StableDiffusion3Pipeline`] in a server
 Start by navigating to the `examples/server` folder and installing all of the dependencies.
 
 ```py
-pip install .
-pip install -f requirements.txt
+pip install diffusers
+pip install -r requirements.txt
 ```
 
 Launch the server with the following command.

examples/server/requirements.in

@@ -6,4 +6,5 @@ py-consul
 prometheus_client >= 0.18.0
 prometheus-fastapi-instrumentator >= 7.0.0
 fastapi
-uvicorn
+uvicorn
+accelerate

examples/server/requirements.txt

@@ -39,7 +39,7 @@ fsspec==2024.10.0
     #   torch
 h11==0.14.0
     # via uvicorn
-huggingface-hub==0.26.1
+huggingface-hub==0.35.0
     # via
     #   tokenizers
     #   transformers

src/diffusers/__init__.py

@@ -515,6 +515,7 @@
             "QwenImageControlNetPipeline",
             "QwenImageEditInpaintPipeline",
             "QwenImageEditPipeline",
+            "QwenImageEditPlusPipeline",
             "QwenImageImg2ImgPipeline",
             "QwenImageInpaintPipeline",
             "QwenImagePipeline",
@@ -1170,6 +1171,7 @@
             QwenImageControlNetPipeline,
             QwenImageEditInpaintPipeline,
             QwenImageEditPipeline,
+            QwenImageEditPlusPipeline,
             QwenImageImg2ImgPipeline,
             QwenImageInpaintPipeline,
             QwenImagePipeline,

src/diffusers/loaders/lora_base.py

@@ -1064,6 +1064,41 @@ def save_function(weights, filename):
         save_function(state_dict, save_path)
         logger.info(f"Model weights saved in {save_path}")
 
+    @classmethod
+    def _save_lora_weights(
+        cls,
+        save_directory: Union[str, os.PathLike],
+        lora_layers: Dict[str, Dict[str, Union[torch.nn.Module, torch.Tensor]]],
+        lora_metadata: Dict[str, Optional[dict]],
+        is_main_process: bool = True,
+        weight_name: str = None,
+        save_function: Callable = None,
+        safe_serialization: bool = True,
+    ):
+        """
+        Helper method to pack and save LoRA weights and metadata. This method centralizes the saving logic for all
+        pipeline types.
+        """
+        state_dict = {}
+        final_lora_adapter_metadata = {}
+
+        for prefix, layers in lora_layers.items():
+            state_dict.update(cls.pack_weights(layers, prefix))
+
+        for prefix, metadata in lora_metadata.items():
+            if metadata:
+                final_lora_adapter_metadata.update(_pack_dict_with_prefix(metadata, prefix))
+
+        cls.write_lora_layers(
+            state_dict=state_dict,
+            save_directory=save_directory,
+            is_main_process=is_main_process,
+            weight_name=weight_name,
+            save_function=save_function,
+            safe_serialization=safe_serialization,
+            lora_adapter_metadata=final_lora_adapter_metadata if final_lora_adapter_metadata else None,
+        )
+
     @classmethod
     def _optionally_disable_offloading(cls, _pipeline):
         return _func_optionally_disable_offloading(_pipeline=_pipeline)