[LoRA] relax lora loading logic

src/diffusers/loaders.py

@@ -25,7 +25,7 @@
 import safetensors
 import torch
 import torch.nn.functional as F
-from huggingface_hub import hf_hub_download
+from huggingface_hub import hf_hub_download, model_info
 from torch import nn
 
 from .utils import (
@@ -1021,6 +1021,13 @@ def lora_state_dict(
                 weight_name is not None and weight_name.endswith(".safetensors")
             ):
                 try:
+                    # Here we're relaxing the loading check to enable more Inference API
+                    # friendliness where sometimes, it's not at all possible to automatically
+                    # determine `weight_name`.
+                    if weight_name is None:
+                        weight_name = cls._best_guess_weight_name(
+                            pretrained_model_name_or_path_or_dict, file_extension=".safetensors"
+                        )
                     model_file = _get_model_file(
                         pretrained_model_name_or_path_or_dict,
                         weights_name=weight_name or LORA_WEIGHT_NAME_SAFE,
@@ -1041,7 +1048,12 @@ def lora_state_dict(
                     # try loading non-safetensors weights
                     model_file = None
                     pass
+
             if model_file is None:
+                if weight_name is None:
+                    weight_name = cls._best_guess_weight_name(
+                        pretrained_model_name_or_path_or_dict, file_extension=".bin"
+                    )
                 model_file = _get_model_file(
                     pretrained_model_name_or_path_or_dict,
                     weights_name=weight_name or LORA_WEIGHT_NAME,
@@ -1077,6 +1089,31 @@ def lora_state_dict(
 
         return state_dict, network_alphas
 
+    @classmethod
+    def _best_guess_weight_name(cls, pretrained_model_name_or_path_or_dict, file_extension=".safetensors"):
+        targeted_files = []
+
+        if os.path.isfile(pretrained_model_name_or_path_or_dict):
+            return
+        elif os.path.isdir(pretrained_model_name_or_path_or_dict):
+            targeted_files = [
+                f for f in os.listdir(pretrained_model_name_or_path_or_dict) if f.endswith(file_extension)
+            ]
+        else:
+            files_in_repo = model_info(pretrained_model_name_or_path_or_dict).siblings
+            targeted_files = [f.rfilename for f in files_in_repo if f.rfilename.endswith(file_extension)]
+
+        if len(targeted_files) == 0:
+            return
+
+        targeted_files = list(filter(lambda x: "scheduler" not in x and "optimizer" not in x, targeted_files))
+        if len(targeted_files) > 1:
+            raise ValueError(
+                f"Provided path contains more than one weights file in the {file_extension} format. Either specify `weight_name` in `load_lora_weights` or make sure there's only one  `.safetensors` or `.bin` file in  {pretrained_model_name_or_path_or_dict}."
+            )
+        weight_name = targeted_files[0]
+        return weight_name
+
     @classmethod
     def _map_sgm_blocks_to_diffusers(cls, state_dict, unet_config, delimiter="_", block_slice_pos=5):
         is_all_unet = all(k.startswith("lora_unet") for k in state_dict)