named_buffers

src/diffusers/loaders/single_file_model.py

@@ -362,13 +362,15 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
 
         if is_accelerate_available():
             param_device = torch.device(device) if device else torch.device("cpu")
+            named_buffers = model.named_buffers()
             unexpected_keys = load_model_dict_into_meta(
                 model,
                 diffusers_format_checkpoint,
                 dtype=torch_dtype,
                 device=param_device,
                 hf_quantizer=hf_quantizer,
                 keep_in_fp32_modules=keep_in_fp32_modules,
+                named_buffers=named_buffers,
             )
 
         else:

src/diffusers/models/model_loading_utils.py

@@ -20,7 +20,7 @@
 from array import array
 from collections import OrderedDict
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import Iterator, List, Optional, Tuple, Union
 
 import safetensors
 import torch
@@ -185,6 +185,7 @@ def load_model_dict_into_meta(
     model_name_or_path: Optional[str] = None,
     hf_quantizer=None,
     keep_in_fp32_modules=None,
+    named_buffers: Optional[Iterator[Tuple[str, torch.Tensor]]] = None,
 ) -> List[str]:
     if device is not None and not isinstance(device, (str, torch.device)):
         raise ValueError(f"Expected device to have type `str` or `torch.device`, but got {type(device)=}.")
@@ -246,7 +247,10 @@ def load_model_dict_into_meta(
             else:
                 set_module_tensor_to_device(model, param_name, device, value=param)
 
-    for param_name, param in model.named_buffers():
+    if named_buffers is None:
+        return unexpected_keys
+
+    for param_name, param in named_buffers:
         if is_quantized and (
             hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
         ):

src/diffusers/models/modeling_utils.py

@@ -902,6 +902,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                             " those weights or else make sure your checkpoint file is correct."
                         )
 
+                    named_buffers = model.named_buffers()
+
                     unexpected_keys = load_model_dict_into_meta(
                         model,
                         state_dict,
@@ -910,6 +912,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                         model_name_or_path=pretrained_model_name_or_path,
                         hf_quantizer=hf_quantizer,
                         keep_in_fp32_modules=keep_in_fp32_modules,
+                        named_buffers=named_buffers,
                     )
 
                     if cls._keys_to_ignore_on_load_unexpected is not None: