Add a disable_mmap option to the from_single_file loader to improve load performance on network mounts

src/diffusers/loaders/single_file.py

@@ -60,6 +60,7 @@ def load_single_file_sub_model(
     local_files_only=False,
     torch_dtype=None,
     is_legacy_loading=False,
+    disable_mmap=False,
     **kwargs,
 ):
     if is_pipeline_module:
@@ -106,6 +107,7 @@ def load_single_file_sub_model(
             subfolder=name,
             torch_dtype=torch_dtype,
             local_files_only=local_files_only,
+            disable_mmap=disable_mmap,
             **kwargs,
         )
 
@@ -308,6 +310,9 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
                       hosted on the Hub.
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing the pipeline
                       component configs in Diffusers format.
+            disable_mmap ('bool', *optional*, defaults to 'False'):
+                Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
+                is on a network mount or hard drive.
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
                 class). The overwritten components are passed directly to the pipelines `__init__` method. See example
@@ -355,6 +360,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        disable_mmap = kwargs.pop("disable_mmap", False)
 
         is_legacy_loading = False
 
@@ -383,6 +389,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
             cache_dir=cache_dir,
             local_files_only=local_files_only,
             revision=revision,
+            disable_mmap=disable_mmap,
         )
 
         if config is None:
@@ -504,6 +511,7 @@ def load_module(name, value):
                         original_config=original_config,
                         local_files_only=local_files_only,
                         is_legacy_loading=is_legacy_loading,
+                        disable_mmap=disable_mmap,
                         **kwargs,
                     )
                 except SingleFileComponentError as e:

src/diffusers/loaders/single_file_model.py

@@ -182,6 +182,9 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
             revision (`str`, *optional*, defaults to `"main"`):
                 The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                 allowed by Git.
+            disable_mmap ('bool', *optional*, defaults to 'False'):
+                Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
+                is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load and saveable variables (for example the pipeline components of the
                 specific pipeline class). The overwritten components are directly passed to the pipelines `__init__`
@@ -229,6 +232,7 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         torch_dtype = kwargs.pop("torch_dtype", None)
         quantization_config = kwargs.pop("quantization_config", None)
         device = kwargs.pop("device", None)
+        disable_mmap = kwargs.pop("disable_mmap", False)
 
         if isinstance(pretrained_model_link_or_path_or_dict, dict):
             checkpoint = pretrained_model_link_or_path_or_dict
@@ -241,6 +245,7 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
                 cache_dir=cache_dir,
                 local_files_only=local_files_only,
                 revision=revision,
+                disable_mmap=disable_mmap,
             )
         if quantization_config is not None:
             hf_quantizer = DiffusersAutoQuantizer.from_config(quantization_config)
@@ -362,7 +367,9 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
             )
 
         else:
-            _, unexpected_keys = model.load_state_dict(diffusers_format_checkpoint, strict=False)
+            _, unexpected_keys = model.load_state_dict(
+                diffusers_format_checkpoint, strict=False
+            )
 
         if model._keys_to_ignore_on_load_unexpected is not None:
             for pat in model._keys_to_ignore_on_load_unexpected:

src/diffusers/loaders/single_file_utils.py

@@ -377,6 +377,7 @@ def load_single_file_checkpoint(
     cache_dir=None,
     local_files_only=None,
     revision=None,
+    disable_mmap=False,
 ):
     if os.path.isfile(pretrained_model_link_or_path):
         pretrained_model_link_or_path = pretrained_model_link_or_path
@@ -394,7 +395,7 @@ def load_single_file_checkpoint(
             revision=revision,
         )
 
-    checkpoint = load_state_dict(pretrained_model_link_or_path)
+    checkpoint = load_state_dict(pretrained_model_link_or_path, disable_mmap=disable_mmap)
 
     # some checkpoints contain the model state dict under a "state_dict" key
     while "state_dict" in checkpoint:

src/diffusers/models/model_loading_utils.py

@@ -131,7 +131,9 @@ def _fetch_remapped_cls_from_config(config, old_class):
         return old_class
 
 
-def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None):
+def load_state_dict(
+    checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None, disable_mmap: bool = False
+):
     """
     Reads a checkpoint file, returning properly formatted errors if they arise.
     """
@@ -142,7 +144,10 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[
     try:
         file_extension = os.path.basename(checkpoint_file).split(".")[-1]
         if file_extension == SAFETENSORS_FILE_EXTENSION:
-            return safetensors.torch.load_file(checkpoint_file, device="cpu")
+            if disable_mmap:
+                return safetensors.torch.load(open(checkpoint_file, "rb").read())
+            else:
+                return safetensors.torch.load_file(checkpoint_file, device="cpu")
         elif file_extension == GGUF_FILE_EXTENSION:
             return load_gguf_checkpoint(checkpoint_file)
         else:

src/diffusers/models/modeling_utils.py

@@ -559,6 +559,9 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 If set to `None`, the `safetensors` weights are downloaded if they're available **and** if the
                 `safetensors` library is installed. If set to `True`, the model is forcibly loaded from `safetensors`
                 weights. If set to `False`, `safetensors` weights are not loaded.
+            disable_mmap ('bool', *optional*, defaults to 'False'):
+                Whether to disable mmap when loading a Safetensors model. This option can perform better when the model
+                is on a network mount or hard drive, which may not handle the seeky-ness of mmap very well.
 
         <Tip>
 
@@ -604,6 +607,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         variant = kwargs.pop("variant", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
         quantization_config = kwargs.pop("quantization_config", None)
+        disable_mmap = kwargs.pop("disable_mmap", False)
 
         allow_pickle = False
         if use_safetensors is None:
@@ -883,7 +887,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     # TODO (sayakpaul,  SunMarc): remove this after model loading refactor
                     else:
                         param_device = torch.device(torch.cuda.current_device())
-                    state_dict = load_state_dict(model_file, variant=variant)
+                    state_dict = load_state_dict(model_file, variant=variant, disable_mmap=disable_mmap)
                     model._convert_deprecated_attention_blocks(state_dict)
 
                     # move the params from meta device to cpu
@@ -983,7 +987,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             else:
                 model = cls.from_config(config, **unused_kwargs)
 
-                state_dict = load_state_dict(model_file, variant=variant)
+                state_dict = load_state_dict(model_file, variant=variant, disable_mmap=disable_mmap)
                 model._convert_deprecated_attention_blocks(state_dict)
 
                 model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(