torch.dtype alias

src/diffusers/loaders/single_file.py

@@ -281,7 +281,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs) -> Self:
                     - A link to the `.ckpt` file (for example
                       `"https://huggingface.co/<repo_id>/blob/main/<path_to_file>.ckpt"`) on the Hub.
                     - A path to a *file* containing all pipeline weights.
-            torch_dtype (`str` or `torch.dtype`, *optional*):
+            dtype (`str` or `torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
@@ -361,6 +361,8 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs) -> Self:
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        dtype = kwargs.pop("dtype", None)
+        torch_dtype = dtype if dtype is not None else torch_dtype
         disable_mmap = kwargs.pop("disable_mmap", False)
 
         is_legacy_loading = False

src/diffusers/loaders/single_file_model.py

@@ -277,7 +277,7 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: str | None = No
             original_config (`str`, *optional*):
                 Dict or path to a yaml file containing the configuration for the model in its original format.
                     If a dict is provided, it will be used to initialize the model configuration.
-            torch_dtype (`torch.dtype`, *optional*):
+            dtype (`torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
@@ -351,6 +351,8 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: str | None = No
         revision = kwargs.pop("revision", None)
         config_revision = kwargs.pop("config_revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        dtype = kwargs.pop("dtype", None)
+        torch_dtype = dtype if dtype is not None else torch_dtype
         quantization_config = kwargs.pop("quantization_config", None)
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
         device = kwargs.pop("device", None)

src/diffusers/models/adapter.py

@@ -166,7 +166,7 @@ def from_pretrained(cls, pretrained_model_path: str | os.PathLike | None, **kwar
             pretrained_model_path (`os.PathLike`):
                 A path to a *directory* containing model weights saved using
                 [`~diffusers.models.adapter.MultiAdapter.save_pretrained`], e.g., `./my_model_directory/adapter`.
-            torch_dtype (`torch.dtype`, *optional*):
+            dtype (`torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model under this dtype.
             output_loading_info(`bool`, *optional*, defaults to `False`):
                 Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.

src/diffusers/models/auto_model.py

@@ -173,7 +173,7 @@ def from_pretrained(cls, pretrained_model_or_path: str | os.PathLike | None = No
             cache_dir (`str | os.PathLike`, *optional*):
                 Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                 is not used.
-            torch_dtype (`torch.dtype`, *optional*):
+            dtype (`torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the

src/diffusers/models/controlnets/multicontrolnet.py

@@ -163,7 +163,7 @@ def from_pretrained(cls, pretrained_model_path: str | os.PathLike | None, **kwar
                 A path to a *directory* containing model weights saved using
                 [`~models.controlnets.multicontrolnet.MultiControlNetModel.save_pretrained`], e.g.,
                 `./my_model_directory/controlnet`.
-            torch_dtype (`torch.dtype`, *optional*):
+            dtype (`torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model under this dtype.
             output_loading_info(`bool`, *optional*, defaults to `False`):
                 Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.

src/diffusers/models/controlnets/multicontrolnet_union.py

@@ -180,7 +180,7 @@ def from_pretrained(cls, pretrained_model_path: str | os.PathLike | None, **kwar
                 A path to a *directory* containing model weights saved using
                 [`~models.controlnets.multicontrolnet.MultiControlNetUnionModel.save_pretrained`], e.g.,
                 `./my_model_directory/controlnet`.
-            torch_dtype (`torch.dtype`, *optional*):
+            dtype (`torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model under this dtype.
             output_loading_info(`bool`, *optional*, defaults to `False`):
                 Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.

src/diffusers/models/modeling_utils.py

@@ -892,7 +892,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: str | os.PathLike | None
             cache_dir (`str | os.PathLike`, *optional*):
                 Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
                 is not used.
-            torch_dtype (`torch.dtype`, *optional*):
+            dtype (`torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
@@ -1018,6 +1018,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: str | os.PathLike | None
         token = kwargs.pop("token", None)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        dtype = kwargs.pop("dtype", None)
+        torch_dtype = dtype if dtype is not None else torch_dtype
         subfolder = kwargs.pop("subfolder", None)
         device_map = kwargs.pop("device_map", None)
         max_memory = kwargs.pop("max_memory", None)

src/diffusers/modular_pipelines/modular_pipeline_utils.py

@@ -311,10 +311,11 @@ def load(self, **kwargs) -> Any:
 
         from diffusers import AutoModel
 
-        # `torch_dtype` is not an accepted parameter for tokenizers and processors.
+        # `dtype`/`torch_dtype` is not an accepted parameter for tokenizers and processors.
         # As a result, it gets stored in `init_kwargs`, which are written to the config
         # during save. This causes JSON serialization to fail when saving the component.
         if self.type_hint is not None and not issubclass(self.type_hint, (torch.nn.Module, AutoModel)):
+            kwargs.pop("dtype", None)
             kwargs.pop("torch_dtype", None)
 
         if self.type_hint is None:

src/diffusers/pipelines/auto_pipeline.py

@@ -426,7 +426,7 @@ def from_pretrained(cls, pretrained_model_or_path, **kwargs):
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
-            torch_dtype (`torch.dtype`, *optional*):
+            dtype (`torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
@@ -717,7 +717,7 @@ def from_pretrained(cls, pretrained_model_or_path, **kwargs):
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
-            torch_dtype (`str` or `torch.dtype`, *optional*):
+            dtype (`str` or `torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
@@ -1022,7 +1022,7 @@ def from_pretrained(cls, pretrained_model_or_path, **kwargs):
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
-            torch_dtype (`str` or `torch.dtype`, *optional*):
+            dtype (`str` or `torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
@@ -1312,7 +1312,7 @@ def from_pretrained(cls, pretrained_model_or_path, **kwargs):
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
-            torch_dtype (`torch.dtype`, *optional*):
+            dtype (`torch.dtype`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype.
             force_download (`bool`, *optional*, defaults to `False`):
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the

src/diffusers/pipelines/pipeline_utils.py

@@ -639,7 +639,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: str | os.PathLike, **kwa
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing a dduf file
-            torch_dtype (`torch.dtype` or `dict[str, Union[str, torch.dtype]]`, *optional*):
+            dtype (`torch.dtype` or `dict[str, Union[str, torch.dtype]]`, *optional*):
                 Override the default `torch.dtype` and load the model with another dtype. To load submodels with
                 different dtype pass a `dict` (for example `{'transformer': torch.bfloat16, 'vae': torch.float16}`).
                 Set the default dtype for unspecified components with `default` (for example `{'transformer':
@@ -769,6 +769,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: str | os.PathLike, **kwa
         revision = kwargs.pop("revision", None)
         from_flax = kwargs.pop("from_flax", False)
         torch_dtype = kwargs.pop("torch_dtype", None)
+        dtype = kwargs.pop("dtype", None)
+        torch_dtype = dtype if dtype is not None else torch_dtype
         custom_pipeline = kwargs.pop("custom_pipeline", None)
         custom_revision = kwargs.pop("custom_revision", None)
         provider = kwargs.pop("provider", None)
@@ -2121,7 +2123,9 @@ def from_pipe(cls, pipeline, **kwargs):
         """
 
         original_config = dict(pipeline.config)
-        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
+        torch_dtype = kwargs.pop("torch_dtype", None)
+        dtype = kwargs.pop("dtype", None)
+        torch_dtype = dtype if dtype is not None else (torch_dtype if torch_dtype is not None else torch.float32)
         trust_remote_code = kwargs.pop("trust_remote_code", False)
 
         # derive the pipeline class to instantiate