fix: remove torch_dtype="auto" option from docstrings

src/diffusers/loaders/single_file_model.py

@@ -187,9 +187,8 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
             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 (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
-                dtype is automatically derived from the model's weights.
+            torch_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
                 cached versions if they exist.

src/diffusers/models/adapter.py

@@ -161,9 +161,8 @@ def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]
             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 (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
-                will be automatically derived from the model's weights.
+            torch_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.
             device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):

src/diffusers/models/auto_model.py

@@ -52,9 +52,8 @@ def from_pretrained(cls, pretrained_model_or_path: Optional[Union[str, os.PathLi
             cache_dir (`Union[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 (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
-                dtype is automatically derived from the model's weights.
+            torch_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
                 cached versions if they exist.

src/diffusers/models/controlnets/multicontrolnet.py

@@ -130,9 +130,8 @@ def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]
                 A path to a *directory* containing model weights saved using
                 [`~models.controlnets.multicontrolnet.MultiControlNetModel.save_pretrained`], e.g.,
                 `./my_model_directory/controlnet`.
-            torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
-                will be automatically derived from the model's weights.
+            torch_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.
             device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):

src/diffusers/models/controlnets/multicontrolnet_union.py

@@ -143,9 +143,8 @@ def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]
                 A path to a *directory* containing model weights saved using
                 [`~models.controlnets.multicontrolnet.MultiControlNetUnionModel.save_pretrained`], e.g.,
                 `./my_model_directory/controlnet`.
-            torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
-                will be automatically derived from the model's weights.
+            torch_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.
             device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):

src/diffusers/models/modeling_utils.py

@@ -787,9 +787,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             cache_dir (`Union[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 (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If `"auto"` is passed, the
-                dtype is automatically derived from the model's weights.
+            torch_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
                 cached versions if they exist.

src/diffusers/pipelines/auto_pipeline.py

@@ -322,9 +322,8 @@ 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*):
-                Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
-                dtype is automatically derived from the model's weights.
+            torch_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
                 cached versions if they exist.
@@ -619,8 +618,7 @@ def from_pretrained(cls, pretrained_model_or_path, **kwargs):
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
             torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
-                dtype is automatically derived from the model's weights.
+                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
                 cached versions if they exist.
@@ -930,8 +928,7 @@ def from_pretrained(cls, pretrained_model_or_path, **kwargs):
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
             torch_dtype (`str` or `torch.dtype`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
-                dtype is automatically derived from the model's weights.
+                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
                 cached versions if they exist.

src/diffusers/pipelines/pipeline_flax_utils.py

@@ -248,9 +248,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                       pretrained pipeline hosted on the Hub.
                     - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                       using [`~FlaxDiffusionPipeline.save_pretrained`].
-            dtype (`str` or `jnp.dtype`, *optional*):
-                Override the default `jnp.dtype` and load the model under this dtype. If `"auto"`, the dtype is
-                automatically derived from the model's weights.
+            dtype (`jnp.dtype`, *optional*):
+                Override the default `jnp.dtype` and load the model under this 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
                 cached versions if they exist.

src/diffusers/pipelines/pipeline_utils.py

@@ -572,12 +572,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                       saved using
                     [`~DiffusionPipeline.save_pretrained`].
                     - A path to a *directory* (for example `./my_pipeline_directory/`) containing a dduf file
-            torch_dtype (`str` or `torch.dtype` or `dict[str, Union[str, torch.dtype]]`, *optional*):
-                Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
-                dtype is automatically derived from the model's weights. 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': torch.bfloat16, 'default':
-                torch.float16}`). If a component is not specified and no default is set, `torch.float32` is used.
+            torch_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':
+                torch.bfloat16, 'default': torch.float16}`). If a component is not specified and no default is set,
+                `torch.float32` is used.
             custom_pipeline (`str`, *optional*):
 
                 <Tip warning={true}>