[docs] More API stuff (huggingface#3835)

* clean up loaders

* clean up rest of main class apis

* apply feedback

Author: stevhliu

configuration_utils.py

@@ -81,18 +81,17 @@ def __setitem__(self, name, value):
 
 class ConfigMixin:
     r"""
-    Base class for all configuration classes. Stores all configuration parameters under `self.config` Also handles all
-    methods for loading/downloading/saving classes inheriting from [`ConfigMixin`] with
-        - [`~ConfigMixin.from_config`]
-        - [`~ConfigMixin.save_config`]
+    Base class for all configuration classes. All configuration parameters are stored under `self.config`. Also
+    provides the [`~ConfigMixin.from_config`] and [`~ConfigMixin.save_config`] methods for loading, downloading, and
+    saving classes that inherit from [`ConfigMixin`].
 
     Class attributes:
         - **config_name** (`str`) -- A filename under which the config should stored when calling
           [`~ConfigMixin.save_config`] (should be overridden by parent class).
         - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
           overridden by subclass).
         - **has_compatibles** (`bool`) -- Whether the class has compatible classes (should be overridden by subclass).
-        - **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the init function
+        - **_deprecated_kwargs** (`List[str]`) -- Keyword arguments that are deprecated. Note that the `init` function
           should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by
           subclass).
     """
@@ -139,12 +138,12 @@ def __getattr__(self, name: str) -> Any:
 
     def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
         """
-        Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
+        Save a configuration object to the directory specified in `save_directory` so that it can be reloaded using the
         [`~ConfigMixin.from_config`] class method.
 
         Args:
             save_directory (`str` or `os.PathLike`):
-                Directory where the configuration JSON file will be saved (will be created if it does not exist).
+                Directory where the configuration JSON file is saved (will be created if it does not exist).
         """
         if os.path.isfile(save_directory):
             raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
@@ -164,15 +163,14 @@ def from_config(cls, config: Union[FrozenDict, Dict[str, Any]] = None, return_un
 
         Parameters:
             config (`Dict[str, Any]`):
-                A config dictionary from which the Python class will be instantiated. Make sure to only load
-                configuration files of compatible classes.
+                A config dictionary from which the Python class is instantiated. Make sure to only load configuration
+                files of compatible classes.
             return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                 Whether kwargs that are not consumed by the Python class should be returned or not.
-
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to update the configuration object (after it is loaded) and initiate the Python class.
-                `**kwargs` are directly passed to the underlying scheduler/model's `__init__` method and eventually
-                overwrite same named arguments in `config`.
+                `**kwargs` are passed directly to the underlying scheduler/model's `__init__` method and eventually
+                overwrite the same named arguments in `config`.
 
         Returns:
             [`ModelMixin`] or [`SchedulerMixin`]:
@@ -280,16 +278,16 @@ def load_config(
                 Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                 cached versions if they exist.
             resume_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to resume downloading the model weights and configuration files. If set to False, any
+                Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
                 incompletely downloaded files are deleted.
             proxies (`Dict[str, str]`, *optional*):
                 A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
                 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
             output_loading_info(`bool`, *optional*, defaults to `False`):
                 Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
-            local_files_only(`bool`, *optional*, defaults to `False`):
-                Whether to only load local model weights and configuration files or not. If set to True, the model
-                won’t be downloaded from the Hub.
+            local_files_only (`bool`, *optional*, defaults to `False`):
+                Whether to only load local model weights and configuration files or not. If set to `True`, the model
+                won't be downloaded from the Hub.
             use_auth_token (`str` or *bool*, *optional*):
                 The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                 `diffusers-cli login` (stored in `~/.huggingface`) is used.
@@ -307,14 +305,6 @@ def load_config(
             `dict`:
                 A dictionary of all the parameters stored in a JSON configuration file.
 
-        <Tip>
-
-        To use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models), log-in with
-        `huggingface-cli login`. You can also activate the special
-        ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to use this method in a
-        firewalled environment.
-
-        </Tip>
         """
         cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
         force_download = kwargs.pop("force_download", False)
@@ -536,10 +526,11 @@ def config(self) -> Dict[str, Any]:
 
     def to_json_string(self) -> str:
         """
-        Serializes this instance to a JSON string.
+        Serializes the configuration instance to a JSON string.
 
         Returns:
-            `str`: String containing all the attributes that make up this configuration instance in JSON format.
+            `str`:
+                String containing all the attributes that make up the configuration instance in JSON format.
         """
         config_dict = self._internal_dict if hasattr(self, "_internal_dict") else {}
         config_dict["_class_name"] = self.__class__.__name__
@@ -560,11 +551,11 @@ def to_json_saveable(value):
 
     def to_json_file(self, json_file_path: Union[str, os.PathLike]):
         """
-        Save this instance to a JSON file.
+        Save the configuration instance's parameters to a JSON file.
 
         Args:
             json_file_path (`str` or `os.PathLike`):
-                Path to the JSON file in which this configuration instance's parameters will be saved.
+                Path to the JSON file to save a configuration instance's parameters.
         """
         with open(json_file_path, "w", encoding="utf-8") as writer:
             writer.write(self.to_json_string())

image_processor.py

@@ -26,19 +26,18 @@
 
 class VaeImageProcessor(ConfigMixin):
     """
-    Image Processor for VAE
+    Image processor for VAE.
 
     Args:
         do_resize (`bool`, *optional*, defaults to `True`):
             Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
-            `height` and `width` arguments from `preprocess` method
+            `height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
         vae_scale_factor (`int`, *optional*, defaults to `8`):
-            VAE scale factor. If `do_resize` is True, the image will be automatically resized to multiples of this
-            factor.
+            VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
         resample (`str`, *optional*, defaults to `lanczos`):
             Resampling filter to use when resizing the image.
         do_normalize (`bool`, *optional*, defaults to `True`):
-            Whether to normalize the image to [-1,1]
+            Whether to normalize the image to [-1,1].
         do_convert_rgb (`bool`, *optional*, defaults to be `False`):
             Whether to convert the images to RGB format.
     """
@@ -75,7 +74,7 @@ def numpy_to_pil(images: np.ndarray) -> PIL.Image.Image:
     @staticmethod
     def pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.ndarray:
         """
-        Convert a PIL image or a list of PIL images to numpy arrays.
+        Convert a PIL image or a list of PIL images to NumPy arrays.
         """
         if not isinstance(images, list):
             images = [images]
@@ -87,7 +86,7 @@ def pil_to_numpy(images: Union[List[PIL.Image.Image], PIL.Image.Image]) -> np.nd
     @staticmethod
     def numpy_to_pt(images: np.ndarray) -> torch.FloatTensor:
         """
-        Convert a numpy image to a pytorch tensor
+        Convert a NumPy image to a PyTorch tensor.
         """
         if images.ndim == 3:
             images = images[..., None]
@@ -98,22 +97,22 @@ def numpy_to_pt(images: np.ndarray) -> torch.FloatTensor:
     @staticmethod
     def pt_to_numpy(images: torch.FloatTensor) -> np.ndarray:
         """
-        Convert a pytorch tensor to a numpy image
+        Convert a PyTorch tensor to a NumPy image.
         """
         images = images.cpu().permute(0, 2, 3, 1).float().numpy()
         return images
 
     @staticmethod
     def normalize(images):
         """
-        Normalize an image array to [-1,1]
+        Normalize an image array to [-1,1].
         """
         return 2.0 * images - 1.0
 
     @staticmethod
     def denormalize(images):
         """
-        Denormalize an image array to [0,1]
+        Denormalize an image array to [0,1].
         """
         return (images / 2 + 0.5).clamp(0, 1)
 
@@ -132,7 +131,7 @@ def resize(
         width: Optional[int] = None,
     ) -> PIL.Image.Image:
         """
-        Resize a PIL image. Both height and width will be downscaled to the next integer multiple of `vae_scale_factor`
+        Resize a PIL image. Both height and width are downscaled to the next integer multiple of `vae_scale_factor`.
         """
         if height is None:
             height = image.height
@@ -152,7 +151,7 @@ def preprocess(
         width: Optional[int] = None,
     ) -> torch.Tensor:
         """
-        Preprocess the image input, accepted formats are PIL images, numpy arrays or pytorch tensors"
+        Preprocess the image input. Accepted formats are PIL images, NumPy arrays or PyTorch tensors.
         """
         supported_formats = (PIL.Image.Image, np.ndarray, torch.Tensor)
         if isinstance(image, supported_formats):
@@ -255,18 +254,17 @@ def postprocess(
 
 class VaeImageProcessorLDM3D(VaeImageProcessor):
     """
-    Image Processor for VAE LDM3D.
+    Image processor for VAE LDM3D.
 
     Args:
         do_resize (`bool`, *optional*, defaults to `True`):
             Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`.
         vae_scale_factor (`int`, *optional*, defaults to `8`):
-            VAE scale factor. If `do_resize` is True, the image will be automatically resized to multiples of this
-            factor.
+            VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
         resample (`str`, *optional*, defaults to `lanczos`):
             Resampling filter to use when resizing the image.
         do_normalize (`bool`, *optional*, defaults to `True`):
-            Whether to normalize the image to [-1,1]
+            Whether to normalize the image to [-1,1].
     """
 
     config_name = CONFIG_NAME
@@ -284,7 +282,7 @@ def __init__(
     @staticmethod
     def numpy_to_pil(images):
         """
-        Convert a numpy image or a batch of images to a PIL image.
+        Convert a NumPy image or a batch of images to a PIL image.
         """
         if images.ndim == 3:
             images = images[None, ...]
@@ -310,7 +308,7 @@ def rgblike_to_depthmap(image):
 
     def numpy_to_depth(self, images):
         """
-        Convert a numpy depth image or a batch of images to a PIL image.
+        Convert a NumPy depth image or a batch of images to a PIL image.
         """
         if images.ndim == 3:
             images = images[None, ...]