convert ckpt script docstring fixes

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -17,6 +17,7 @@
 import os
 import re
 import tempfile
+from typing import Optional
 
 import requests
 import torch
@@ -787,8 +788,8 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     model_type: str = None,
     extract_ema: bool = False,
     scheduler_type: str = "pndm",
-    num_in_channels: int = None,
-    upcast_attention: bool = None,
+    num_in_channels: Optional[int] = None,
+    upcast_attention: Optional[bool] = None,
     device: str = None,
     from_safetensors: bool = False,
 ) -> StableDiffusionPipeline:
@@ -800,28 +801,36 @@ def load_pipeline_from_original_stable_diffusion_ckpt(
     global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
     recommended that you override the default values and/or supply an `original_config_file` wherever possible.
 
-    :param checkpoint_path: Path to `.ckpt` file. :param original_config_file: Path to `.yaml` config file
-    corresponding to the original architecture. If `None`, will be
-            automatically inferred by looking for a key that only exists in SD2.0 models.
-    :param image_size: The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable
-    Diffusion v2
+    Args:
+        checkpoint_path (`str`): Path to `.ckpt` file.
+        original_config_file (`str`):
+            Path to `.yaml` config file corresponding to the original architecture. If `None`, will be automatically
+            inferred by looking for a key that only exists in SD2.0 models.
+        image_size (`int`, *optional*, defaults to 512):
+            The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2
             Base. Use 768 for Stable Diffusion v2.
-    :param prediction_type: The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion
-    v1.X and Stable
+        prediction_type (`str`, *optional*):
+            The prediction type that the model was trained on. Use `'epsilon'` for Stable Diffusion v1.X and Stable
             Diffusion v2 Base. Use `'v_prediction'` for Stable Diffusion v2.
-    :param num_in_channels: The number of input channels. If `None` number of input channels will be automatically
-    inferred. :param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler",
-    "euler-ancestral", "dpm", "ddim"]`. :param model_type: The pipeline type. `None` to automatically infer, or one of
-    `["FrozenOpenCLIPEmbedder", "FrozenCLIPEmbedder", "PaintByExample"]`. :param extract_ema: Only relevant for
-    checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights
-            or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher
-            quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
-    :param upcast_attention: Whether the attention computation should always be upcasted. This is necessary when
-    running
-                    stable diffusion 2.1.
-    :param device: The device to use. Pass `None` to determine automatically. :param from_safetensors: If
-    `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. :return: A
-    StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
+        num_in_channels (`int`, *optional*, defaults to None):
+            The number of input channels. If `None`, it will be automatically inferred.
+        scheduler_type (`str`, *optional*, defaults to 'pndm'):
+            Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler", "euler-ancestral", "dpm",
+            "ddim"]`.
+        model_type (`str`, *optional*, defaults to `None`):
+            The pipeline type. `None` to automatically infer, or one of `["FrozenOpenCLIPEmbedder",
+            "FrozenCLIPEmbedder", "PaintByExample"]`.
+        extract_ema (`bool`, *optional*, defaults to `False`): Only relevant for
+            checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights or not. Defaults to
+            `False`. Pass `True` to extract the EMA weights. EMA weights usually yield higher quality images for
+            inference. Non-EMA weights are usually better to continue fine-tuning.
+        upcast_attention (`bool`, *optional*, defaults to `None`):
+            Whether the attention computation should always be upcasted. This is necessary when running stable
+            diffusion 2.1.
+        device (`str`, *optional*, defaults to `None`):
+            The device to use. Pass `None` to determine automatically. :param from_safetensors: If `checkpoint_path` is
+            in `safetensors` format, load checkpoint with safetensors instead of PyTorch. :return: A
+            StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
     """
     if prediction_type == "v-prediction":
         prediction_type = "v_prediction"