[Outputs] Improve syntax (#423)

* [Outputs] Improve syntax

* improve more

* fix docstring return

* correct all

* uP

Co-authored-by: Mishig Davaadorj <dmishig@gmail.com>

docs/source/api/diffusion_pipeline.mdx

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Diffusion Pipeline
+# Pipelines
 
 The [`DiffusionPipeline`] is the easiest way to load any pretrained diffusion pipeline from the [Hub](https://huggingface.co/models?library=diffusers) and to use it in inference.
 
@@ -28,6 +28,12 @@ pipeline and pass them into the `__init__` function of the pipeline, *e.g.* [`~S
 
 Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrained`].
 
+## DiffusionPipeline
 [[autodoc]] DiffusionPipeline
 	- from_pretrained
 	- save_pretrained
+
+## ImagePipelineOutput
+By default diffusion pipelines return an object of class
+
+[[autodoc]] pipeline_utils.ImagePipelineOutput

docs/source/api/pipelines/ddim.mdx

@@ -17,7 +17,6 @@ The original codebase of this paper can be found [here](https://github.com/ermon
 | [pipeline_ddim.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddim/pipeline_ddim.py) | *Unconditional Image Generation* | - |
 
 
-## API
-
-[[autodoc]] pipelines.ddim.pipeline_ddim.DDIMPipeline
+## DDIMPipeline
+[[autodoc]] DDIMPipeline
     - __call__

docs/source/api/pipelines/ddpm.mdx

@@ -19,7 +19,6 @@ The original codebase of this paper can be found [here](https://github.com/hojon
 | [pipeline_ddpm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/ddpm/pipeline_ddpm.py) | *Unconditional Image Generation* | - |
 
 
-## API
-
-[[autodoc]] pipelines.ddpm.pipeline_ddpm.DDPMPipeline
+# DDPMPipeline
+[[autodoc]] DDPMPipeline
     - __call__

docs/source/api/pipelines/latent_diffusion.mdx

@@ -25,7 +25,6 @@ The original codebase can be found [here](https://github.com/CompVis/latent-diff
 ## Examples:
 
 
-## API
-
+## LDMTextToImagePipeline
 [[autodoc]] pipelines.latent_diffusion.pipeline_latent_diffusion.LDMTextToImagePipeline
     - __call__

docs/source/api/pipelines/latent_diffusion_uncond.mdx

@@ -24,7 +24,6 @@ The original codebase can be found [here](https://github.com/CompVis/latent-diff
 
 ## Examples:
 
-## API
-
-[[autodoc]] pipelines.latent_diffusion_uncond.pipeline_latent_diffusion_uncond.LDMPipeline
+## LDMPipeline
+[[autodoc]] LDMPipeline
     - __call__

docs/source/api/pipelines/pndm.mdx

@@ -17,8 +17,7 @@ The original codebase can be found [here](https://github.com/luping-liu/PNDM).
 | [pipeline_pndm.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pndm/pipeline_pndm.py) | *Unconditional Image Generation* | - |
 
 
-## API
-
+## PNDMPipeline
 [[autodoc]] pipelines.pndm.pipeline_pndm.PNDMPipeline
     - __call__
 

docs/source/api/pipelines/score_sde_ve.mdx

@@ -18,8 +18,7 @@ This pipeline implements the Variance Expanding (VE) variant of the method.
 |---|---|:---:|
 | [pipeline_score_sde_ve.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py) | *Unconditional Image Generation* | - |
 
-## API
-
-[[autodoc]] pipelines.score_sde_ve.pipeline_score_sde_ve.ScoreSdeVePipeline
+## ScoreSdeVePipeline
+[[autodoc]] ScoreSdeVePipeline
     - __call__
 

docs/source/api/pipelines/stable_diffusion.mdx

@@ -8,29 +8,31 @@ For more details about how Stable Diffusion works and how it differs from the ba
 
 *Tips*:
 - To tweak your prompts on a specific result you liked, you can generate your own latents, as demonstrated in the following notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb)
-- TODO: some interesting Tips
 
+*Overview*:
 | Pipeline | Tasks | Colab | Demo
  |---|---|:---:|:---:|
  | [pipeline_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb) | [🤗 Stable Diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion)
  | [pipeline_stable_diffusion_img2img.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) | *Image-to-Image Text-Guided Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/image_2_image_using_diffusers.ipynb) | [🤗 Diffuse the Rest](https://huggingface.co/spaces/huggingface/diffuse-the-rest)
  | [pipeline_stable_diffusion_inpaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | **Experimental** – *Text-Guided Image Inpainting* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/in_painting_with_stable_diffusion_using_diffusers.ipynb) | Coming soon
 
+## StableDiffusionPipelineOutput
+[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
+
+## StableDiffusionPipeline
 [[autodoc]] StableDiffusionPipeline
-	 - __init__
-     - __call__
-     - enable_attention_slicing
-     - disable_attention_slicing
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
 
+## StableDiffusionImg2ImgPipeline
 [[autodoc]] StableDiffusionImg2ImgPipeline
-	 - __init__
-     - __call__
-     - enable_attention_slicing
-     - disable_attention_slicing
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
 
+## StableDiffusionInpaintPipeline
 [[autodoc]] StableDiffusionInpaintPipeline
-	 - __init__
-     - __call__
-     - enable_attention_slicing
-     - disable_attention_slicing
-
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing

docs/source/api/pipelines/stochastic_karras_ve.mdx

@@ -18,7 +18,6 @@ This pipeline implements the Stochastic sampling tailored to the Variance-Expand
 | [pipeline_stochastic_karras_ve.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py) | *Unconditional Image Generation* | - |
 
 
-## API
-
-[[autodoc]] pipelines.stochastic_karras_ve.pipeline_stochastic_karras_ve.KarrasVePipeline
+## KarrasVePipeline
+[[autodoc]] KarrasVePipeline
     - __call__

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -52,21 +52,26 @@ class DDIMPipeline(DiffusionPipeline):
     ) -> Union[ImagePipelineOutput, Tuple]:
         r"""
         Args:
-            batch_size (:obj:`int`, *optional*, defaults to 1):
+            batch_size (`int`, *optional*, defaults to 1):
                 The number of images to generate.
-            generator (:obj:`torch.Generator`, *optional*):
+            generator (`torch.Generator`, *optional*):
                 A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
-            eta (:obj:`float`, *optional*, defaults to 0.0):
+            eta (`float`, *optional*, defaults to 0.0):
                 The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
-            num_inference_steps (:obj:`int`, *optional*, defaults to 50):
+            num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference.
-            output_type (:obj:`str`, *optional*, defaults to :obj:`"pil"`):
+            output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (:obj:`bool`, *optional*, defaults to :obj:`True`):
+            return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
         """
 
         if "torch_device" in kwargs:

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -50,16 +50,21 @@ class DDPMPipeline(DiffusionPipeline):
     ) -> Union[ImagePipelineOutput, Tuple]:
         r"""
         Args:
-            batch_size (:obj:`int`, *optional*, defaults to 1):
+            batch_size (`int`, *optional*, defaults to 1):
                 The number of images to generate.
-            generator (:obj:`torch.Generator`, *optional*):
+            generator (`torch.Generator`, *optional*):
                 A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
-            output_type (:obj:`str`, *optional*, defaults to :obj:`"pil"`):
+            output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (:obj:`bool`, *optional*, defaults to :obj:`True`):
+            return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
         """
         if "torch_device" in kwargs:
             device = kwargs.pop("torch_device")

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -88,6 +88,11 @@ class LDMTextToImagePipeline(DiffusionPipeline):
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
             return_dict (`bool`, *optional*):
                 Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
         """
         if "torch_device" in kwargs:
             device = kwargs.pop("torch_device")

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -54,6 +54,11 @@ class LDMPipeline(DiffusionPipeline):
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
         """
 
         if "torch_device" in kwargs:

src/diffusers/pipelines/pndm/pipeline_pndm.py

@@ -30,7 +30,7 @@ class PNDMPipeline(DiffusionPipeline):
     library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
 
     Parameters:
-        unet (:obj:`UNet2DModel`): U-Net architecture to denoise the encoded image latents.
+        unet (`UNet2DModel`): U-Net architecture to denoise the encoded image latents.
         scheduler ([`SchedulerMixin`]):
             The `PNDMScheduler` to be used in combination with `unet` to denoise the encoded image.
     """
@@ -55,20 +55,22 @@ class PNDMPipeline(DiffusionPipeline):
     ) -> Union[ImagePipelineOutput, Tuple]:
         r"""
         Args:
-            batch_size (:obj:`int`, `optional`, defaults to 1): The number of images to generate.
-            num_inference_steps (:
-                obj:`int`, `optional`, defaults to 50): The number of denoising steps. More denoising steps usually
-                lead to a higher quality image at the expense of slower inference.
-            generator (:
-                obj:`torch.Generator`, `optional`): A [torch
+            batch_size (`int`, `optional`, defaults to 1): The number of images to generate.
+            num_inference_steps (`int`, `optional`, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            generator (`torch.Generator`, `optional`): A [torch
                 generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
-            output_type (:
-                obj:`str`, `optional`, defaults to :obj:`"pil"`): The output format of the generate image. Choose
+            output_type (`str`, `optional`, defaults to `"pil"`): The output format of the generate image. Choose
                 between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (:
-                obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to return a
+            return_dict (`bool`, `optional`, defaults to `True`): Whether or not to return a
                 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
         """
         # For more information on the sampling method you can take a look at Algorithm 2 of
         # the official paper: https://arxiv.org/pdf/2202.09778.pdf

src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py

@@ -36,16 +36,21 @@ class ScoreSdeVePipeline(DiffusionPipeline):
     ) -> Union[ImagePipelineOutput, Tuple]:
         r"""
         Args:
-            batch_size (:obj:`int`, *optional*, defaults to 1):
+            batch_size (`int`, *optional*, defaults to 1):
                 The number of images to generate.
-            generator (:obj:`torch.Generator`, *optional*):
+            generator (`torch.Generator`, *optional*):
                 A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
-            output_type (:obj:`str`, *optional*, defaults to :obj:`"pil"`):
+            output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (:obj:`bool`, *optional*, defaults to :obj:`True`):
+            return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
         """
 
         if "torch_device" in kwargs:

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -142,7 +142,8 @@ class StableDiffusionPipeline(DiffusionPipeline):
                 plain tuple.
 
         Returns:
-            `~pipelines.stable_diffusion.StableDiffusionPipelineOutput` if `return_dict` is True, otherwise a tuple.
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
             When returning a tuple, the first element is a list with the generated images, and the second element is a
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -152,7 +152,8 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
                 plain tuple.
 
         Returns:
-            `~pipelines.stable_diffusion.StableDiffusionPipelineOutput` if `return_dict` is True, otherwise a tuple.
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
             When returning a tuple, the first element is a list with the generated images, and the second element is a
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -170,7 +170,8 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
                 plain tuple.
 
         Returns:
-            `~pipelines.stable_diffusion.StableDiffusionPipelineOutput` if `return_dict` is True, otherwise a tuple.
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
             When returning a tuple, the first element is a list with the generated images, and the second element is a
             list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
             (nsfw) content, according to the `safety_checker`.

src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py

@@ -45,19 +45,24 @@ class KarrasVePipeline(DiffusionPipeline):
     ) -> Union[Tuple, ImagePipelineOutput]:
         r"""
         Args:
-            batch_size (:obj:`int`, *optional*, defaults to 1):
+            batch_size (`int`, *optional*, defaults to 1):
                 The number of images to generate.
-            generator (:obj:`torch.Generator`, *optional*):
+            generator (`torch.Generator`, *optional*):
                 A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
-            num_inference_steps (:obj:`int`, *optional*, defaults to 50):
+            num_inference_steps (`int`, *optional*, defaults to 50):
                 The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                 expense of slower inference.
-            output_type (:obj:`str`, *optional*, defaults to :obj:`"pil"`):
+            output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
-            return_dict (:obj:`bool`, *optional*, defaults to :obj:`True`):
+            return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
         """
         if "torch_device" in kwargs:
             device = kwargs.pop("torch_device")

src/diffusers/schedulers/scheduling_ddim.py

@@ -59,6 +59,11 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
     Denoising diffusion implicit models is a scheduler that extends the denoising procedure introduced in denoising
     diffusion probabilistic models (DDPMs) with non-Markovian guidance.
 
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functios.
+
     For more details, see the original paper: https://arxiv.org/abs/2010.02502
 
     Args:
@@ -171,7 +176,9 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
         Returns:
-            `SchedulerOutput`: updated sample in the diffusion chain.
+            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
 
         """
         if self.num_inference_steps is None:

src/diffusers/schedulers/scheduling_ddpm.py

@@ -58,6 +58,11 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
     Denoising diffusion probabilistic models (DDPMs) explores the connections between denoising score matching and
     Langevin dynamics sampling.
 
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functios.
+
     For more details, see the original paper: https://arxiv.org/abs/2006.11239
 
     Args:
@@ -189,7 +194,9 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
         Returns:
-            `SchedulerOutput`: updated sample in the diffusion chain.
+            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
 
         """
         t = timestep

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -50,6 +50,11 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
     https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic
     differential equations." https://arxiv.org/abs/2011.13456
 
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functios.
+
     For more details on the parameters, see the original paper's Appendix E.: "Elucidating the Design Space of
     Diffusion-Based Generative Models." https://arxiv.org/abs/2206.00364. The grid search values used to find the
     optimal {s_noise, s_churn, s_min, s_max} for a specific model are described in Table 5 of the paper.
@@ -147,8 +152,11 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
             sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
-        Returns:
             KarrasVeOutput: updated sample in the diffusion chain and derivative (TODO double check).
+        Returns:
+            [`~schedulers.scheduling_karras_ve.KarrasVeOutput`] or `tuple`:
+            [`~schedulers.scheduling_karras_ve.KarrasVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
 
         """
 

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -29,6 +29,11 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
     Katherine Crowson:
     https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181
 
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functios.
+
     Args:
         num_train_timesteps (`int`): number of diffusion steps used to train the model.
         beta_start (`float`): the starting `beta` value of inference.
@@ -143,7 +148,9 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
         Returns:
-            prev_sample (`SchedulerOutput` or `Tuple`): updated sample in the diffusion chain.
+            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
 
         """
         sigma = self.sigmas[timestep]

src/diffusers/schedulers/scheduling_pndm.py

@@ -58,6 +58,11 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
     Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques,
     namely Runge-Kutta method and a linear multi-step method.
 
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functios.
+
     For more details, see the original paper: https://arxiv.org/abs/2202.09778
 
     Args:
@@ -186,7 +191,9 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
         Returns:
-            `SchedulerOutput`: updated sample in the diffusion chain.
+            [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.SchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
 
         """
         if self.counter < len(self.prk_timesteps) and not self.config.skip_prk_steps:
@@ -213,7 +220,8 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
         Returns:
-            prev_sample (`SchedulerOutput` or `Tuple`): updated sample in the diffusion chain.
+            [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
+            True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
 
         """
         if self.num_inference_steps is None:
@@ -267,7 +275,8 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
         Returns:
-            prev_sample (`SchedulerOutput` or `Tuple`): updated sample in the diffusion chain.
+            [`~scheduling_utils.SchedulerOutput`] or `tuple`: [`~scheduling_utils.SchedulerOutput`] if `return_dict` is
+            True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
 
         """
         if self.num_inference_steps is None:

src/diffusers/schedulers/scheduling_sde_ve.py

@@ -49,6 +49,11 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
 
     For more information, see the original paper: https://arxiv.org/abs/2011.13456
 
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functios.
+
     Args:
         snr (`float`):
             coefficient weighting the step from the model_output sample (from the network) to the random noise.
@@ -182,7 +187,8 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
         Returns:
-            prev_sample (`SchedulerOutput` or `Tuple`): updated sample in the diffusion chain.
+            [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if
+            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
 
         """
         if "seed" in kwargs and kwargs["seed"] is not None:
@@ -241,7 +247,8 @@ class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
             return_dict (`bool`): option for returning tuple rather than SchedulerOutput class
 
         Returns:
-            prev_sample (`SchedulerOutput` or `Tuple`): updated sample in the diffusion chain.
+            [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`: [`~schedulers.scheduling_sde_ve.SdeVeOutput`] if
+            `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
 
         """
         if "seed" in kwargs and kwargs["seed"] is not None:

src/diffusers/schedulers/scheduling_sde_vp.py

@@ -27,6 +27,11 @@ class ScoreSdeVpScheduler(SchedulerMixin, ConfigMixin):
     """
     The variance preserving stochastic differential equation (SDE) scheduler.
 
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functios.
+
     For more information, see the original paper: https://arxiv.org/abs/2011.13456
 
     UNDER CONSTRUCTION