[Pipeline Docs] ddpm docs for sprint (#382)

* initial ddpm

for issue #293

* initial ddpm pipeline doc

* added docstrings

* Update docs/source/api/pipelines/ddpm.mdx

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* make style

* fix docs

* make style

* fix doc strings

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

docs/source/api/pipelines/ddpm.mdx

@@ -1 +1,25 @@
-# GLIDE MODEL
+# DDPM
+
+## Overview
+
+[Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) 
+ (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes the diffusion based model of the same name, but in the context of the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline.
+
+The abstract of the paper is the following:
+
+We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.
+
+The original codebase of this paper can be found [here](https://github.com/hojonathanho/diffusion).
+
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [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
+    - __call__

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -23,6 +23,17 @@ from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
 
 
 class DDPMPipeline(DiffusionPipeline):
+    r"""
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Parameters:
+        unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
+            [`DDPMScheduler`], or [`DDIMScheduler`].
+    """
+
     def __init__(self, unet, scheduler):
         super().__init__()
         scheduler = scheduler.set_format("pt")
@@ -37,6 +48,19 @@ class DDPMPipeline(DiffusionPipeline):
         return_dict: bool = True,
         **kwargs,
     ) -> Union[ImagePipelineOutput, Tuple]:
+        r"""
+        Args:
+            batch_size (:obj:`int`, *optional*, defaults to 1):
+                The number of images to generate.
+            generator (:obj:`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 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 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+        """
         if "torch_device" in kwargs:
             device = kwargs.pop("torch_device")
             warnings.warn(