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DDPM

Overview

Denoising Diffusion Probabilistic Models (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.

Available Pipelines:

Pipeline Tasks Colab
pipeline_ddpm.py Unconditional Image Generation -

DDPMPipeline

class diffusers.DDPMPipeline

< >

( unet scheduler )

Parameters

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.)

__call__

< >

( batch_size: int = 1 generator: typing.Optional[torch._C.Generator] = None num_inference_steps: int = 1000 output_type: typing.Optional[str] = 'pil' return_dict: bool = True **kwargs ) → ImagePipelineOutput or tuple

Parameters

  • batch_size (int, optional, defaults to 1) — The number of images to generate.
  • generator (torch.Generator, optional) — A torch generator to make generation deterministic.
  • num_inference_steps (int, optional, defaults to 1000) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
  • output_type (str, optional, defaults to "pil") — The output format of the generate image. Choose between PIL: PIL.Image.Image or np.array.
  • return_dict (bool, optional, defaults to True) — Whether or not to return a ImagePipelineOutput instead of a plain tuple.

Returns

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.