#
DDPM

Denoising Diffusion Probabilistic Models (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the 🤗 Diffusers library, DDPM refers to the discrete denoising scheduler from the paper as well as the pipeline.

The abstract from the paper is:

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 can be found at hohonathanho/diffusion.

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

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DDPMPipeline

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class diffusers.DDPMPipeline

< source >

( unet scheduler )

Parameters

- unet (UNet2DModel) — A `UNet2DModel` to denoise the encoded image latents.
- scheduler (SchedulerMixin) — A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of DDPMScheduler, or DDIMScheduler.

Pipeline for image generation.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

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__call__

< source >

( batch_size: int = 1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None num_inference_steps: int = 1000 output_type: typing.Optional[str] = 'pil' return_dict: bool = True ) →
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 generated image. Choose between `PIL.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`

If `return_dict` is `True`, ImagePipelineOutput is returned, otherwise a `tuple` is returned where the first element is a list with the generated images

The call function to the pipeline for generation.

Example:

    >>> from diffusers import DDPMPipeline

    >>> # load model and scheduler
    >>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256")

    >>> # run pipeline in inference (sample random noise and denoise)
    >>> image = pipe().images[0]

    >>> # save image
    >>> image.save("ddpm_generated_image.png")

##
ImagePipelineOutput

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class diffusers.ImagePipelineOutput

< source >

( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] )

Parameters

- images (`List[PIL.Image.Image]` or `np.ndarray`) — List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`.

Output class for image pipelines.

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