diffusers/pr_12509/en/api/pipelines/ddpm.md

# DDPM

[Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2006.11239) (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](https://github.com/hojonathanho/diffusion).

> [!TIP]
> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

# DDPMPipeline[[diffusers.DDPMPipeline]]
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<docstring><name>class diffusers.DDPMPipeline</name><anchor>diffusers.DDPMPipeline</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12509/src/diffusers/pipelines/ddpm/pipeline_ddpm.py#L35</source><parameters>[{"name": "unet", "val": ": UNet2DModel"}, {"name": "scheduler", "val": ": DDPMScheduler"}]</parameters><paramsdesc>- **unet** ([UNet2DModel](/docs/diffusers/pr_12509/en/api/models/unet2d#diffusers.UNet2DModel)) --
  A `UNet2DModel` to denoise the encoded image latents.
- **scheduler** ([SchedulerMixin](/docs/diffusers/pr_12509/en/api/schedulers/overview#diffusers.SchedulerMixin)) --
  A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
  [DDPMScheduler](/docs/diffusers/pr_12509/en/api/schedulers/ddpm#diffusers.DDPMScheduler), or [DDIMScheduler](/docs/diffusers/pr_12509/en/api/schedulers/ddim#diffusers.DDIMScheduler).</paramsdesc><paramgroups>0</paramgroups></docstring>

Pipeline for image generation.

This model inherits from [DiffusionPipeline](/docs/diffusers/pr_12509/en/api/pipelines/overview#diffusers.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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<docstring><name>__call__</name><anchor>diffusers.DDPMPipeline.__call__</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12509/src/diffusers/pipelines/ddpm/pipeline_ddpm.py#L56</source><parameters>[{"name": "batch_size", "val": ": int = 1"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "num_inference_steps", "val": ": int = 1000"}, {"name": "output_type", "val": ": typing.Optional[str] = 'pil'"}, {"name": "return_dict", "val": ": bool = True"}]</parameters><paramsdesc>- **batch_size** (`int`, *optional*, defaults to 1) --
  The number of images to generate.
- **generator** (`torch.Generator`, *optional*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) 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](/docs/diffusers/pr_12509/en/api/pipelines/consistency_models#diffusers.ImagePipelineOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[ImagePipelineOutput](/docs/diffusers/pr_12509/en/api/pipelines/consistency_models#diffusers.ImagePipelineOutput) or `tuple`</rettype><retdesc>If `return_dict` is `True`, [ImagePipelineOutput](/docs/diffusers/pr_12509/en/api/pipelines/consistency_models#diffusers.ImagePipelineOutput) is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images</retdesc></docstring>

The call function to the pipeline for generation.



<ExampleCodeBlock anchor="diffusers.DDPMPipeline.__call__.example">

Example:

```py
>>> 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")
```

</ExampleCodeBlock>






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## ImagePipelineOutput[[diffusers.ImagePipelineOutput]]
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<docstring><name>class diffusers.ImagePipelineOutput</name><anchor>diffusers.ImagePipelineOutput</anchor><source>https://github.com/huggingface/diffusers/blob/vr_12509/src/diffusers/pipelines/pipeline_utils.py#L118</source><parameters>[{"name": "images", "val": ": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"}]</parameters><paramsdesc>- **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)`.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output class for image pipelines.




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