overview.md

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# Pipelines

Pipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different schedulers or even model components.

All pipelines are built from the base [`DiffusionPipeline`] class which provides basic functionality for loading, downloading, and saving all the components. Specific pipeline types (for example [`StableDiffusionPipeline`]) loaded with [`~DiffusionPipeline.from_pretrained`] are automatically detected and the pipeline components are loaded and passed to the `__init__` function of the pipeline.

<Tip warning={true}>

You shouldn't use the [`DiffusionPipeline`] class for training. Individual components (for example, [`UNet2DModel`] and [`UNet2DConditionModel`]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.

<br>

Pipelines do not offer any training functionality. You'll notice PyTorch's autograd is disabled by decorating the [`~DiffusionPipeline.__call__`] method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you're interested in training, please take a look at the [Training](../../training/overview) guides instead!

</Tip>

The table below lists all the pipelines currently available in 🤗 Diffusers and the tasks they support. Click on a pipeline to view its abstract and published paper.

| Pipeline | Tasks |
|---|---|
| [AltDiffusion](alt_diffusion) | image2image |
| [AnimateDiff](animatediff) | text2video |
| [Attend-and-Excite](attend_and_excite) | text2image |
| [Audio Diffusion](audio_diffusion) | image2audio |
| [AudioLDM](audioldm) | text2audio |
| [AudioLDM2](audioldm2) | text2audio |
| [BLIP Diffusion](blip_diffusion) | text2image |
| [Consistency Models](consistency_models) | unconditional image generation |
| [ControlNet](controlnet) | text2image, image2image, inpainting |
| [ControlNet with Stable Diffusion XL](controlnet_sdxl) | text2image |
| [Cycle Diffusion](cycle_diffusion) | image2image |
| [Dance Diffusion](dance_diffusion) | unconditional audio generation |
| [DDIM](ddim) | unconditional image generation |
| [DDPM](ddpm) | unconditional image generation |
| [DeepFloyd IF](deepfloyd_if) | text2image, image2image, inpainting, super-resolution |
| [DiffEdit](diffedit) | inpainting |
| [DiT](dit) | text2image |
| [GLIGEN](stable_diffusion/gligen) | text2image |
| [InstructPix2Pix](pix2pix) | image editing |
| [Kandinsky 2.1](kandinsky) | text2image, image2image, inpainting, interpolation |
| [Kandinsky 2.2](kandinsky_v22) | text2image, image2image, inpainting |
| [Kandinsky 3](kandinsky3) | text2image, image2image |
| [Latent Consistency Models](latent_consistency_models) | text2image |
| [Latent Diffusion](latent_diffusion) | text2image, super-resolution |
| [LDM3D](stable_diffusion/ldm3d_diffusion) | text2image, text-to-3D, text-to-pano, upscaling |
| [MultiDiffusion](panorama) | text2image |
| [MusicLDM](musicldm) | text2audio |
| [Paint by Example](paint_by_example) | inpainting |
| [ParaDiGMS](paradigms) | text2image |
| [Pix2Pix Zero](pix2pix_zero) | image editing |
| [PixArt-α](pixart) | text2image |
| [PNDM](pndm) | unconditional image generation |
| [RePaint](repaint) | inpainting |
| [Score SDE VE](score_sde_ve) | unconditional image generation |
| [Self-Attention Guidance](self_attention_guidance) | text2image |
| [Semantic Guidance](semantic_stable_diffusion) | text2image |
| [Shap-E](shap_e) | text-to-3D, image-to-3D |
| [Spectrogram Diffusion](spectrogram_diffusion) |  |
| [Stable Diffusion](stable_diffusion/overview) | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
| [Stable Diffusion Model Editing](model_editing) | model editing |
| [Stable Diffusion XL](stable_diffusion/stable_diffusion_xl) | text2image, image2image, inpainting |
| [Stable unCLIP](stable_unclip) | text2image, image variation |
| [Stochastic Karras VE](stochastic_karras_ve) | unconditional image generation |
| [T2I-Adapter](stable_diffusion/adapter) | text2image |
| [Text2Video](text_to_video) | text2video, video2video |
| [Text2Video-Zero](text_to_video_zero) | text2video |
| [unCLIP](unclip) | text2image, image variation |
| [Unconditional Latent Diffusion](latent_diffusion_uncond) | unconditional image generation |
| [UniDiffuser](unidiffuser) | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |
| [Value-guided planning](value_guided_sampling) | value guided sampling |
| [Versatile Diffusion](versatile_diffusion) | text2image, image variation |
| [VQ Diffusion](vq_diffusion) | text2image |
| [Wuerstchen](wuerstchen) | text2image |

## DiffusionPipeline

[[autodoc]] DiffusionPipeline
	- all
	- __call__
	- device
	- to
	- components

## FlaxDiffusionPipeline

[[autodoc]] pipelines.pipeline_flax_utils.FlaxDiffusionPipeline

## PushToHubMixin

[[autodoc]] utils.PushToHubMixin