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 from_pretrained() are automatically detected and the pipeline components are loaded and passed to the __init__ function of the pipeline. 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.  Pipelines do not offer any training functionality. You’ll notice PyTorch’s autograd is disabled by decorating the __call__() method with a torch.no_grad decorator because pipelines should not be used for training. If you’re interested in training, please take a look at the Training guides instead! 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 image2image AnimateDiff text2video Attend-and-Excite text2image Audio Diffusion image2audio AudioLDM text2audio AudioLDM2 text2audio BLIP Diffusion text2image Consistency Models unconditional image generation ControlNet text2image, image2image, inpainting ControlNet with Stable Diffusion XL text2image ControlNet-XS text2image ControlNet-XS with Stable Diffusion XL text2image Cycle Diffusion image2image Dance Diffusion unconditional audio generation DDIM unconditional image generation DDPM unconditional image generation DeepFloyd IF text2image, image2image, inpainting, super-resolution DiffEdit inpainting DiT text2image GLIGEN text2image InstructPix2Pix image editing Kandinsky 2.1 text2image, image2image, inpainting, interpolation Kandinsky 2.2 text2image, image2image, inpainting Kandinsky 3 text2image, image2image Latent Consistency Models text2image Latent Diffusion text2image, super-resolution LDM3D text2image, text-to-3D, text-to-pano, upscaling MultiDiffusion text2image MusicLDM text2audio Paint by Example inpainting ParaDiGMS text2image Pix2Pix Zero image editing PixArt-α text2image PNDM unconditional image generation RePaint inpainting Score SDE VE unconditional image generation Self-Attention Guidance text2image Semantic Guidance text2image Shap-E text-to-3D, image-to-3D Spectrogram Diffusion  Stable Diffusion text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution Stable Diffusion Model Editing model editing Stable Diffusion XL text2image, image2image, inpainting Stable Diffusion XL Turbo text2image, image2image, inpainting Stable unCLIP text2image, image variation Stochastic Karras VE unconditional image generation T2I-Adapter text2image Text2Video text2video, video2video Text2Video-Zero text2video unCLIP text2image, image variation Unconditional Latent Diffusion unconditional image generation UniDiffuser text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation Value-guided planning value guided sampling Versatile Diffusion text2image, image variation VQ Diffusion text2image Wuerstchen text2image  DiffusionPipeline  class diffusers.DiffusionPipeline  < source > ( )    Base class for all pipelines. DiffusionPipeline stores all components (models, schedulers, and processors) for diffusion pipelines and