<!--Copyright 2025 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. -->

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.

[!WARNING] 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 [~DiffusionPipeline.__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
aMUSEd	text2image
AnimateDiff	text2video
Attend-and-Excite	text2image
AudioLDM	text2audio
AudioLDM2	text2audio
AuraFlow	text2image
BLIP Diffusion	text2image
Bria 3.2	text2image
CogVideoX	text2video
Consistency Models	unconditional image generation
ControlNet	text2image, image2image, inpainting
ControlNet with Flux.1	text2image
ControlNet with Hunyuan-DiT	text2image
ControlNet with Stable Diffusion 3	text2image
ControlNet with Stable Diffusion XL	text2image
ControlNet-XS	text2image
ControlNet-XS with Stable Diffusion XL	text2image
Dance Diffusion	unconditional audio generation
DDIM	unconditional image generation
DDPM	unconditional image generation
DeepFloyd IF	text2image, image2image, inpainting, super-resolution
DiffEdit	inpainting
DiT	text2image
Flux	text2image
Hunyuan-DiT	text2image
I2VGen-XL	image2video
InstructPix2Pix	image editing
Kandinsky 2.1	text2image, image2image, inpainting, interpolation
Kandinsky 2.2	text2image, image2image, inpainting
Kandinsky 3	text2image, image2image
Kolors	text2image
Latent Consistency Models	text2image
Latent Diffusion	text2image, super-resolution
Latte	text2image
LEDITS++	image editing
Lumina-T2X	text2image
Marigold	depth-estimation, normals-estimation, intrinsic-decomposition
MultiDiffusion	text2image
MusicLDM	text2audio
PAG	text2image
Paint by Example	inpainting
PIA	image2video
PixArt-α	text2image
PixArt-Σ	text2image
Self-Attention Guidance	text2image
Semantic Guidance	text2image
Shap-E	text-to-3D, image-to-3D
Stable Audio	text2audio
Stable Cascade	text2image
Stable Diffusion	text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution
Stable Diffusion XL	text2image, image2image, inpainting
Stable Diffusion XL Turbo	text2image, image2image, inpainting
Stable unCLIP	text2image, image variation
T2I-Adapter	text2image
Text2Video	text2video, video2video
Text2Video-Zero	text2video
unCLIP	text2image, image variation
UniDiffuser	text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation
Value-guided planning	value guided sampling
Wuerstchen	text2image
VisualCloze	text2image, image2image, subject driven generation, inpainting, style transfer, image restoration, image editing, [depth,normal,edge,pose]2image, [depth,normal,edge,pose]-estimation, virtual try-on, image relighting

DiffusionPipeline

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

[[autodoc]] pipelines.StableDiffusionMixin.enable_freeu

[[autodoc]] pipelines.StableDiffusionMixin.disable_freeu

PushToHubMixin

[[autodoc]] utils.PushToHubMixin

Callbacks

[[autodoc]] callbacks.PipelineCallback

[[autodoc]] callbacks.SDCFGCutoffCallback

[[autodoc]] callbacks.SDXLCFGCutoffCallback

[[autodoc]] callbacks.SDXLControlnetCFGCutoffCallback

[[autodoc]] callbacks.IPAdapterScaleCutoffCallback

[[autodoc]] callbacks.SD3CFGCutoffCallback