Package: diffusers
🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus onusability over performance,simple over easy, andcustomizability over abstractions.🤗 Diffusers offers three core components:State-of-the-artdiffusion pipelinesthat can be run in inference with just a few lines of code.Interchangeable noiseschedulersfor different diffusion speeds and output quality.Pretrainedmodelsthat can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.InstallationWe recommend installing 🤗 Diffusers in a virtual environment from PyPI or Conda. For more details about installingPyTorchandFlax, please refer to their official documentation.PyTorchWithpip(official package):pipinstall--upgradediffusers[torch]Withconda(maintained by the community):condainstall-cconda-forgediffusersFlaxWithpip(official package):pipinstall--upgradediffusers[flax]Apple Silicon (M1/M2) supportPlease refer to theHow to use Stable Diffusion in Apple Siliconguide.QuickstartGenerating outputs is super easy with 🤗 Diffusers. To generate an image from text, use thefrom_pretrainedmethod to load any pretrained diffusion model (browse theHubfor 19000+ checkpoints):fromdiffusersimportDiffusionPipelineimporttorchpipeline=DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",torch_dtype=torch.float16)pipeline.to("cuda")pipeline("An image of a squirrel in Picasso style").images[0]You can also dig into the models and schedulers toolbox to build your own diffusion system:fromdiffusersimportDDPMScheduler,UNet2DModelfromPILimportImageimporttorchscheduler=DDPMScheduler.from_pretrained("google/ddpm-cat-256")model=UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")scheduler.set_timesteps(50)sample_size=model.config.sample_sizenoise=torch.randn((1,3,sample_size,sample_size),device="cuda")input=noisefortinscheduler.timesteps:withtorch.no_grad():noisy_residual=model(input,t).sampleprev_noisy_sample=scheduler.step(noisy_residual,t,input).prev_sampleinput=prev_noisy_sampleimage=(input/2+0.5).clamp(0,1)image=image.cpu().permute(0,2,3,1).numpy()[0]image=Image.fromarray((image*255).round().astype("uint8"))imageCheck out theQuickstartto launch your diffusion journey today!How to navigate the documentationDocumentationWhat can I learn?TutorialA basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model.LoadingGuides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers.Pipelines for inferenceGuides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library.OptimizationGuides for how to optimize your diffusion model to run faster and consume less memory.TrainingGuides for how to train a diffusion model for different tasks with different training techniques.ContributionWe ❤️  contributions from the open-source community!
If you want to contribute to this library, please check out ourContribution guide.
You can look out forissuesyou'd like to tackle to contribute to the library.SeeGood first issuesfor general opportunities to contributeSeeNew model/pipelineto contribute exciting new diffusion models / diffusion pipelinesSeeNew schedulerAlso, say 👋 in our public Discord channel. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out ☕.Popular Tasks & PipelinesTaskPipeline🤗 HubUnconditional Image GenerationDDPMgoogle/ddpm-ema-church-256Text-to-ImageStable Diffusion Text-to-Imagerunwayml/stable-diffusion-v1-5Text-to-ImageunCLIPkakaobrain/karlo-v1-alphaText-to-ImageDeepFloyd IFDeepFloyd/IF-I-XL-v1.0Text-to-ImageKandinskykandinsky-community/kandinsky-2-2-decoderText-guided Image-to-ImageControlNetlllyasviel/sd-controlnet-cannyText-guided Image-to-ImageInstructPix2Pixtimbrooks/instruct-pix2pixText-guided Image-to-ImageStable Diffusion Image-to-Imagerunwayml/stable-diffusion-v1-5Text-guided Image InpaintingStable Diffusion Inpaintingrunwayml/stable-diffusion-inpaintingImage VariationStable Diffusion Image Variationlambdalabs/sd-image-variations-diffusersSuper ResolutionStable Diffusion Upscalestabilityai/stable-diffusion-x4-upscalerSuper ResolutionStable Diffusion Latent Upscalestabilityai/sd-x2-latent-upscalerPopular libraries using 🧨 Diffusershttps://github.com/microsoft/TaskMatrixhttps://github.com/invoke-ai/InvokeAIhttps://github.com/apple/ml-stable-diffusionhttps://github.com/Sanster/lama-cleanerhttps://github.com/IDEA-Research/Grounded-Segment-Anythinghttps://github.com/ashawkey/stable-dreamfusionhttps://github.com/deep-floyd/IFhttps://github.com/bentoml/BentoMLhttps://github.com/bmaltais/kohya_ss+8000 other amazing GitHub repositories 💪Thank you for using us ❤️.CreditsThis library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:@CompVis' latent diffusion models library, availablehere@hojonathanho original DDPM implementation, availablehereas well as the extremely useful translation into PyTorch by @pesser, availablehere@ermongroup's DDIM implementation, availablehere@yang-song's Score-VE and Score-VP implementations, availablehereWe also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, availablehereas well as @crowsonkb and @rromb for useful discussions and insights.Citation@misc{von-platen-etal-2022-diffusers,author={Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},title={Diffusers: State-of-the-art diffusion models},year={2022},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/huggingface/diffusers}}}