#
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 want to train your own diffusion model, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions.

The library has three main components:

- State-of-the-art diffusion pipelines for inference with just a few lines of code.
- Interchangeable noise schedulers for balancing trade-offs between generation speed and quality.
- Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.

Tutorials

Learn the fundamental skills you need to start generating outputs, build your own diffusion system, and train a diffusion model. We recommend starting here if you're using 🤗 Diffusers for the first time!

How-to guides

Practical guides for helping you load pipelines, models, and schedulers. You'll also learn how to use pipelines for specific tasks, control how outputs are generated, optimize for inference speed, and different training techniques.

Conceptual guides

Understand why the library was designed the way it was, and learn more about the ethical guidelines and safety implementations for using the library.

Reference

Technical descriptions of how 🤗 Diffusers classes and methods work.

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Supported pipelines

Pipeline | Paper/Repository | Tasks
--- | --- | ---
alt_diffusion | AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities | Image-to-Image Text-Guided Generation
audio_diffusion | Audio Diffusion | Unconditional Audio Generation
controlnet | Adding Conditional Control to Text-to-Image Diffusion Models | Image-to-Image Text-Guided Generation
cycle_diffusion | Unifying Diffusion Models’ Latent Space, with Applications to CycleDiffusion and Guidance | Image-to-Image Text-Guided Generation
dance_diffusion | Dance Diffusion | Unconditional Audio Generation
ddpm | Denoising Diffusion Probabilistic Models | Unconditional Image Generation
ddim | Denoising Diffusion Implicit Models | Unconditional Image Generation
if | IF | Image Generation
if_img2img | IF | Image-to-Image Generation
if_inpainting | IF | Image-to-Image Generation
latent_diffusion | High-Resolution Image Synthesis with Latent Diffusion Models | Text-to-Image Generation
latent_diffusion | High-Resolution Image Synthesis with Latent Diffusion Models | Super Resolution Image-to-Image
latent_diffusion_uncond | High-Resolution Image Synthesis with Latent Diffusion Models | Unconditional Image Generation
paint_by_example | Paint by Example: Exemplar-based Image Editing with Diffusion Models | Image-Guided Image Inpainting
pndm | Pseudo Numerical Methods for Diffusion Models on Manifolds | Unconditional Image Generation
score_sde_ve | Score-Based Generative Modeling through Stochastic Differential Equations | Unconditional Image Generation
score_sde_vp | Score-Based Generative Modeling through Stochastic Differential Equations | Unconditional Image Generation
semantic_stable_diffusion | Semantic Guidance | Text-Guided Generation
stable_diffusion_adapter | T2I-Adapter | Image-to-Image Text-Guided Generation
stable_diffusion_text2img | Stable Diffusion | Text-to-Image Generation
stable_diffusion_img2img | Stable Diffusion | Image-to-Image Text-Guided Generation
stable_diffusion_inpaint | Stable Diffusion | Text-Guided Image Inpainting
stable_diffusion_panorama | MultiDiffusion | Text-to-Panorama Generation
stable_diffusion_pix2pix | InstructPix2Pix: Learning to Follow Image Editing Instructions | Text-Guided Image Editing
stable_diffusion_pix2pix_zero | Zero-shot Image-to-Image Translation | Text-Guided Image Editing
stable_diffusion_attend_and_excite | Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models | Text-to-Image Generation
stable_diffusion_self_attention_guidance | Improving Sample Quality of Diffusion Models Using Self-Attention Guidance | Text-to-Image Generation Unconditional Image Generation
stable_diffusion_image_variation | Stable Diffusion Image Variations | Image-to-Image Generation
stable_diffusion_latent_upscale | Stable Diffusion Latent Upscaler | Text-Guided Super Resolution Image-to-Image
stable_diffusion_model_editing | Editing Implicit Assumptions in Text-to-Image Diffusion Models | Text-to-Image Model Editing
stable_diffusion_2 | Stable Diffusion 2 | Text-to-Image Generation
stable_diffusion_2 | Stable Diffusion 2 | Text-Guided Image Inpainting
stable_diffusion_2 | Depth-Conditional Stable Diffusion | Depth-to-Image Generation
stable_diffusion_2 | Stable Diffusion 2 | Text-Guided Super Resolution Image-to-Image
stable_diffusion_safe | Safe Stable Diffusion | Text-Guided Generation
stable_unclip | Stable unCLIP | Text-to-Image Generation
stable_unclip | Stable unCLIP | Image-to-Image Text-Guided Generation
stochastic_karras_ve | Elucidating the Design Space of Diffusion-Based Generative Models | Unconditional Image Generation
text_to_video_sd | Modelscope’s Text-to-video-synthesis Model in Open Domain | Text-to-Video Generation
unclip | Hierarchical Text-Conditional Image Generation with CLIP Latents(implementation by kakaobrain) | Text-to-Image Generation
versatile_diffusion | Versatile Diffusion: Text, Images and Variations All in One Diffusion Model | Text-to-Image Generation
versatile_diffusion | Versatile Diffusion: Text, Images and Variations All in One Diffusion Model | Image Variations Generation
versatile_diffusion | Versatile Diffusion: Text, Images and Variations All in One Diffusion Model | Dual Image and Text Guided Generation
vq_diffusion | Vector Quantized Diffusion Model for Text-to-Image Synthesis | Text-to-Image Generation
stable_diffusion_ldm3d | LDM3D: Latent Diffusion Model for 3D | Text to Image and Depth Generation