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Diffusers is a PyTorch-based library and generative AI framework used to build, train, and deploy diffusion pipelines for producing multi-modal media. It provides a suite of tools for generating images, video, and audio from natural language descriptions, as well as specialized systems for text-to-image generation.
The main features of huggingface/diffusers are: Diffusion Pipelines, Text-to-Image Generators, Custom Diffusion Model Training, End-to-End Inference Pipelines, Diffusion Models, Generative AI Pipelines, Latent Diffusion Models, Model Fine-Tuning.
Projects with overlapping indexed features include: modelscope/diffsynth-studio — DiffSynth-Studio is a comprehensive platform for the lifecycle management of generative diffusion models, providing a… deep-floyd/if — IF is a text-to-image diffusion system that translates natural language descriptions into visual imagery. The project… stability-ai/generative-models — This is a framework for training and sampling diffusion models to generate high-fidelity images, video, and 4D assets.… kohya-ss/sd-scripts — sd-scripts is a suite of utilities designed for fine-tuning generative models, preprocessing datasets, and converting… timothybrooks/instruct-pix2pix — Instruct-pix2pix is an instruction-based image model and PyTorch library designed to modify visual content by… black-forest-labs/flux — Flux is a diffusion model inference engine designed for text-to-image generation and image-to-image manipulation. It…
DiffSynth-Studio is a comprehensive platform for the lifecycle management of generative diffusion models, providing a unified environment for inference, fine-tuning, and training. It utilizes a modular pipeline architecture and a standardized abstraction layer to support consistent workflows across diverse model configurations for image and video generation. The platform distinguishes itself through a memory-optimized inference engine that dynamically manages resources to facilitate high-resolution generation on constrained hardware. It also integrates specialized training capabilities, inclu
IF is a text-to-image diffusion system that translates natural language descriptions into visual imagery. The project provides a generative pipeline for creating images, an inpainting tool for modifying specific image sections, and a super-resolution upscaler to increase pixel density and clarity. The system includes a concept fine-tuning framework that allows for the teaching of new visual concepts by updating a small set of parameters. It also supports image style transfer to apply the aesthetic characteristics of a reference image to a new output.
This is a framework for training and sampling diffusion models to generate high-fidelity images, video, and 4D assets. It provides a modular environment for managing generative AI training pipelines, including the handling of datasets, noise sampling, and loss weighting to stabilize the creation of synthetic content. The project features a modular model configuration system that uses YAML-based assembly to define network submodules and conditioners. It also includes a dedicated toolset for AI image watermarking, allowing for the embedding and detection of invisible markers to verify the origi
sd-scripts is a suite of utilities designed for fine-tuning generative models, preprocessing datasets, and converting model weights. It provides a collection of scripts for executing Stable Diffusion training through methods such as DreamBooth, textual inversion, and full fine-tuning, alongside a framework for creating and managing Low-Rank Adaptation weights. The project features specialized capabilities for model weight conversion between different architectures and precision formats. It includes tools for merging adaptation weights into base models, extracting weights from trained models,