Train LoRAs and Image Models Locally

HunyuanDiT is a local text-to-image model for people generating images from Chinese or English prompts. It handles detailed descriptions and Chinese cultural references, including poetry, idioms, food and architecture.

Diffusers is a Python library for developers and researchers building image, video and audio generation into applications or running models on their own hardware. It supports pretrained generation pipelines as well as custom diffusion systems.

DiffSynth-Studio is a diffusion model engine for developers and researchers generating media or training models on their own hardware. It combines inference and training, with memory-saving features for consumer GPUs.

npcpy is a Python library for developers and researchers building AI agents and applications with language models. It supports local LLM backends as well as cloud providers, so projects can choose where model requests run.

Draw Things creates and edits images on iPhone, iPad and Mac. It's for artists and other creators who want to work offline and keep generation on their own hardware.

DiffusionBee is an AI image app for Mac users who want to create and edit artwork locally through a visual interface. Artists and designers can work with text prompts, existing images and drawings without writing code.

MLX Tune is a Python library for adapting AI models on an Apple Silicon Mac. Formerly unsloth-mlx, it is an independent project and is not affiliated with Unsloth. Its Unsloth-compatible API lets you prototype training locally with minimal changes to existing scripts, then move to NVIDIA hardware with Unsloth. Training runs through Apple's MLX framework and doesn't require a cloud GPU.

Faceswap replaces faces in photos and videos using deep learning. It's for VFX artists, hobbyists and researchers who want to train face-swapping models on their own material and run them locally.

DL4MicEverywhere lets microscopy researchers train and use deep learning models on their own computers through graphical Jupyter notebooks. It builds on ZeroCostDL4Mic, bringing its bioimage analysis workflows to local machines and remote computing infrastructure. You don't need coding skills to use the notebook interfaces.

CSBDeep is a Python toolbox for researchers who want to restore fluorescence microscopy images using content-aware restoration (CARE). It covers training restoration models and applying them to images, with deep learning built on Keras and TensorFlow.

PaddleClas is a PaddlePaddle toolkit for developers and researchers building image classification and recognition applications on their own hardware. It covers fixed-category classifiers and image matching for large or frequently changing catalogs.

Lumina-Image 2.0 generates images from text on your own hardware. It's for developers, researchers and people who want a model they can use in visual workflows or adapt with their own image collections.