Tools tagged with "LoRA"

Sana is an open-source framework for running image and video generation on your own hardware, with image models small enough for laptop GPUs. It's aimed at creators who want local AI generation and developers who need training and inference pipelines for their own models. The code uses the Apache 2.0 license.

Intel IPEX-LLM is a library for developers running or fine-tuning models on Intel hardware. The project is archived and no longer maintained. Intel reports known security issues and no longer accepts patches or provides updates. The code is open source under Apache 2.0.

OmniGen is a local AI image generation model that handles text prompts, reference images, and image editing within one model. It's for creators who want to reuse subjects across images and developers building image tools on their own hardware. The code is open source under the MIT license.

Lumina-Image 2.0 is a local AI image generation framework for developers, researchers and people who want to generate images from text on their own hardware. It provides downloadable checkpoints, generation code and tools for adapting the model to your own image collections. The code uses the Apache 2.0 license.

Orpheus TTS is an open-source text-to-speech system for developers building voice applications or adapting speech models to their own recordings. It runs locally and uses a Llama backbone to generate speech with control over emotion and intonation. The code uses the Apache 2.0 license.

HQQ is a Python library that compresses language and vision models without needing a calibration dataset. It's for developers preparing models to run on their own hardware or servers, particularly when GPU memory limits the model they can use. The library is open source under Apache 2.0.

Musubi Tuner is a Python toolkit for training LoRA adapters for image and video generation models on your own hardware. It's aimed at people who want to customize these models using their own datasets and are comfortable working with training scripts. It also includes image and video generation scripts for supported architectures.

diffusion-pipe is a local diffusion model training tool for people fine-tuning image and video models on their own GPU hardware. Its main distinction is that it can divide a model across several GPUs when it won't fit on one, while also distributing training work across GPUs. The Python project is open source under GPL-3.0 and uses DeepSpeed.

GPTQModel is a Python toolkit for developers compressing LLMs and running them on their own hardware or servers. It brings model calibration, compression, quality checks and inference into one API, so teams can compare quantization methods without adopting a separate tool for each one.

sd-scripts is a collection of Python scripts for training and generating images with models on your own hardware. It's aimed at people who want to customize image models through LoRA training or deeper fine-tuning and are comfortable working with scripts. The project is open source under the Apache 2.0 license.

AIBrix is open-source infrastructure for teams serving large language models on their own Kubernetes clusters. It focuses on the work around inference: directing requests, scaling capacity and managing models across servers. Enterprise infrastructure teams can use its components to build a self-hosted model service. It's licensed under Apache 2.0.

SimpleTuner is an open-source toolkit for fine-tuning image, video and audio generation models on your own hardware or GPU servers. It's for creators and researchers adapting models to their datasets, and teams sharing training infrastructure. A web dashboard manages training jobs.

Oumi builds specialized AI models for teams that want control over their training data, model weights, and deployment. Its Apache 2.0 open-source stack runs on laptops, clusters, and your own servers, while its hosted service automates model development from a plain-English task description. You own the resulting weights, data, and training recipes.

TorchAO is a PyTorch library for developers who want to train or run models on their own hardware with less memory and faster computation. It reduces the precision of model weights and activations, with options for language models and image or video generation. Its PyTorch integration works with torch.compile and FSDP2 across most Hugging Face PyTorch models.

text-generation-webui, also called TextGen, runs language models on your own hardware through a desktop app or a self-hosted browser interface. It's for people who want private chat and writing tools, and developers who need a local model API. It works offline without telemetry; web search and page fetching use the internet.

Stable Diffusion WebUI Forge runs image generation on your own hardware through a browser interface. It builds on Stable Diffusion WebUI and suits people who want its image creation tools with more control over GPU memory use, as well as developers extending those tools. It's open source under AGPL-3.0.

TRL is a Python library for developers and researchers who want to adapt foundation models on their own hardware. It builds on Hugging Face Transformers and covers supervised fine-tuning, reinforcement learning and training from preference feedback. It's open source under Apache 2.0.

Stable Diffusion web UI (AUTOMATIC1111) is a browser interface for generating and editing images with models running on your own hardware. It's for artists and anyone who wants control over prompts, models and image variations. The software is open source under AGPL-3.0.

Kohya's GUI lets you train and fine-tune image generation models on your own GPU-equipped computer through a browser interface. It's for artists and model makers who want to teach a model a particular style or subject while controlling the training settings. The interface builds on Kohya's Stable Diffusion training scripts, with a command-line interface available too.

Stability Matrix is an open source desktop app for people who use more than one Stable Diffusion interface. It manages local installations of ComfyUI, Automatic1111, Fooocus, Forge, and InvokeAI, so you can try different workflows without maintaining a separate model collection for each one. It runs on Windows, macOS, and Linux under the AGPL-3.0 license.

Ollama JavaScript connects Node.js and browser applications to models running through Ollama. It's for developers building chat interfaces, AI agents or other apps that need a local LLM backend. The library is open source under the MIT license, with TypeScript types and an API that follows Ollama's REST interface.

ms-swift is a Python framework for developers and researchers who want to train and deploy language or multimodal models on their own hardware. It brings fine-tuning, evaluation and model serving into one project, with support for Qwen3, DeepSeek-R1, Llama4 and Mistral, plus multimodal models such as Qwen3-VL and InternVL3.5. It's open source under Apache 2.0.

AI Toolkit (ostris) is an MIT-licensed training suite for people who want to fine-tune image and video models on their own hardware or a self-hosted server. It targets consumer NVIDIA GPUs and runs on Linux and Windows, including ARM64 Linux systems such as DGX Spark. An experimental installer also supports Apple Silicon Macs. GPU memory needs depend on the model and training task.

verl is a Python library for teams training large language models on their own GPU infrastructure. It's the open-source implementation of HybridFlow, aimed at researchers and engineers who need reinforcement learning after initial model training. It uses the Apache 2.0 license.

Draw Things is an AI image generation app for iPhone, iPad and Mac that keeps generation on your device and works offline. It's for people who want to create and edit images without sending that work to a cloud service, including artists developing character concepts or trying out apparel designs.

BentoML is a Python framework for developers turning AI models into services on their own hardware or servers. It supports self-hosted inference APIs and multi-model applications, with Apache 2.0 licensing. You can develop and debug locally, then deploy the services in Docker containers, on Kubernetes, or in your own cloud.

LitGPT is a Python toolkit for developers and researchers who want to train, adapt and serve language models on their own hardware or servers. Its model implementations are written directly, with little abstraction between you and the code, so you can inspect model behavior and modify it for research or custom applications. It's open source under Apache 2.0.

OpenRLHF is a self-hosted Python framework for researchers and teams training language models with human feedback or custom rewards. It runs on your own NVIDIA GPU hardware, with Docker support and distributed training across servers. It's open source under Apache 2.0.

mistral.rs is an open source inference engine for running models on your own computer or self-hosted server. It's for developers building AI applications and people who want local chat, multimodal models and agent tools in the same runtime. The Rust project uses the MIT license.

DiffusionBee is an open-source AI art app for Mac users who want to generate and edit images on their own computer. It runs Stable Diffusion offline, with image generation processed on the device. Model downloads require network access, and optional image uploads can send images externally. Its visual interface suits artists and designers who want local image tools without working through code.

torchtune is a Python library for developers and researchers who want to adapt LLMs on their own GPU hardware using PyTorch. Its editable training recipes suit work that needs control over the training code and model implementations. The project is no longer actively maintained.

LMDeploy is an open-source toolkit for developers serving language and vision-language models on their own hardware. It combines model compression with inference and self-hosted APIs, so teams can use it for batch processing or as the model backend for an application. It uses the Apache 2.0 license.

TensorRT-LLM is a library for developers running LLMs on their own NVIDIA GPUs or self-hosted servers. It focuses on inference performance, with support for a single GPU, multiple GPUs, or deployments spread across several machines. Its PyTorch architecture lets teams adapt models and extend the runtime in Python.

OneTrainer is an open-source application for training diffusion models on your own machine, with dataset preparation and model previews in the same interface. It's for people adapting image or video models with their own training data. It runs on Windows, macOS and Linux under the AGPL-3.0 license.

bitsandbytes is an open-source Python library for developers who need to fit large language model inference or fine-tuning into less memory on their own hardware. It works with PyTorch and carries the MIT license. Its focus is the memory cost of model weights and training, rather than a chat interface.

KTransformers is an open-source framework for running and fine-tuning large language models on your own hardware. It focuses on mixture-of-experts (MoE) models, distributing work between CPU memory and GPU resources to reduce the GPU memory needed. It's aimed at researchers and developers who want to serve or adapt models such as DeepSeek-V3 and DeepSeek-R1.