Tools tagged with "LoRA"

Wan2GP brings video, image, music and speech generation to your own computer, with particular attention to GPUs with limited memory. It's for creators who want several media models in one browser interface. The project builds on Wan-Video/Wan2.1.

DiffSynth-Studio is a Python diffusion model engine for developers and researchers who want to generate media and train models on their own hardware. It supports large models on consumer GPUs through memory offloading and quantization, with inference and training in the same framework. It's open source under Apache 2.0.

vLLM is an open source engine for serving large language models on hardware you control. It suits developers and teams that need to handle many requests through an API while making efficient use of memory and compute. It's licensed under Apache 2.0 and can run with GPUs or on a CPU.

Diffusers is an open-source Python library for developers and researchers who want to run diffusion models on their own hardware or build generation features into an application. It uses PyTorch and supports image, video and audio generation. The library is licensed under Apache 2.0 and supports Apple Silicon.

SD.Next is a self-hosted web interface for artists, researchers and people who want to generate and edit images or videos on their own hardware. It builds on Automatic1111 WebUI's original codebase and supports Stable Diffusion alongside other diffusion models. It's open source under Apache 2.0.

Gemma is Google DeepMind’s family of open-weight AI models for developers building applications that can run on their own hardware. Its range covers compact models for phones and IoT devices alongside larger Gemma 4 models for reasoning on personal computers and servers. Some applications can work offline, keeping model inference on the device. Google AI Studio and Google Cloud are also available for hosted use.

ComfyUI is a local visual AI workspace for artists and technical teams who want to control how images, video, audio, 3D models and text are made. Its node canvas shows each model and processing step, so users can build and adjust workflows without writing code. It runs on your hardware.

MLX is a machine learning array framework for researchers and developers building models on their own hardware. Its distinctive feature on Apple silicon is shared CPU and GPU memory: both processors can work on the same arrays without copying data between them. It's open source under the MIT license.

LLaMA-Factory is an open-source framework for developers and researchers who want to adapt language and multimodal models on their own hardware. It brings training and inference into one toolkit, with support for LLaMA, Qwen3, Qwen3-VL, DeepSeek, Gemma, Mistral and LLaVA. Its license is Apache 2.0.

InvokeAI is a free, open-source image generator for artists and production teams who want to create and edit images on their own hardware. Its locally hosted web interface brings image generation, canvas editing and workflow tools into one application. With local models, prompts and artwork stay on the machine you control.

SGLang is a self-hosted inference framework for teams that need to serve language and multimodal models on their own hardware. It runs on a single GPU or across distributed clusters and exposes an OpenAI-compatible API. The project is open source under the Apache 2.0 license.

Unsloth brings model training and everyday AI use into a desktop app for people who want to run models on their own hardware. Its no-code interface covers chat, fine-tuning and media generation on macOS, Windows and Linux. The Unsloth software is open source under Apache 2.0.

DataDreamer connects LLM prompting, synthetic data generation, and model training in one Python library. It's for researchers and developers who want to build datasets and use them to fine-tune or align models in reproducible workflows. The library is open source under the MIT license.

Fooocus is a free, open-source AI image generator for people who want to create images on their own computer without spending much time tuning settings. It uses Stable Diffusion XL and automatically expands prompts with a local GPT-2 engine. Generation works offline once the required models are downloaded, so prompts and images can stay on your machine.

FluxGym is a local web interface for training FLUX LoRAs on your own images, with support for GPUs with 12GB, 16GB or 20GB of VRAM. It's for people who want to customize an image model through a browser while retaining access to detailed training controls. It runs on Windows and Linux, with Docker support, and is open source under the MIT license.

NMKD Stable Diffusion GUI is a local AI image generator for people who want to create and edit images on a Windows PC. It combines Stable Diffusion generation with inpainting, LoRA training and image post-processing in a desktop interface. It's open source under GPL-3.0.

ColPali is a local AI document retrieval library for developers and researchers building document search or retrieval-augmented generation systems. It searches pages as images, using their text, charts and layout together rather than relying on a separate OCR pipeline. The colpali-engine package is deprecated; its maintainers recommend Sentence Transformers for new projects and production use.

Jina Embeddings is a family of models that converts content into vectors for retrieval, similarity matching, classification and clustering. It includes multilingual text models and multimodal variants for searching across different media.

Ludwig is an open-source Python framework for developers and researchers who want to train custom AI models on their own hardware. A YAML file describes the model and training pipeline, while Ludwig handles preprocessing, training and evaluation. It uses the Apache 2.0 license. Install the Python package with the optional LLM dependencies for fine-tuning; current source requires Python 3.12 or later.

H2O LLM Studio is a self-hosted tool for teams that want to adapt language models to their own datasets without writing training code. Its browser interface brings training experiments, evaluation, and model testing into one place. The project is open source under Apache 2.0.

AnimateDiff adds text-driven animation to personalized Stable Diffusion models without requiring separate training for each model. It's for artists and developers who want to generate motion while keeping the visual style of a chosen image model. The Python implementation runs locally and includes a Gradio browser interface.

Mochi 1 is a text-to-video model for creators and developers who want to generate videos on their own hardware or adapt a model to their own footage. Genmo releases it under Apache 2.0, with downloadable weights and code for local use. Genmo also offers a hosted playground for trying the model in a browser.

CogVideoX is a family of downloadable video generation models for developers, researchers and creators who want to generate clips on their own hardware. It turns English text prompts into video, animates a supplied image and can continue an existing video. A local Gradio web interface provides a browser front end for generation.

CogView4 is a text-to-image model you can run on your own hardware, with support for Chinese and English prompts and Chinese text within generated images. It's aimed at developers and image creators who want local AI generation with native Chinese language support. The CogView4-6B model weights and repository code use Apache 2.0.

Sonar is a self-hosted inference engine for developers and teams serving Hugging Face-compatible language and multimodal models on their own hardware. Based on vLLM, it adds model and quantization formats, sampling methods, and deployment features. It's open source under AGPL-3.0.

Qwen-Image is an open-source image generation and editing model you can deploy locally. It's for developers and creators who want to generate images from text or revise existing pictures on their own hardware. Its text rendering capabilities, especially for Chinese, make it relevant for images that need readable lettering alongside visual content.

SmolVLM is a compact vision language model from Hugging Face for developers building local AI applications that work with images and text. It can describe pictures, answer questions about diagrams, and read information from documents such as invoices. Its small memory footprint makes on-device use practical on laptops and smaller local setups.

Jamba is AI21's language model family for teams building AI applications on their own servers. The documented Large 1.7 model combines Mamba state-space models with Transformer attention to process long context efficiently. A 256K-token context window makes it relevant for work that depends on lengthy documents, such as investment research, due diligence and reviewing procurement responses.

Kaito manages self-hosted LLM inference, fine-tuning, and document retrieval services in a Kubernetes cluster. It's for teams that want to run models on infrastructure they control while reducing the work of sizing GPU resources and managing model deployments. The project is open source under Apache 2.0.

GLM-4.5 is an open-source language model for developers building AI agents and coding tools on their own servers. It combines reasoning with tool calling and offers a choice between thinking mode for complex tasks and non-thinking mode for direct responses. The MIT license permits commercial use and modification.

lm-evaluation-harness lets researchers and model developers compare language models using shared academic benchmarks and public prompts. It runs evaluations against local models and benchmarks, or sends requests to a hosted model API. EleutherAI's Python framework is open source under the MIT license and powers Hugging Face's Open LLM Leaderboard.

Amuse combines AI generation with media editing in a Windows app that runs models on your own hardware. It's for people who want to create images, video, audio and text locally, then work on the results in the same application. Its editor also accepts existing local video.

MiniCPM-V is a family of local vision-language models for developers building apps that interpret images and video on their own hardware. It supports iOS, Android and HarmonyOS, as well as Mac deployment and server inference. The current repository states that MiniCPM-o/V code and model weights use Apache 2.0.

Kolors is a text-to-image model for people who want to generate photorealistic images on their own hardware, including work with Chinese prompts and Chinese cultural content. Developed by Kuaishou, it understands prompts in Chinese and English and can render text in both languages within generated images.

LLaVA is a family of vision-language models for researchers and developers who want to ask questions about images on their own hardware. It pairs a CLIP vision encoder with a language model to support image descriptions, visual reasoning and reading text in pictures. Its Python code is open source under Apache 2.0; the project places research-use restrictions on its data and checkpoints, with additional terms from the underlying models.

StarCoder2 is a family of code generation models for developers who want to run code completion on their own hardware or adapt a model to their code. It predicts code continuations rather than following conversational instructions, so it's suited to completion workflows rather than a chat-based coding assistant.