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biniou
a self-hosted webui for 30+ generative ai
Stars: 535
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biniou is a self-hosted webui for various GenAI (generative artificial intelligence) tasks. It allows users to generate multimedia content using AI models and chatbots on their own computer, even without a dedicated GPU. The tool can work offline once deployed and required models are downloaded. It offers a wide range of features for text, image, audio, video, and 3D object generation and modification. Users can easily manage the tool through a control panel within the webui, with support for various operating systems and CUDA optimization. biniou is powered by Huggingface and Gradio, providing a cross-platform solution for AI content generation.
README:
biniou is a self-hosted webui for several kinds of GenAI (generative artificial intelligence). You can generate multimedia contents with AI and use a chatbot on your own computer, even without dedicated GPU and starting from 8GB RAM. Can work offline (once deployed and required models downloaded).
GNU/Linux [ OpenSUSE base | RHEL base | Debian base ] โข Windows โข macOS Intel (experimental) โข Docker Documentation โ | Showroom ๐ผ๏ธ | Video presentation (by @Natlamir) ๐๏ธ | Windows installation tutorial (by Fahd Mirza) ๐๏ธ
-
๐ 2025-01-25 : ๐ฅ This week's updates ๐ฅ >
- Add support for Chatbot models bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF and mradermacher/Lucie-7B-Instruct-GGUF.
- Add support for Flux LoRA models glif-loradex-trainer/i12bp8_appelsiensam_vintagesign_v1, strangerzonehf/Ctoon-Plus-Plus, Jovie/Comics, den123/squidgame, prithivMLmods/Logo-Design-Flux-LoRA, saurabhswami/Vibrant-tech-3D, leonel4rd/Comicfx and noahyoungs/icon-generator.
- Add support for Flux model ostris/Flex.1-alpha. Please note that this huge (but highly qualitatives !) model will requires at least 64GB RAM for CPU-only inferences.
- Update of default Flux model from Freepik/flux.1-lite-8B-alpha to Freepik/flux.1-lite-8B.
-
๐ 2025-01-18 : ๐ฅ This week's updates ๐ฅ >
- Add support for Chatbot model prithivMLmods/GWQ-9B-Preview2-Q5_K_M-GGUF.
- Add support for Flux LoRA models ludocomito/flux-lora-caravaggio, strangerzonehf/Flux-Claude-Art, WiroAI/GTA6-style-flux-lora, strangerzonehf/Flux-Cardboard-Art-LoRA, strangerzonehf/Flux-Midjourney-Mix2-LoRA, strangerzonehf/Flux-Sketch-Ep-LoRA, alvdansen/flux_film_foto and glif-loradex-trainer/x_bulbul_x_90s_anime.
- New Flux LoRA category : Enhancement.
-
๐ 2025-01-12 : ๐ฅ New high-end Chatbot model ๐ฅ > Phi-4 is now available in biniou, via microsoft/phi-4-gguf. This model is absolutely awesome and cleary out of his league. It only weight 8.4 GB, and -so far- answered perfectly to all submited requests. Definitely a must try !
-
๐ 2025-01-11 : ๐ฅ This week's updates ๐ฅ >
- Add support for Chatbot high-end model bartowski/Qwentile2.5-32B-Instruct-GGUF and Chatbot model cognitivecomputations/Dolphin3.0-Llama3.1-8B-GGUF.
- Update of Chatbot default model to bartowski/Meta-Llama-3.1-8B-Instruct-GGUF and update of Qwen2.5-Coder-7B to bartowski/Qwen2.5.1-Coder-7B-Instruct-GGUF.
- Add support for Flux LoRA models keturn/woodcut-illustrations-Trousset-LoRA, glif-loradex-trainer/AP123_flux_dev_cutaway_style, glif-loradex-trainer/i12bp8_i12bp8_povshots_v1, glif-loradex-trainer/fabian3000_bosch, glif-loradex-trainer/i12bp8_i12bp8_greeksculptures_v1 and leonel4rd/DBZFLUX.
- Add preliminary suppport for ControlNet Flux models Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro and InstantX/FLUX.1-dev-Controlnet-Union. You currently had to manually select these models if you want to use them during inferences.
- Add support for Flux model AlekseyCalvin/AuraFlux_merge_diffusers. Please note that this huge (but highly qualitatives !) model will requires at least 70GB RAM for CPU-only inferences.
-
๐ 2025-01-04 : ๐ฅ This week's updates ๐ฅ >
- Happy new year 2025 to everyone !
- Add support for Chatbot specialized model bartowski/HuatuoGPT-o1-8B-GGUF, Chatbot High-end model DavidAU/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF and Chatbot models bartowski/DRT-o1-7B-GGUF and bartowski/Llama-3.1-8B-Open-SFT-GGUF.
- Add support for Flux LoRA models renderartist/toyboxflux, EvanZhouDev/open-genmoji, aixonlab/FLUX.1-dev-LoRA-Cinematic-1940s, glif-loradex-trainer/shipley_flux_dev_bookFoldArt_v1, fofr/flux-80s-cyberpunk, veryVANYA/ps1-style-flux, alvdansen/the-point-flux, mgwr/Cine-Aesthetic, WizWhite/Wizards_vintage_romance_novel-FLUX, WizWhite/wizard-s-paper-model-universe, leonel4rd/FluxDisney and Weiii722/SouthParkVibe.
- Add support for text2image with Flux models in IP-Adapter module
- Add support for Flux model enhanceaiteam/Mystic. Please note that this huge (but highly qualitatives !) model will requires at least 70GB RAM for CPU-only inferences.
- Bugfix for Flux pipelines following Diffusers upgrade.
โข Features
โข Prerequisites
โข Installation
ย ย ย ย GNU/Linux
ย ย ย ย ย ย OpenSUSE Leap 15.5 / OpenSUSE Tumbleweed
ย ย ย ย ย ย Rocky 9.3 / Alma 9.3 / CentOS Stream 9 / Fedora 39
ย ย ย ย ย ย Debian 12 / Ubuntu 22.04.3 / Ubuntu 24.04 / Linux Mint 21.2
ย ย ย ย Windows 10 / Windows 11
ย ย ย ย macOS Intel Homebrew install
ย ย ย ย Dockerfile
โข CUDA support
โข How To Use
โข Good to know
โข Credits
โข License
-
Text generation using :
- โ๏ธ llama-cpp based chatbot module (uses .gguf models)
- ๐๏ธ Llava multimodal chatbot module (uses .gguf models)
- ๐๏ธ Microsoft GIT image captioning module
- ๐ Whisper speech-to-text module
- ๐ฅ nllb translation module (200 languages)
- ๐ Prompt generator (require 16GB+ RAM for ChatGPT output type)
-
Image generation and modification using :
- ๐ผ๏ธ Stable Diffusion module
- ๐ผ๏ธ Kandinsky module (require 16GB+ RAM)
- ๐ผ๏ธ Latent Consistency Models module
- ๐ผ๏ธ Midjourney-mini module
- ๐ผ๏ธPixArt-Alpha module
- ๐๏ธ Stable Diffusion Img2img module
- ๐๏ธ IP-Adapter module
- ๐ผ๏ธ Stable Diffusion Image variation module (require 16GB+ RAM)
- ๐๏ธ Instruct Pix2Pix module
- ๐๏ธ MagicMix module
- ๐๏ธ Stable Diffusion Inpaint module
- ๐๏ธ Fantasy Studio Paint by Example module (require 16GB+ RAM)
- ๐๏ธ Stable Diffusion Outpaint module (require 16GB+ RAM)
- ๐ผ๏ธ Stable Diffusion ControlNet module
- ๐ผ๏ธ Photobooth module
- ๐ญ Insight Face faceswapping module
- ๐ Real ESRGAN upscaler module
- ๐GFPGAN face restoration module
-
Audio generation using :
- ๐ถ MusicGen module
- ๐ถ MusicGen Melody module (require 16GB+ RAM)
- ๐ถ MusicLDM module
- ๐ Audiogen module (require 16GB+ RAM)
- ๐ Harmonai module
- ๐ฃ๏ธ Bark module
-
Video generation and modification using :
- ๐ผ Modelscope module (require 16GB+ RAM)
- ๐ผ Text2Video-Zero module
- ๐ผ AnimateDiff module (require 16GB+ RAM)
- ๐ผ Stable Video Diffusion module (require 16GB+ RAM)
- ๐๏ธ Video Instruct-Pix2Pix module (require 16GB+ RAM)
-
3D objects generation using :
- ๐ง Shap-E txt2shape module
- ๐ง Shap-E img2shape module (require 16GB+ RAM)
-
Other features
- Zeroconf installation through one-click installers or Windows exe.
- User friendly : Everything required to run biniou is installed automatically, either at install time or at first use.
- WebUI in English, French, Chinese (traditional).
- Easy management through a control panel directly inside webui : update, restart, shutdown, activate authentication, control network access or share your instance online with a single click.
- Easy management of models through a simple interface.
- Communication between modules : send an output as an input to another module
- Powered by ๐ค Huggingface and gradio
- Cross platform : GNU/Linux, Windows 10/11 and macOS(experimental, via homebrew)
- Convenient Dockerfile for cloud instances
- Generation settings saved as metadatas in each content.
- Support for CUDA (see CUDA support)
- Experimental support for ROCm (see here)
- Support for Stable Diffusion SD-1.5, SD-2.1, SD-Turbo, SDXL, SDXL-Turbo, SDXL-Lightning, Hyper-SD, Stable Diffusion 3, LCM, VegaRT, Segmind, Playground-v2, Koala, Pixart-Alpha, Pixart-Sigma, Kandinsky and compatible models, through built-in model list or standalone .safetensors files
- Support for LoRA models (SD 1.5, SDXL and SD3)
- Support for textual inversion
- Support llama-cpp-python optimizations CUDA, OpenBLAS, OpenCL BLAS, ROCm and Vulkan through a simple setting
- Support for Llama/2/3, Mistral, Mixtral and compatible GGUF quantized models, through built-in model list or standalone .gguf files.
- Easy copy/paste integration for TheBloke GGUF quantized models.
-
Minimal hardware :
- 64bit CPU (AMD64 architecture ONLY)
- 8GB RAM
- Storage requirements :
- for GNU/Linux : at least 20GB for installation without models.
- for Windows : at least 30GB for installation without models.
- for macOS : at least ??GB for installation without models.
- Storage type : HDD
- Internet access (required only for installation and models download) : unlimited bandwidth optical fiber internet access
-
Recommended hardware :
- Massively multicore 64bit CPU (AMD64 architecture ONLY) and a GPU compatible with CUDA or ROCm
- 16GB+ RAM
- Storage requirements :
- for GNU/Linux : around 200GB for installation including all defaults models.
- for Windows : around 200GB for installation including all defaults models.
- for macOS : around ??GB for installation including all defaults models.
- Storage type : SSD Nvme
- Internet access (required only for installation and models download) : unlimited bandwidth optical fiber internet access
-
Operating system :
- a 64 bit OS :
- Debian 12
- Ubuntu 22.04.3 / 24.04
- Linux Mint 21.2+ / 22
- Rocky 9.3
- Alma 9.3
- CentOS Stream 9
- Fedora 39
- OpenSUSE Leap 15.5
- OpenSUSE Tumbleweed
- Windows 10 22H2
- Windows 11 22H2
- macOS ???
- a 64 bit OS :
Note : biniou supports Cuda or ROCm but does not require a dedicated GPU to run. You can install it in a virtual machine.
- Copy/paste and execute the following command in a terminal :
sh <(curl https://raw.githubusercontent.com/Woolverine94/biniou/main/oci-opensuse.sh || wget -O - https://raw.githubusercontent.com/Woolverine94/biniou/main/oci-opensuse.sh)
- Copy/paste and execute the following command in a terminal :
sh <(curl https://raw.githubusercontent.com/Woolverine94/biniou/main/oci-rhel.sh || wget -O - https://raw.githubusercontent.com/Woolverine94/biniou/main/oci-rhel.sh)
- Copy/paste and execute the following command in a terminal :
sh <(curl https://raw.githubusercontent.com/Woolverine94/biniou/main/oci-debian.sh || wget -O - https://raw.githubusercontent.com/Woolverine94/biniou/main/oci-debian.sh)
- Install the pre-requisites as root :
apt install git pip python3 python3-venv gcc perl make ffmpeg openssl
- Clone this repository as user :
git clone https://github.com/Woolverine94/biniou.git
- Launch the installer :
cd ./biniou
./install.sh
- (optional, but highly recommended) Install TCMalloc as root to optimize memory management :
apt install google-perftools
Windows installation has more prerequisites than GNU/Linux one, and requires following softwares (which will be installed automatically) :
- Git
- Python 3.11 (and specifically 3.11 version)
- OpenSSL
- Visual Studio Build tools
- Windows 10/11 SDK
- Vcredist
- ffmpeg
- ... and all their dependencies.
It's a lot of changes on your operating system, and this could potentially bring unwanted behaviors on your system, depending on which softwares are already installed on it.
-
Download and execute : biniou_netinstall.exe
OR
-
Download and execute : install_win.cmd (right-click on the link and select "Save Target/Link as ..." to download)
All the installation is automated, but Windows UAC will ask you for confirmation for each software installed during the "prerequisites" phase. You can avoid this by running the chosen installer as administrator.
install_win.cmd
Proceed as follow :
- Download and edit install_win.cmd
- Modify
set DEFAULT_BINIOU_DIR="%userprofile%"
toset DEFAULT_BINIOU_DIR="E:\datas\somedir"
(for example) - Only use absolute path (e.g.:
E:\datas\somedir
and not.\datas\somedir
) - Don't add a trailing slash (e.g.:
E:\datas\somedir
and notE:\datas\somedir\
) - Don't add a "biniou" suffix to your path (e.g.:
E:\datas\somedir\biniou
), as the biniou directory will be created by the git clone command - Save and launch install_win.cmd
-
Install Homebrew for your operating system
-
Install required homebrew "bottles" :
brew install git python3 gcc gcc@11 perl make ffmpeg openssl
- Install python virtualenv :
python3 -m pip install virtualenv
- Clone this repository as user :
git clone https://github.com/Woolverine94/biniou.git
- Launch the installer :
cd ./biniou
./install.sh
These instructions assumes that you already have a configured and working docker environment.
- Create the docker image :
docker build -t biniou https://github.com/Woolverine94/biniou.git
or, for CUDA support :
docker build -t biniou https://raw.githubusercontent.com/Woolverine94/biniou/main/CUDA/Dockerfile
- Launch the container :
docker run -it --restart=always -p 7860:7860 \
-v biniou_outputs:/home/biniou/biniou/outputs \
-v biniou_models:/home/biniou/biniou/models \
-v biniou_cache:/home/biniou/.cache/huggingface \
-v biniou_gfpgan:/home/biniou/biniou/gfpgan \
biniou:latest
or, for CUDA support :
docker run -it --gpus all --restart=always -p 7860:7860 \
-v biniou_outputs:/home/biniou/biniou/outputs \
-v biniou_models:/home/biniou/biniou/models \
-v biniou_cache:/home/biniou/.cache/huggingface \
-v biniou_gfpgan:/home/biniou/biniou/gfpgan \
biniou:latest
-
Access the webui by the url :
https://127.0.0.1:7860 or https://127.0.0.1:7860/?__theme=dark for dark theme (recommended)
... or replace 127.0.0.1 by ip of your container
Note : to save storage space, the previous container launch command defines common shared volumes for all biniou containers and ensure that the container auto-restart in case of OOM crash. Remove
--restart
and-v
arguments if you didn't want these behaviors.
biniou is natively cpu-only, to ensure compatibility with a wide range of hardware, but you can easily activate CUDA support through Nvidia CUDA (if you have a functional CUDA 12.1 environment) or AMD ROCm (if you have a functional ROCm 5.6 environment) by selecting the type of optimization to activate (CPU, CUDA or ROCm for Linux), in the WebUI control module.
Currently, all modules except Chatbot, Llava and faceswap modules, could benefits from CUDA optimization.
- Launch by executing from the biniou directory :
- for GNU/Linux :
cd /home/$USER/biniou
./webui.sh
- for Windows :
Double-click webui.cmd in the biniou directory (C:\Users\%username%\biniou\). When asked by the UAC, configure the firewall according to your network type to authorize access to the webui
Note : First start could be very slow on Windows 11 (comparing to others OS).
-
Access the webui by the url :
https://127.0.0.1:7860 or https://127.0.0.1:7860/?__theme=dark for dark theme (recommended)
You can also access biniou from any device (including smartphones) on the same LAN/Wifi network by replacing 127.0.0.1 in the url with biniou host ip address. -
Quit by using the keyboard shortcut CTRL+C in the Terminal
-
Update this application (biniou + python virtual environment) by using the WebUI control updates options.
-
Most frequent cause of crash is not enough memory on the host. Symptom is biniou program closing and returning to/closing the terminal without specific error message. You can use biniou with 8GB RAM, but 16GB at least is recommended to avoid OOM (out of memory) error.
-
biniou use a lot of differents AI models, which requires a lot of space : if you want to use all the modules in biniou, you will need around 200GB of disk space only for the default model of each module. Models are downloaded on the first run of each module or when you select a new model in a module and generate content. Models are stored in the directory /models of the biniou installation. Unused models could be deleted to save some space.
-
... consequently, you will need a fast internet access to download models.
-
A backup of every content generated is available inside the /outputs directory of the biniou folder.
-
biniou natively only rely on CPU for all operations. It use a specific CPU-only version of PyTorch. The result is a better compatibility with a wide range of hardware, but degraded performances. Depending on your hardware, expect slowness. See here for Nvidia CUDA support and AMD ROCm experimental support (GNU/Linux only).
-
Defaults settings are selected to permit generation of contents on low-end computers, with the best ratio performance/quality. If you have a configuration above the minimal settings, you could try using other models, increasing media dimensions or duration, modifying inference parameters or other settings (like token merging for images) to obtain better quality contents.
-
biniou is licensed under GNU GPL3, but each model used in biniou has its own license. Please consult each model license to know what you can and cannot do with the models. For each model, you can find a link to the huggingface page of the model in the "About" section of the associated module.
-
Don't have too much expectations : biniou is in an early stage of development, and most open source software used in it are in development (some are still experimental).
-
Every biniou modules offers 2 accordions elements About and Settings :
- About is a quick help feature that describes the module and gives instructions and tips on how to use it.
- Settings is a panel setting specific to the module that lets you configure the generation parameters.
This application uses the following softwares and technologies :
- ๐ค Huggingface : Diffusers and Transformers libraries and almost all the generative models.
- Gradio : webUI
- llama-cpp-python : python bindings for llama-cpp
- Llava
- BakLLava
- Microsoft GIT : Image2text
- Whisper : speech2text
- nllb translation : language translation
- Stable Diffusion : txt2img, img2img, Image variation, inpaint, ControlNet, Text2Video-Zero, img2vid
- Kandinsky : txt2img
- Latent consistency models : txt2img
- PixArt-Alpha : PixArt-Alpha
- IP-Adapter : IP-Adapter img2img
- Instruct pix2pix : pix2pix
- MagicMix : MagicMix
- Fantasy Studio Paint by Example : paintbyex
- Controlnet Auxiliary models : preview models for ControlNet module
- IP-Adapter FaceID : Adapter model for Photobooth module
- Photomaker Adapter model for Photobooth module
- Insight Face : faceswapping
- Real ESRGAN : upscaler
- GFPGAN : face restoration
- Audiocraft : musicgen, musicgen melody, audiogen
- MusicLDM : MusicLDM
- Harmonai : harmonai
- Bark : text2speech
- Modelscope text-to-video-synthesis : txt2vid
- AnimateLCM : txt2vid
- Open AI Shap-E : txt2shape, img2shape
-
compel : Prompt enhancement for various
StableDiffusionPipeline
-based modules -
tomesd : Token merging for various
StableDiffusionPipeline
-based modules - Python
- PyTorch
- Git
- ffmpeg
... and all their dependencies
GNU General Public License v3.0
GitHub @Woolverine94 ย ยทย
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sematic
Sematic is an open-source ML development platform that allows ML Engineers and Data Scientists to write complex end-to-end pipelines with Python. It can be executed locally, on a cloud VM, or on a Kubernetes cluster. Sematic enables chaining data processing jobs with model training into reproducible pipelines that can be monitored and visualized in a web dashboard. It offers features like easy onboarding, local-to-cloud parity, end-to-end traceability, access to heterogeneous compute resources, and reproducibility.
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langflow
Langflow is an open-source Python-powered visual framework designed for building multi-agent and RAG applications. It is fully customizable, language model agnostic, and vector store agnostic. Users can easily create flows by dragging components onto the canvas, connect them, and export the flow as a JSON file. Langflow also provides a command-line interface (CLI) for easy management and configuration, allowing users to customize the behavior of Langflow for development or specialized deployment scenarios. The tool can be deployed on various platforms such as Google Cloud Platform, Railway, and Render. Contributors are welcome to enhance the project on GitHub by following the contributing guidelines.
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lingo.dev
Replexica AI automates software localization end-to-end, producing authentic translations instantly across 60+ languages. Teams can do localization 100x faster with state-of-the-art quality, reaching more paying customers worldwide. The tool offers a GitHub Action for CI/CD automation and supports various formats like JSON, YAML, CSV, and Markdown. With lightning-fast AI localization, auto-updates, native quality translations, developer-friendly CLI, and scalability for startups and enterprise teams, Replexica is a top choice for efficient and effective software localization.
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AgentGPT
AgentGPT is a platform that allows users to configure and deploy autonomous AI agents. Users can name their own custom AI and set it on any goal. The AI will think of tasks, execute them, and learn from the results to reach the goal. The platform provides a demo experience, automatic setup CLI, and a tech stack including Next.js, FastAPI, Prisma, TailwindCSS, Zod, and more. AgentGPT is designed to help users easily create and deploy AI agents for various tasks.
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llm-awq
AWQ (Activation-aware Weight Quantization) is a tool designed for efficient and accurate low-bit weight quantization (INT3/4) for Large Language Models (LLMs). It supports instruction-tuned models and multi-modal LMs, providing features such as AWQ search for accurate quantization, pre-computed AWQ model zoo for various LLMs, memory-efficient 4-bit linear in PyTorch, and efficient CUDA kernel implementation for fast inference. The tool enables users to run large models on resource-constrained edge platforms, delivering more efficient responses with LLM/VLM chatbots through 4-bit inference.
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fastRAG
fastRAG is a research framework designed to build and explore efficient retrieval-augmented generative models. It incorporates state-of-the-art Large Language Models (LLMs) and Information Retrieval to empower researchers and developers with a comprehensive tool-set for advancing retrieval augmented generation. The framework is optimized for Intel hardware, customizable, and includes key features such as optimized RAG pipelines, efficient components, and RAG-efficient components like ColBERT and Fusion-in-Decoder (FiD). fastRAG supports various unique components and backends for running LLMs, making it a versatile tool for research and development in the field of retrieval-augmented generation.
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multi-agent-orchestrator
Multi-Agent Orchestrator is a flexible and powerful framework for managing multiple AI agents and handling complex conversations. It intelligently routes queries to the most suitable agent based on context and content, supports dual language implementation in Python and TypeScript, offers flexible agent responses, context management across agents, extensible architecture for customization, universal deployment options, and pre-built agents and classifiers. It is suitable for various applications, from simple chatbots to sophisticated AI systems, accommodating diverse requirements and scaling efficiently.
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Awesome-AITools
This repo collects AI-related utilities. ## All Categories * All Categories * ChatGPT and other closed-source LLMs * AI Search engine * Open Source LLMs * GPT/LLMs Applications * LLM training platform * Applications that integrate multiple LLMs * AI Agent * Writing * Programming Development * Translation * AI Conversation or AI Voice Conversation * Image Creation * Speech Recognition * Text To Speech * Voice Processing * AI generated music or sound effects * Speech translation * Video Creation * Video Content Summary * OCR(Optical Character Recognition)
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NSMusicS
NSMusicS is a local music software that is expected to support multiple platforms with AI capabilities and multimodal features. The goal of NSMusicS is to integrate various functions (such as artificial intelligence, streaming, music library management, cross platform, etc.), which can be understood as similar to Navidrome but with more features than Navidrome. It wants to become a plugin integrated application that can almost have all music functions.
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biniou
biniou is a self-hosted webui for various GenAI (generative artificial intelligence) tasks. It allows users to generate multimedia content using AI models and chatbots on their own computer, even without a dedicated GPU. The tool can work offline once deployed and required models are downloaded. It offers a wide range of features for text, image, audio, video, and 3D object generation and modification. Users can easily manage the tool through a control panel within the webui, with support for various operating systems and CUDA optimization. biniou is powered by Huggingface and Gradio, providing a cross-platform solution for AI content generation.
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generative-ai-js
Generative AI JS is a JavaScript library that provides tools for creating generative art and music using artificial intelligence techniques. It allows users to generate unique and creative content by leveraging machine learning models. The library includes functions for generating images, music, and text based on user input and preferences. With Generative AI JS, users can explore the intersection of art and technology, experiment with different creative processes, and create dynamic and interactive content for various applications.
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pictureChange
The 'pictureChange' repository is a plugin that supports image processing using Baidu AI, stable diffusion webui, and suno music composition AI. It also allows for file summarization and image summarization using AI. The plugin supports various stable diffusion models, administrator control over group chat features, concurrent control, and custom templates for image and text generation. It can be deployed on WeChat enterprise accounts, personal accounts, and public accounts.
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Generative-AI-Indepth-Basic-to-Advance
Generative AI Indepth Basic to Advance is a repository focused on providing tutorials and resources related to generative artificial intelligence. The repository covers a wide range of topics from basic concepts to advanced techniques in the field of generative AI. Users can find detailed explanations, code examples, and practical demonstrations to help them understand and implement generative AI algorithms. The goal of this repository is to help beginners get started with generative AI and to provide valuable insights for more experienced practitioners.
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nodetool
NodeTool is a platform designed for AI enthusiasts, developers, and creators, providing a visual interface to access a variety of AI tools and models. It simplifies access to advanced AI technologies, offering resources for content creation, data analysis, automation, and more. With features like a visual editor, seamless integration with leading AI platforms, model manager, and API integration, NodeTool caters to both newcomers and experienced users in the AI field.
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LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
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weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.
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LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
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VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.
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kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
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PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.
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tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.
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spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.
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Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.