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Home

biniou screenshot

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 ] • Windows • macOS (experimental) • Docker
Documentation ❓ | Showroom 🖼️

Updates

  • 🆕 2024-05-23 : 🔥 Support for ControlNet tile 🔥 > Adding support for models lllyasviel/control_v11f1e_sd15_tile and ValouF-pimento/ControlNet_SDXL_tile_upscale to module ControlNet.

  • 🆕 2024-05-21 : 🔥 Support for sdxl-flash 🔥 > Adding support for model sd-community/sdxl-flash to modules Stable Diffusion, Img2img, IP-Adapter, Controlnet, Photobooth and Text2Video-Zero. This 6-9 steps model is really qualitative, versatile and 40% faster than standard SDXL models.

  • 🆕 2024-05-19 : 🔥 Adding quantization option to the Chatbot module 🔥 > With this new feature, you can now use any GGUF-compatible model from Huggingface. Paste the repo ID in the model field, paste the name of the quantized gguf file in the quantization field, force a prompt template corresponding to the model and ... enjoy !

  • 🆕 2024-05-18 : 🔥 Support for TCD LoRA models 🔥 > Adding support for LoRA models h1t/TCD-SD15-LoRA and h1t/TCD-SDXL-LoRA to modules Stable Diffusion, Img2img, IP-Adapter, Controlnet and Photobooth. These 2-8 steps LoRA models are similar to Hyper-SD.

  • 🆕 2024-05-17 : 🔥 Adding WebUI translation feature and support for french language 🔥 >

    • You can now use biniou WebUI in French by setting the default language option to "lang_FR" in the Global settings panel (restart biniou and reload the WebUI).
    • Adding support for model NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF to the Chatbot module.

List of archived updates

Menu

• Features
• Prerequisites
• Installation
    GNU/Linux
      OpenSUSE Leap 15.5 / OpenSUSE Tumbleweed
      Rocky 9.3 / Alma 9.3 / CentOS Stream 9 / Fedora 39
      Debian 12 / Ubuntu 22.04.3 / Linux Mint 21.2
    Windows 10 / Windows 11
    macOS Homebrew install
    Dockerfile
• CUDA support
• How To Use
• Good to know
• Credits
• License

Features

Prerequisites

  • Minimal hardware :

    • 64bit CPU
    • 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 bandwith optical fiber internet access

  • Recommended hardware :

    • Massively multicore 64bit CPU
    • 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 bandwith optical fiber internet access

  • Operating system :

    • a 64 bit OS :
      • Debian 12
      • Ubuntu 22.04.3
      • Linux Mint 21.2
      • Rocky 9.3
      • Alma 9.3
      • CentOS Stream 9
      • Fedora 39
      • OpenSUSE Leap 15.5
      • OpenSUSE Tumbleweed
      • Windows 10 22H2
      • Windows 11 22H2
      • macOS ???

Note : biniou support Cuda or ROCm but does not require a dedicated GPU to run. You can install it in a virtual machine.

Installation

GNU/Linux

OpenSUSE Leap 15.5 / OpenSUSE Tumbleweed

One-click installer :
  1. 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)

Rocky 9.3 / Alma 9.3 / CentOS Stream 9 / Fedora 39

One-click installer :
  1. 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)

Debian 12 / Ubuntu 22.04.3 / Linux Mint 21.2+

One-click installer :
  1. 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)
Manual installation :
  1. Install the pre-requisites as root :
apt install git pip python3 python3-venv gcc perl make ffmpeg openssl
  1. Clone this repository as user :
git clone https://github.com/Woolverine94/biniou.git
  1. Launch the installer :
cd ./biniou
./install.sh
  1. (optional, but highly recommended) Install TCMalloc as root to optimize memory management :
apt install google-perftools

Windows 10 / Windows 11

Windows installation has more prerequisites than GNU/Linux one, and requires following softwares (which will be installed automatically) :

  • Git
  • Python
  • 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.

⚠️ You should really make a backup of your system and datas before starting the installation process. ⚠️

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 confirmation for each software installed during the "prerequisites" phase. You can avoid this by running the choosen installer as administrator.

macOS Homebrew install

⚠️ Homebrew install is theoretically compatible with macOS, but has not been tested. Use at your own risk. Any feedback on this procedure through discussions or an issue ticket will be really appreciated. ⚠️

  1. Install Homebrew for your operating system

  2. Install required homebrew "bottles" :

brew install git python3 gcc gcc@11 perl make ffmpeg openssl
  1. Install python virtualenv :
python3 -m pip install virtualenv
  1. Clone this repository as user :
git clone https://github.com/Woolverine94/biniou.git
  1. Launch the installer :
cd ./biniou
./install.sh

Dockerfile

These instructions assumes that you already have a configured and working docker environment.

  1. 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
  1. 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
  1. 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.

CUDA support

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 functionnal CUDA 12.1 environment) or AMD ROCm (if you have a functionnal 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.

How To Use

  1. 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).

  1. 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.

  2. Quit by using the keyboard shortcut CTRL+C in the Terminal

  3. Update this application (biniou + python virtual environment) by using the WebUI control updates options.

Good to know

  • 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, increase media dimensions or duration, modify inference parameters or others 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 experimentals).

  • Every biniou modules offers 2 accordions elements About and Settings :

    • About is a quick help features that describes the module and give instructions and tips on how to use it.
    • Settings is a panel setting specific to the module that let you configure the generation parameters.

Credits

This application uses the following softwares and technologies :

... and all their dependencies

License

GNU General Public License v3.0

GitHub @Woolverine94  · 

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a self-hosted webui for 30+ generative ai

Topics

bark webui gradio whisper insightface huggingface kandinsky real-esrgan gfpgan stable-diffusion diffusers generative-ai controlnet musicgen animatediff audiogen llama-cpp-python ip-adapter stable-video-diffusion photomaker

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