#WebUI

Logo of rvc-webui
rvc-webui
The RVC-WebUI project provides an accessible voice conversion interface for Windows, Linux, and Mac, requiring environment setup with Windows 10, Python 3.10.9, and torch 2.0.0+cu118. Detailed troubleshooting instructions for Microsoft Visual C++ 14.0 support smooth installation. This collaborative project builds on existing innovations to enhance voice conversion functionalities with straightforward setup guides.
Logo of stable-diffusion-webui-ux
stable-diffusion-webui-ux
The interface enhances interactions with Stable Diffusion, providing customization and speed using Gradio. Features include mobile responsiveness, a micro-template engine, and console logs for debugging. Compatible with Gradio 3 and 4, it offers advanced usability with toggle input and slider options, as well as seamless extension integration like Deforum and Aspect-Ratio-Helper. Future updates will introduce a theme editor and workspace management for tailored workflows, offering optimized styles and reducing redundancies.
Logo of LLM-Kit
LLM-Kit
This open-source project provides a versatile WebUI toolkit designed to manage language model workflows effortlessly. Users can create custom models and applications without coding, in environments like Python and CUDA. The toolkit features robust modules, including APIs for prominent language models such as OpenAI and Baidu's Wenxin Yiyan. It supports functionalities including chat, image generation, dataset processing, and embedding models. Key features include role-play settings with memory and background libraries, and compatibility with large-scale models like ChatGLM and Phoenix-Chat. Operating under the AGPL-3.0 license, it encourages community involvement and shared development.
Logo of lora-scripts
lora-scripts
Lora-scripts provides a streamlined GUI and script solution for LoRA and Dreambooth training, featuring a comprehensive one-click setup environment. It seamlessly integrates with kohya-ss/sd-scripts and incorporates advanced tools such as a web-based training interface, Tensorboard integration, and diverse configuration options for both Windows and Linux systems. Developers can utilize easy setup scripts and benefit from the WD 1.4 Tagger and Tag Editor, making it ideal for efficient Stable Diffusion model training.