Repository History
5 repositories tagged with Image Generation
OGAD: Private, On-Device AI with an OpenAI-Compatible Local Gateway
OGAD (Off Grid AI Desktop) is an open-source, AGPL-licensed application for private, on-device AI. It enables users to run various open models, including text, vision, image, and voice, entirely locally through a single OpenAI-compatible gateway. This ensures complete data privacy with no cloud dependencies, accounts, or API keys.
Open-Higgsfield-AI: Free, Self-Hosted AI Image Generation & Cinema Studio
Open-Higgsfield-AI offers an open-source, self-hosted alternative for AI image generation and a cinema studio. It provides access to over 20 models, including Flux, SDXL, Midjourney, and Ideogram, allowing users to create stunning visuals and cinematic content. This MIT-licensed project is fully customizable and designed for local operation.
StreamDiffusion: Real-Time Interactive Generation with Diffusion Pipelines
StreamDiffusion is an innovative diffusion pipeline designed for real-time interactive generation, significantly enhancing the performance of current diffusion-based image generation techniques. It offers a pipeline-level solution to achieve high-speed image and text-to-image generation, making interactive AI experiences more accessible. This project introduces several key features to optimize computational efficiency and GPU utilization.
dom-to-image: Convert DOM Nodes to Images with JavaScript and HTML5 Canvas
dom-to-image is a JavaScript library designed to transform any DOM node into a vector (SVG) or raster (PNG, JPEG) image. It leverages HTML5 canvas to provide a flexible solution for capturing web content. This tool is ideal for developers needing to generate visual representations of specific UI elements.
Leffa: Controllable Person Image Generation with Flow Fields in Attention
Leffa is a unified framework for controllable person image generation, enabling precise manipulation of appearance through virtual try-on and pose via pose transfer. This project addresses the common issue of fine-grained textural detail distortion by learning flow fields in attention, guiding target queries to correct reference keys. It achieves state-of-the-art performance, maintaining high image quality while significantly reducing detail distortion.