Repository History

4 repositories tagged with Image Generation

Topic: Image Generation
Open-Higgsfield-AI: Free, Self-Hosted AI Image Generation & Cinema Studio

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.

Analyzed Jun 15, 2026
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StreamDiffusion: Real-Time Interactive Generation with Diffusion Pipelines

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.

Analyzed Dec 13, 2025
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dom-to-image: Convert DOM Nodes to Images with JavaScript and HTML5 Canvas

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.

Analyzed Oct 12, 2025
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Leffa: Controllable Person Image Generation with Flow Fields in Attention

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.

Analyzed Oct 12, 2025
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