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FLUX.1 repository contains inference code and tooling for the FLUX.1 text-to-image diffusion models, enabling developers and researchers to generate and edit images from natural-language prompts using open-weight versions of the model on their own hardware or within custom applications. The project is part of a larger family of FLUX models developed by Black Forest Labs, designed to produce high-quality, detailed visuals from text descriptions with competitive prompt adherence and artistic...

Boogu-Image is an open-source image generation and editing model family focused on unified multimodal understanding and generation. It includes Base, Turbo, Edit, and Edit-Turbo variants for different speed and quality needs. The project supports text-to-image generation, image-to-image editing, fast distilled inference, and Chinese-English text rendering. It is designed to handle photography, posters, products, stylized art, dense text layouts, and precise in-image text edits. The...

Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before...