Скачать или смотреть FireRed-Image-Edit-1.0 Technical Report

FireRed-Image-Edit represents a significant advancement in instruction-based image editing, utilizing a diffusion transformer architecture optimized through rigorous data engineering and a sophisticated multi-stage training pipeline. The researchers constructed an extensive training corpus initially containing 1.6 billion samples, which was meticulously filtered and balanced to retain over 100 million high-quality text-to-image and image-editing pairs, ensuring precise semantic alignment and broad coverage. To address specific challenges in generative editing, the framework incorporates innovative efficiency optimizations such as a Multi-Condition Aware Bucket Sampler for handling variable resolutions and input counts, alongside a specialized Consistency Loss designed to preserve subject identity during complex modifications. Furthermore, the authors established REDEdit-Bench, a comprehensive benchmark spanning 15 distinct editing categories, to rigorously evaluate the model against both open-source and proprietary competitors, ultimately demonstrating state-of-the-art performance in prompt compliance and visual preservation.

https://arxiv.org/pdf/2602.13344