Generative diffusion models have advanced image editing with high-quality results and intuitive interfaces such as prompts and semantic drawing. However, these interfaces lack precise control, and the associated methods typically specialize on a single editing task. We introduce a versatile, generative workflow that operates in an intrinsic-image latent space, enabling semantic, local manipulation with pixel precision for a range of editing operations. Building atop the RGB-X diffusion framework, we address key challenges of identity preservation and intrinsic-channel entanglement. By incorporating exact diffusion inversion and disentangled channel manipulation, we enable precise, efficient editing with automatic resolution of global illumination effects -- all without additional data collection or model fine-tuning. We demonstrate state-of-the-art performance across a variety of tasks on complex images, including color and texture adjustments, object insertion and removal, global relighting, and their combinations.
@article{arxiv.2505.08889,
title = {IntrinsicEdit: Precise generative image manipulation in intrinsic space},
author = {Linjie Lyu and Valentin Deschaintre and Yannick Hold-Geoffroy and Miloš Hašan and Jae Shin Yoon and Thomas Leimkühler and Christian Theobalt and Iliyan Georgiev},
journal= {arXiv preprint arXiv:2505.08889},
year = {2025}
}