TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models
<|reference_start|>TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models: Diffusion models have opened the path to a wide range of text-based image editing frameworks. However, these typically build on the multi-step nature of the diffusion backwards process, and adapting them to distilled, fast-sampling methods has proven surprisingly challenging. Here, we focus on a popular line of text-based editing frameworks - the ``edit-friendly'' DDPM-noise inversion approach. We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength. We trace the artifacts to mismatched noise statistics between inverted noises and the expected noise schedule, and suggest a shifted noise schedule which corrects for this offset. To increase editing strength, we propose a pseudo-guidance approach that efficiently increases the magnitude of edits without introducing new artifacts. All in all, our method enables text-based image editing with as few as three diffusion steps, while providing novel insights into the mechanisms behind popular text-based editing approaches.<|reference_end|>
@article{deutch2024turboedit:,
    title={TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models},
    author={Gilad Deutch, Rinon Gal, Daniel Garibi, Or Patashnik, Daniel Cohen-Or},
    journal={arXiv preprint arXiv:2408.00735},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00735},
    primaryClass={cs.CV cs.GR}
}