Specify and Edit: Overcoming Ambiguity in Text-Based Image Editing


Ekaterina Iakovleva (Télécom Paris), Fabio Pizzati (MBZUAI), Philip Torr (University of Oxford), Stéphane Lathuilière (INRIA)
The 35th British Machine Vision Conference
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Abstract

Text-based editing diffusion models exhibit limited performance when the user's input instruction is ambiguous. To solve this problem, we propose Specify ANd Edit (SANE), a zero-shot inference pipeline for diffusion-based editing systems. We use a large language model (LLM) to decompose the input instruction into specific instructions, i.e. well-defined interventions to apply to the input image to satisfy the user's request. We benefit from the LLM-derived instructions along the original one, thanks to a novel denoising guidance strategy specifically designed for the task. Our experiments with three baselines and on two datasets demonstrate the benefits of SANE in all setups. Moreover, our pipeline improves the interpretability of editing models, and boosts the output diversity. Our code is publicly available at https://github.com/fabvio/SANE.

Citation

@inproceedings{Iakovleva_2025_BMVC,
author    = {Ekaterina Iakovleva and Fabio Pizzati and Philip Torr and Stéphane Lathuilière},
title     = {Specify and Edit: Overcoming Ambiguity in Text-Based Image Editing},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_766/paper.pdf}
}


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