Language-Free Generative Editing from One Visual Example
Omar Elezabi, Eduard Zamfir, Zongwei Wu, Radu Timofte

TL;DR
This paper introduces Visual Diffusion Conditioning (VDC), a training-free, vision-centric approach for precise image editing using visual examples, overcoming limitations of text-based diffusion models in simple transformations.
Contribution
VDC is a novel, training-free framework that derives visual conditions directly from examples for accurate, language-free image editing, improving over existing text-based methods.
Findings
VDC outperforms text-based editing methods across diverse tasks.
VDC preserves fine details and realism through inversion-correction.
VDC requires no additional training or fine-tuning.
Abstract
Text-guided diffusion models have advanced image editing by enabling intuitive control through language. However, despite their strong capabilities, we surprisingly find that SOTA methods struggle with simple, everyday transformations such as rain or blur. We attribute this limitation to weak and inconsistent textual supervision during training, which leads to poor alignment between language and vision. Existing solutions often rely on extra finetuning or stronger text conditioning, but suffer from high data and computational requirements. We argue that diffusion-based editing capabilities aren't lost but merely hidden from text. The door to cost-efficient visual editing remains open, and the key lies in a vision-centric paradigm that perceives and reasons about visual change as humans do, beyond words. Inspired by this, we introduce Visual Diffusion Conditioning (VDC), a training-free…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Digital Humanities and Scholarship · Cell Image Analysis Techniques
