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Experiments
InfEdit in various complex image editing tasks:
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Comparison
Comparison with inversion-base methods:
Performance in image editing: DDCM matches or exceeds other algorithms, with LCM and UAC bringing further improvement. Notably, it runs about an order of magnitude faster.
Qualitative examples: InfEdit vs prior methods. InfEdit attains editing goals with the best consistency with source images.
Comparison with existing methods:
Qualitative examples: InfEdit vs prior methods. InfEdit attains editing goals with the best consistency with source images.
More ResultsMethod
We make an attempt to eliminate the inversion process and introduce Denoising Diffusion Consistent Model (DDCM), a sampling strategy that enables virtual inversion. DDCM leverages a diffusion process that significantly enhances consistency throughout the image generation phases, ensuring fidelity and speed in transforming and refining visual content.
We also present Unified Attention Control (UAC) for tuning-free image editing through natural language that integrates cross-attention and self-attention control within a unified framework.
BibTeX
@article{xu2023infedit,
title={Inversion-Free Image Editing with Natural Language},
author={Sihan Xu and Yidong Huang and Jiayi Pan and Ziqiao Ma and Joyce Chai},
booktitle={Conference on Computer Vision and Pattern Recognition 2024},
year={2024}
}