CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation

Diffusion models (DMs) have enabled breakthroughs in image synthesis tasks but lack an intuitive interface for consistent image-to-image (I2I) translation. Various methods have been explored to address this issue, including mask-based methods, attention-based methods, and image-conditioning. However, it remains a critical challenge to enable unpaired I2I translation with pre-trained DMs while maintaining satisfying consistency. This paper introduces CycleNet, a novel but simple method that incorporates cycle consistency into DMs to regularize image manipulation.