CycleNet: Rethinking Cycle Consistent in Text‑Guided Diffusion for Image Manipulation

Abstract

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. We validate CycleNet on unpaired I2I tasks of different granularities. Besides the scene and object level translation, we additionally contribute a multi-domain I2I translation dataset with object state changes. Our empirical studies show that CycleNet is superior in translation consistency and quality, and can generate high-quality images for out-of-domain distributions with a simple change of the textual prompt. CycleNet is a practical framework, which is robust even with very limited training data (around 2k) and requires minimal computational resources (1 GPU) to train.