Adaptive Classifier-Free Guidance for Robust Image-to-Image Translation

Text-guided image-to-image translation aims to edit a source image according to a textual prompt while preserving its structure. However, existing approaches often rely on a fixed classifier-free guidance scale and a single prompt input, leading to unstable results and a poor balance between semantic fidelity and structural preservation. In this work, we propose a unified framework that improves both thecontrollabilityandstabilityof text-driven diffusion editing without requiring fine-tuning or paired training data. Our method introduces two key components: (1) an adaptive guidance scheduler that dynamically modulates the classifier-free guidance scale over timesteps based on the input image and prompt, and (2) a prompt ensemble mechanism that generates and ranks multiple semantically aligned prompt variants to mitigate prompt sensitivity. Together, these components form a plug-and-play framework that significantly improves editing consistency and visual quality. Extensive experiments on NuScenes, AFHQ, and CelebA demonstrate that our method consistently outperforms existing approaches across diverse scenarios.