Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling
This addresses a key challenge in image editing for users of diffusion models, though it appears incremental as it builds on existing methods.
The paper tackles the trade-off between editability and faithfulness in text-guided diffusion-based image editing by proposing Faithfulness Guidance and Scheduling (FGS), which improves faithfulness with minimal impact on editability, achieving superior results in experiments.
Text-guided diffusion models have become essential for high-quality image synthesis, enabling dynamic image editing. In image editing, two crucial aspects are editability, which determines the extent of modification, and faithfulness, which reflects how well unaltered elements are preserved. However, achieving optimal results is challenging because of the inherent trade-off between editability and faithfulness. To address this, we propose Faithfulness Guidance and Scheduling (FGS), which enhances faithfulness with minimal impact on editability. FGS incorporates faithfulness guidance to strengthen the preservation of input image information and introduces a scheduling strategy to resolve misalignment between editability and faithfulness. Experimental results demonstrate that FGS achieves superior faithfulness while maintaining editability. Moreover, its compatibility with various editing methods enables precise, high-quality image edits across diverse tasks.