Reproduce “FineEdit: Fine-Grained Image Edit with Bounding Box Guidance” on OpenResearch

About this paper

Haohang Xu, Lin Liu, Zhibo Zhang, Rong Cong, Xiaopeng Zhang, Qi Tian

Huawei Inc. researchers developed FineEdit, a framework that employs multi-level bounding box guidance and a decoupled reinforcement learning strategy to achieve precise, fine-grained image editing while preserving background consistency. The model outperforms existing state-of-the-art methods across various benchmarks, demonstrating superior instruction compliance, localization accuracy, and background preservation.

Key insights

• Integrating spatial conditions at multiple levels (early and deep fusion) within the diffusion model significantly enhances localization precision and accelerates convergence.
• Decoupling reward signals for foreground edits and background preservation in reinforcement learning leads to more accurate and artifact-free localized modifications.
• A meticulously curated, large-scale dataset with fine-grained annotations and a robust evaluation benchmark are crucial for developing and validating high-fidelity, controllable image editing models.

Summary by alphaXiv.