OmniRefiner: Reinforcement-Guided Local Diffusion Refinement
Reference-guided image generation has progressed rapidly, yet current diffusion models still struggle to preserve fine-grained visual details when refining a generated image using a reference. This limitation arises because VAE-based latent compression inherently discards subtle texture information, causing identity- and attribute-specific cues to vanish. Moreover, post-editing approaches that amplify local details based on existing methods often produce results inconsistent with the original image in terms of lighting, texture, or shape. To address this, we introduce , a detail-aware refinement framework that performs two consecutive stages of reference-driven correction to enhance pixel-level consistency. We first adapt a single-image diffusion editor by fine-tuning it to jointly ingest the draft image and the reference image, enabling globally coherent refinement while maintaining structural fidelity. We then apply reinforcement learning to further strengthen localized editing capability, explicitly optimizing for detail accuracy and semantic consistency. Extensive experiments demonstrate that  significantly improves reference alignment and fine-grained detail preservation, producing faithful and visually coherent edits that surpass both open-source and commercial models on challenging reference-guided restoration benchmarks.
692d4b364397b1ec214f6987
Yaoli Liu
692d4b364397b1ec214f6988
Ziheng Ouyang
claimed_verified
2025-12-03T09:18:45.974Z
673c566071f89d461772aa36
/avatars/aff6378a57272e1c34bfcd827f39211a.svg
Ouyang Ziheng
ziheng1234
user
ziheng1234
692d4b364397b1ec214f6989
Shengtao Lou
692d4b364397b1ec214f698a
Yiren Song
OmniRefiner: Reinforcement-Guided Local Diffusion Refinement
Reference-guided image generation has progressed rapidly, yet current diffusion models still struggle to preserve fine-grained visual details when refining a generated image using a reference. This limitation arises because VAE-based latent compression inherently discards subtle texture information, causing identity- and attribute-specific cues to vanish. Moreover, post-editing approaches that amplify local details based on existing methods often produce results inconsistent with the original image in terms of lighting, texture, or shape. To address this, we introduce , a detail-aware refinement framework that performs two consecutive stages of reference-driven correction to enhance pixel-level consistency. We first adapt a single-image diffusion editor by fine-tuning it to jointly ingest the draft image and the reference image, enabling globally coherent refinement while maintaining structural fidelity. We then apply reinforcement learning to further strengthen localized editing capability, explicitly optimizing for detail accuracy and semantic consistency. Extensive experiments demonstrate that  significantly improves reference alignment and fine-grained detail preservation, producing faithful and visually coherent edits that surpass both open-source and commercial models on challenging reference-guided restoration benchmarks.
A detail-aware refinement framework using single-image diffusion and reinforcement learning enhances reference-guided image generation, improving detail preservation and consistency.
diffusion models VAE latent compression reference-guided image generation single-image diffusion editor reinforcement learning pixel-level consistency reference alignment fine-grained detail preservation