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Towards Enhanced Image Inpainting:
Mitigating Unwanted Object Insertion and Preserving Color Consistency
Fudan University
CVPR 2025 (Highlight)
Overview
This repo contains the proposed ASUKA model in our paper "Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency".
ASUKA solves two issues existed in current diffusion and rectified flow inpainting models: Unwanted object insertion, where randomly elements that are not aligned with the unmasked region are generated; Color-inconsistency, the color shift of the generated masked region, causing smear-like traces. ASUKA proposes a post-training procedure for these models, significantly mitigates object hallucination and improves color consistency of inpainted results.
We released ASUKA for FLUX.1-Fill-dev, denoted as ASUKA(FLUX.1-Fill). The code and dataset can be found at here. We are actively working to improve both our model and evaluation dataset. If you encounter failure cases with ASUKA (FLUX.1-Fill) or have challenging examples in image inpainting, we would love to hear from you. Please email them to [email protected]. We truly appreciate your contributions!
Modifications to FLUX
- The text conditional input of CLIP and T5 is replaced by the MAE condition to mitigate object hallucination.
- The decoder is replaced by our conditional decoder to enhance color consistency.
BibTeX
If you find our repo helpful, please consider cite our paper :)
@inproceedings{wang2025towards,
title={Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency.},
author={Wang, Yikai and Cao, Chenjie and Yu, Junqiu and Fan, Ke and Xue, Xiangyang and Fu, Yanwei},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
year={2025}
}