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Recent image harmonization methods have demonstrated promising results.However, due to their heavy reliance on a large number of composite images,these works are expensive in the training phase and often fail to generalize tounseen images. In this paper, we draw lessons from human behavior and come upwith a zero-shot image harmonization method. Specifically, in the harmonizationprocess, a human mainly utilizes his long-term prior on harmonious images andmakes a composite image close to that prior. To imitate that, we resort topretrained generative models for the prior of natural images. For the guidanceof the harmonization direction, we propose an Attention-Constraint Text whichis optimized to well illustrate the image environments. Some further designsare introduced for preserving the foreground content structure. The resultingframework, highly consistent with human behavior, can achieve harmoniousresults without burdensome training. Extensive experiments have demonstratedthe effectiveness of our approach, and we have also explored some interestingapplications.

Zero-Shot Image Harmonization with Generative Model Prior