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arXiv · 2308.15854

Textual and Visual Prompt Fusion for Image Editing via Step-Wise Alignment

Zhanbo Feng·Zenan Ling·Xinyu Lu·Ci Gong·Feng Zhou·Wugedele Bao·Jie Li·Fan Yang·Robert C. Qiu
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Abstract

The use of denoising diffusion models is becoming increasingly popular in the field of image editing. However, current approaches often rely on either image-guided methods, which provide a visual reference but lack control over semantic consistency, or text-guided methods, which ensure alignment with the text guidance but compromise visual quality. To resolve this issue, we propose a framework that integrates a fusion of generated visual references and text guidance into the semantic latent space of a \textit{frozen} pre-trained diffusion model. Using only a tiny neural network, our framework provides control over diverse content and attributes, driven intuitively by the text prompt. Compared to state-of-the-art methods, the framework generates images of higher quality while providing realistic editing effects across various benchmark datasets.

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Zhanbo Feng, Zenan Ling, Xinyu Lu, Ci Gong, Feng Zhou, Wugedele Bao, Jie Li, Fan Yang, Robert C. Qiu. 2023-08-30. Textual and Visual Prompt Fusion for Image Editing via Step-Wise Alignment. https://arxiv.org/abs/2308.15854

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