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

• URL: http://arxiv.org/abs/2308.15854v3
• Date: Sat, 04 Jan 2025 08:24:37 GMT
• Title: Textual and Visual Prompt Fusion for Image Editing via Step-Wise Alignment
• Authors: Zhanbo Feng, Zenan Ling, Xinyu Lu, Ci Gong, Feng Zhou, Wugedele Bao, Jie Li, Fan Yang, Robert C. Qiu,
• Abstract summary: We propose a framework that integrates a fusion of generated visual references and text guidance into the semantic latent space of a textitfrozen pre-trained diffusion model.<n>Using only a tiny neural network, our framework provides control over diverse content and attributes, driven intuitively by the text prompt.
• Score: 10.82748329166797
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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