Zero-shot Inversion Process for Image Attribute Editing with Diffusion Models

• URL: http://arxiv.org/abs/2308.15854v2
• Date: Wed, 11 Oct 2023 02:34:23 GMT
• Title: Zero-shot Inversion Process for Image Attribute Editing with Diffusion Models
• Authors: Zhanbo Feng, Zenan Ling, Ci Gong, Feng Zhou, Jie Li, Robert C. Qiu
• Abstract summary: We propose a framework that injects a fusion of generated visual reference and text guidance into the semantic latent space of a pre-trained diffusion model. Only using a tiny neural network, the proposed ZIP produces diverse content and attributes under the intuitive control of the text prompt. Compared to state-of-the-art methods, ZIP produces images of equivalent quality while providing a realistic editing effect.
• Score: 9.924851219904843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Denoising diffusion models have shown outstanding performance in image editing. Existing works tend to use either image-guided methods, which provide a visual reference but lack control over semantic coherence, or text-guided methods, which ensure faithfulness to text guidance but lack visual quality. To address the problem, we propose the Zero-shot Inversion Process (ZIP), a framework that injects a fusion of generated visual reference and text guidance into the semantic latent space of a \textit{frozen} pre-trained diffusion model. Only using a tiny neural network, the proposed ZIP produces diverse content and attributes under the intuitive control of the text prompt. Moreover, ZIP shows remarkable robustness for both in-domain and out-of-domain attribute manipulation on real images. We perform detailed experiments on various benchmark datasets. Compared to state-of-the-art methods, ZIP produces images of equivalent quality while providing a realistic editing effect.

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