Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach

Yufan Zhou, Ruiyi Zhang, Jinhui Xu, Tong Sun

Primary Area: generative models

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Keywords: Generative Model, Diffusion Model

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Abstract: Recent text-to-image generation models have demonstrated impressive capability of generating text-aligned images with high fidelity. However, generating images of novel concepts specified by a reference image remains a challenging task. To address this problem, researchers have been exploring various methods for customizing pre-trained text-to-image generation models. Currently, most existing methods for customizing pre-trained text-to-image generation models involve the use of regularization techniques to prevent over-fitting. Although regularization will ease the challenge of customization and leads to successful content creation with respect to text guidance, it may restrict the model capability, resulting in the loss of detailed information and inferior performance. In this work, we propose ProFusion, a novel framework for customized text-to-image generation, which can tackle the over-fitting problem without the widely used regularization. Specifically, it consists of an encoder network and a novel sampling method. Given a single user-provided image from an arbitrary domain, the proposed framework can customize a pre-trained text-to-image generation model within half a minute. Empirical results demonstrate that our proposed framework outperforms existing methods.

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Submission Number: 6920