Customization Assistant for Text-to-Image Generation
Authors: Yufan Zhou, Ruiyi Zhang, Jiuxiang Gu, Tong Sun

Customizing pre-trained text-to-image generation model has attracted massive research interest recently due to its huge potential in real-world applications. Although existing methods are able to generate creative content for a novel concept contained in single user-input image their capability are still far from perfection. Specifically most existing methods require fine-tuning the generative model on testing images. Some existing methods do not require fine-tuning while their performance are unsatisfactory. Furthermore the interaction between users and models are still limited to directive and descriptive prompts such as instructions and captions. In this work we build a customization assistant based on pre-trained large language model and diffusion model which can not only perform customized generation in a tuning-free manner but also enable more user-friendly interactions: users can chat with the assistant and input either ambiguous text or clear instruction. Specifically we propose a new framework consists of a new model design and a novel training strategy. The resulting assistant can perform customized generation in 2-5 seconds without any test time fine-tuning. Extensive experiments are conducted competitive results have been obtained across different domains illustrating the effectiveness of the proposed method.