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Customizing pre-trained text-to-image generation model has attracted massiveresearch interest recently, due to its huge potential in real-worldapplications. Although existing methods are able to generate creative contentfor a novel concept contained in single user-input image, their capability arestill far from perfection. Specifically, most existing methods requirefine-tuning the generative model on testing images. Some existing methods donot require fine-tuning, while their performance are unsatisfactory.Furthermore, the interaction between users and models are still limited todirective and descriptive prompts such as instructions and captions. In thiswork, we build a customization assistant based on pre-trained large languagemodel and diffusion model, which can not only perform customized generation ina tuning-free manner, but also enable more user-friendly interactions: userscan chat with the assistant and input either ambiguous text or clearinstruction. Specifically, we propose a new framework consists of a new modeldesign and a novel training strategy. The resulting assistant can performcustomized generation in 2-5 seconds without any test time fine-tuning.Extensive experiments are conducted, competitive results have been obtainedacross different domains, illustrating the effectiveness of the proposedmethod.

Customization Assistant for Text-to-image Generation