Skip to yearly menu bar Skip to main content


Customization Assistant for Text-to-Image Generation

Yufan Zhou · Ruiyi Zhang · Jiuxiang Gu · Tong Sun

Arch 4A-E Poster #425
[ ]
Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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 does 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 built 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 within few seconds, 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 with self-distillation. The resulting assistant can perform customized generation in2-5 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.

Live content is unavailable. Log in and register to view live content