An Internet-Augmented text-to-image generation framework improves uncertain text prompt handling by integrating reference images, enhancing image quality and fidelity.
Current text-to-image (T2I) generation models achieve promising results, but
they fail on the scenarios where the knowledge implied in the text prompt is
uncertain. For example, a T2I model released in February would struggle to
generate a suitable poster for a movie premiering in April, because the
character designs and styles are uncertain to the model. To solve this problem,
we propose an Internet-Augmented text-to-image generation (IA-T2I) framework to
compel T2I models clear about such uncertain knowledge by providing them with
reference images. Specifically, an active retrieval module is designed to
determine whether a reference image is needed based on the given text prompt; a
hierarchical image selection module is introduced to find the most suitable
image returned by an image search engine to enhance the T2I model; a
self-reflection mechanism is presented to continuously evaluate and refine the
generated image to ensure faithful alignment with the text prompt. To evaluate
the proposed framework’s performance, we collect a dataset named Img-Ref-T2I,
where text prompts include three types of uncertain knowledge: (1) known but
rare. (2) unknown. (3) ambiguous. Moreover, we carefully craft a complex prompt
to guide GPT-4o in making preference evaluation, which has been shown to have
an evaluation accuracy similar to that of human preference evaluation.
Experimental results demonstrate the effectiveness of our framework,
outperforming GPT-4o by about 30% in human evaluation.
