IA-T2I: Internet-Augmented Text-to-Image Generation
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.
682e9d84a0db46260ccc15e1
Chuanhao Li
claimed_verified
2025-06-23T08:32:40.931Z
674ea59a8f2e7614a6c72f26
/avatars/86fd4c6d7d33435de49b35659bf65265.svg
Chuanhao
user
ChuanhaoLi
682e9d84a0db46260ccc15e2
Jianwen Sun
682e9d84a0db46260ccc15e3
Yukang Feng
682e9d84a0db46260ccc15e4
Mingliang Zhai
682e9d84a0db46260ccc15e5
Yifan Chang
682e9d84a0db46260ccc15e6
Kaipeng Zhang
claimed_verified
2025-06-02T07:49:38.607Z
65f1713552c38a91e0a445e8
/avatars/47ab3ada51c9b9976ac1cd0c4301c373.svg
kaipeng
user
kpzhang996
IA-T2I: Internet-Augmented Text-to-Image Generation
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.
An Internet-Augmented text-to-image generation framework improves uncertain text prompt handling by integrating reference images, enhancing image quality and fidelity.
text-to-image generation IA-T2I framework reference images active retrieval module hierarchical image selection module self-reflection mechanism Img-Ref-T2I dataset GPT-4o