TIPO: Text to Image with Text Presampling for Prompt Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912886165602304 |
|---|---|
| author | Yeh, Shih-Ying Li, Yi Park, Sang-Hyun Oh, Giyeong Wang, Xuehai Song, Min Yu, Youngjae Lai, Shang-Hong |
| author_facet | Yeh, Shih-Ying Li, Yi Park, Sang-Hyun Oh, Giyeong Wang, Xuehai Song, Min Yu, Youngjae Lai, Shang-Hong |
| contents | TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these prompts into richer and more detailed versions. Conceptually, TIPO samples refined prompts from a targeted sub-distribution within the broader semantic space, preserving the original intent while significantly improving visual quality, coherence, and detail. Unlike resource-intensive methods based on large language models (LLMs) or reinforcement learning (RL), TIPO offers strong computational efficiency and scalability, opening new possibilities for effective automated prompt engineering in T2I tasks. Extensive experiments across multiple domains demonstrate that TIPO achieves stronger text alignment, reduced visual artifacts, and consistently higher human preference rates, while maintaining competitive aesthetic quality. These results highlight the effectiveness of distribution-aligned prompt engineering and point toward broader opportunities for scalable, automated refinement in text-to-image generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08127 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TIPO: Text to Image with Text Presampling for Prompt Optimization Yeh, Shih-Ying Li, Yi Park, Sang-Hyun Oh, Giyeong Wang, Xuehai Song, Min Yu, Youngjae Lai, Shang-Hong Computer Vision and Pattern Recognition TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these prompts into richer and more detailed versions. Conceptually, TIPO samples refined prompts from a targeted sub-distribution within the broader semantic space, preserving the original intent while significantly improving visual quality, coherence, and detail. Unlike resource-intensive methods based on large language models (LLMs) or reinforcement learning (RL), TIPO offers strong computational efficiency and scalability, opening new possibilities for effective automated prompt engineering in T2I tasks. Extensive experiments across multiple domains demonstrate that TIPO achieves stronger text alignment, reduced visual artifacts, and consistently higher human preference rates, while maintaining competitive aesthetic quality. These results highlight the effectiveness of distribution-aligned prompt engineering and point toward broader opportunities for scalable, automated refinement in text-to-image generation. |
| title | TIPO: Text to Image with Text Presampling for Prompt Optimization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.08127 |