You're currently viewing an old version of this dataset. To see the current version, click here.

Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning

Text-to-image generative models have recently attracted considerable interest, enabling the synthesis of high-quality images from textual prompts. However, these models often lack the capability to generate specific subjects from given reference images or to synthesize novel renditions under varying conditions.

Data and Resources

Cite this as

Yanting Miao, William Loh, Suraj Kothawade, Pascal Poupart, Abdullah Rashwan, Yeqing Li (2024). Dataset: Subject-driven Text-to-Image Generation via Preference-based Reinforcement Learning. https://doi.org/10.57702/b1svg0nj

DOI retrieved: December 2, 2024

Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Author Yanting Miao
More Authors
William Loh
Suraj Kothawade
Pascal Poupart
Abdullah Rashwan
Yeqing Li