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.

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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

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