Related papers: VersaT2I: Improving Text-to-Image Models with Versatile Reward

VersaT2I: Improving Text-to-Image Models with Versatile Reward

URL: http://arxiv.org/abs/2403.18493v1
Date: Wed, 27 Mar 2024 12:08:41 GMT
Title: VersaT2I: Improving Text-to-Image Models with Versatile Reward
Authors: Jianshu Guo, Wenhao Chai, Jie Deng, Hsiang-Wei Huang, Tian Ye, Yichen Xu, Jiawei Zhang, Jenq-Neng Hwang, Gaoang Wang,
Abstract summary: VersaT2I is a versatile training framework that can boost the performance of any text-to-image (T2I) model. We decompose the quality of the image into several aspects such as aesthetics, text-image alignment, geometry, low-level quality, etc.
Score: 32.30564849001593
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recent text-to-image (T2I) models have benefited from large-scale and high-quality data, demonstrating impressive performance. However, these T2I models still struggle to produce images that are aesthetically pleasing, geometrically accurate, faithful to text, and of good low-level quality. We present VersaT2I, a versatile training framework that can boost the performance with multiple rewards of any T2I model. We decompose the quality of the image into several aspects such as aesthetics, text-image alignment, geometry, low-level quality, etc. Then, for every quality aspect, we select high-quality images in this aspect generated by the model as the training set to finetune the T2I model using the Low-Rank Adaptation (LoRA). Furthermore, we introduce a gating function to combine multiple quality aspects, which can avoid conflicts between different quality aspects. Our method is easy to extend and does not require any manual annotation, reinforcement learning, or model architecture changes. Extensive experiments demonstrate that VersaT2I outperforms the baseline methods across various quality criteria.

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