@inproceedings{
zhao2024evolvedirector,
title={EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models},
author={Rui Zhao and Hangjie Yuan and Yujie Wei and Shiwei Zhang and Yuchao Gu and Lingmin Ran and Xiang Wang and Jay Zhangjie Wu and David Junhao Zhang and Yingya Zhang and Mike Zheng Shou},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=28bFUt6rUY}
}
Recent advancements in generation models have showcased remarkable capabilities in generating fantastic content. However, most of them are trained on proprietary high-quality data, and some models withhold their parameters and only provide accessible application programming interfaces (APIs), limiting their benefits for downstream tasks. To explore the feasibility of training a text-to-image generation model comparable to advanced models using publicly available resources, we introduce EvolveDirector. This framework interacts with advanced models through their public APIs to obtain text-image data pairs to train a base model. Our experiments with extensive data indicate that the model trained on generated data of the advanced model can approximate its generation capability. However, it requires large-scale samples of 10 million or more. This incurs significant expenses in time, computational resources, and especially the costs associated with calling fee-based APIs. To address this problem, we leverage pre-trained large vision-language models (VLMs) to guide the evolution of the base model. VLM continuously evaluates the base model during training and dynamically updates and refines the training dataset by the discrimination, expansion, deletion, and mutation operations. Experimental results show that this paradigm significantly reduces the required data volume. Furthermore, when approaching multiple advanced models, EvolveDirector can select the best samples generated by them to learn powerful and balanced abilities. The final trained model Edgen is demonstrated to outperform these advanced models. The framework EvolveDiretor and the trained model Edgen will be fully open-sourced to benefit the downstream tasks.
EvolveDirector: Approaching Advanced Text-to-Image Generation with Large Vision-Language Models
Rui Zhao Hangjie Yuan Yujie Wei Shiwei Zhang Yuchao Gu Lingmin Ran Xiang Wang Jay Zhangjie Wu David Junhao Zhang Yingya Zhang Mike Zheng Shou