PMGAN: pretrained model-based generative adversarial network for text-to-image generation

Author:

Yu Yue,Yang Yue,Xing Jingshuo

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Reference37 articles.

1. Chang, H., Zhang, H., Barber, J., et al.: Muse: Text-to-image generation via masked generative transformers (2023). arXiv:2301.00704

2. Ding, M., Yang, Z., Hong, W., et al.: Cogview: mastering text-to-image generation via transformers. Adv. Neural. Inf. Process. Syst. 34, 19822–19835 (2021)

3. Ding, M., Zheng, W., Hong, W., et al.: Cogview2: faster and better text-to-image generation via hierarchical transformers. Adv. Neural. Inf. Process. Syst. 35, 16890–16902 (2022)

4. Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12873–12883 (2021)

5. Gu, S., Chen, D., Bao, J., et al.: Vector quantized diffusion model for text-to-image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10696–10706 (2022)

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