1. Borji, A. (2022). Generated faces in the wild: Quantitative comparison of stable diffusion, midjourney and DALL-E 2. arXiv preprint http://arxiv.org/abs/2210.00586.
2. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., & Agarwal, S. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–901.
3. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Nips, 33, 6840–6851.
4. Ho, J., Salimans, T., Gritsenko, A.A., Chan, W., Norouzi, M., & Fleet, D.J. (2022). Video diffusion models. ICLR workshop on deep generative models for highly structured data.
5. Kawar, B., Zada, S., Lang, O., Tov O, Chang, H., Dekel, T., Mosseri, I., & Irani, M. (2022). Imagic: Text-based real image editing with diffusion models. arXiv preprint http://arxiv.org/abs/2210.09276.