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RANANO-Recreating Nano Banana Using Diffusion Model
Authors
1 2 3 4 5 Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, Karnataka, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 365-370
- Article Type
- Original Article
- Check for updates
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References
1. J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” Advances in Neural Information Processing Systems (NeurIPS), 2020.
2. Q. Nichol and P. Dhariwal, “Improved Denoising Diffusion Probabilistic Models,” Proceedings of the 38th International Conference on Machine Learning (ICML), vol. 139, pp. 8162–8171, 2021.
3. R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684–10695, 2022.
4. A. Radford et al., “Learning Transferable Visual Models from Natural Language Supervision,” Proceedings of the 38th International Conference on Machine Learning (ICML), vol. 139, pp. 8748–8763, 2021.
5. C. Saharia et al., “Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding,” arXiv preprint arXiv:2205.11487, 2022.
6. L. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
7. W. Zhang, Y. Zuo, and L. Zhang, “Deep Learning Based Image Super-Resolution and Enhancement: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
8. G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, vol. 25, no. 11, 2000.
2. Q. Nichol and P. Dhariwal, “Improved Denoising Diffusion Probabilistic Models,” Proceedings of the 38th International Conference on Machine Learning (ICML), vol. 139, pp. 8162–8171, 2021.
3. R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684–10695, 2022.
4. A. Radford et al., “Learning Transferable Visual Models from Natural Language Supervision,” Proceedings of the 38th International Conference on Machine Learning (ICML), vol. 139, pp. 8748–8763, 2021.
5. C. Saharia et al., “Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding,” arXiv preprint arXiv:2205.11487, 2022.
6. L. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
7. W. Zhang, Y. Zuo, and L. Zhang, “Deep Learning Based Image Super-Resolution and Enhancement: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
8. G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, vol. 25, no. 11, 2000.
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