Full Citation: “Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).”
Link to Paper: https://arxiv.org/abs/1312.6114
Conference Details: arXiv 2013 \
Full Citation: “Rombach, Robin, et al. “High-resolution image synthesis with latent diffusion models.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.”
Link to Paper: https://arxiv.org/abs/2112.10752
Conference Details: CVPR 2022 \
- Inference, Training에 매우 큰 자원과 시간이 소모되던 기존의 Diffusion 모델의 한계를 극복하고 안정된 공간에서 diffusion process를 학습시키기 때문에 다양한 conditional에 대해 prior를 학습할수 있게함.
- 실험적인 해석이 매우 필요함. 직접적인 코드 해석이 필요함.
Full Citation: “Song, Jiaming, Chenlin Meng, and Stefano Ermon. “Denoising diffusion implicit models.” arXiv preprint arXiv:2010.02502 (2020).”
Link to Paper: https://arxiv.org/abs/2010.02502
Conference Details: arXiv 2020 \
Full Citation: “Ho, Jonathan, and Tim Salimans. “Classifier-free diffusion guidance.” arXiv preprint arXiv:2207.12598 (2022).”
Link to Paper: https://arxiv.org/abs/2207.12598
Conference Details: arXiv 2022 \
Full Citation: “Zhang, Lvmin, Anyi Rao, and Maneesh Agrawala. “Adding conditional control to text-to-image diffusion models.” Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.”
Link to Paper: https://arxiv.org/abs/2302.05543
Conference Details: CVPR 2023 \
Full Citation: “Nichol, Alexander Quinn, and Prafulla Dhariwal. “Improved denoising diffusion probabilistic models.” International Conference on Machine Learning. PMLR, 2021.”
Link to Paper: https://arxiv.org/abs/2102.09672
Conference Details: PMLR 2021 \
Full Citation: “Bhunia, Ankan Kumar, et al. “Person image synthesis via denoising diffusion model.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.”
Link to Paper: https://arxiv.org/abs/2211.12500
Conference Details: CVPR 2023 \
Full Citation: “Ho, Jonathan, Ajay Jain, and Pieter Abbeel. “Denoising diffusion probabilistic models.” Advances in neural information processing systems 33 (2020): 6840-6851.”
Link to Paper: https://arxiv.org/abs/2006.11239
Conference Details: NIPS 2020 \
Full Citation: “Sohl-Dickstein, Jascha, et al. “Deep unsupervised learning using nonequilibrium thermodynamics.” International conference on machine learning. PMLR, 2015.”
Link to Paper: https://arxiv.org/abs/1503.03585
Conference Details: PMLR 2015 \
Full Citation: “Chen, Ricky TQ, et al. “Neural ordinary differential equations.” Advances in neural information processing systems 31 (2018).”
Link to Paper: https://arxiv.org/abs/1806.07366
Conference Details: NIPS 2018 \