This page shows the most relevant public items for Diffusion Models, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • May 11, 2021 • arXiv • Prafulla Dhariwal, Alex Nichol
We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better archi...
Paper • Dec 20, 2021 • arXiv • Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen
Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guiding technique to trade off diversity for fidelity. We explore diffusion models...
Paper • Mar 2, 2023 • arXiv • Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever
Diffusion models have achieved significant success in image, audio, and video generation, but they depend on an iterative generation process that causes slow sampling and precludes real-time applic...
Paper • Feb 18, 2021 • arXiv • Alex Nichol, Prafulla Dhariwal
Denoising diffusion probabilistic models (DDPMs) have recently demonstrated high-quality image generation, but they suffer from notoriously slow sampling times and sub-optimal log-likelihoods. We p...
Paper • Apr 13, 2022 • arXiv • Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a tw...
Paper • Dec 16, 2022 • arXiv • Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen
While text-to-image generation has witnessed rapid progress, text-to-3D synthesis remains challenging due to the lack of massive 3D datasets and the complexity of 3D representations. We introduce P...