This page shows the most relevant public items for Generative AI, 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 • Oct 19, 2023 • OpenAI • James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, Wesam Manassra, Prafulla Dhariwal, Casey Chu, Yunxing Jiao, Aditya Ramesh
Current text-to-image models often struggle to faithfully follow detailed or complex prompts, frequently ignoring specific attributes or object relationships. We propose that this issue stems from ...
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 15, 2024 • OpenAI Technical Report • Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, Aditya Ramesh
We explore the large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of highly variable durations, resolutio...
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 • Feb 24, 2021 • arXiv • Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. We describe a simple approach for this task based on a transformer that au...
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 • Sep 5, 2024 • bioRxiv • Google DeepMind
Designing novel proteins that bind tightly and specifically to disease-relevant targets is a fundamental challenge in drug discovery and synthetic biology. We introduce AlphaProteo, an integrated m...
Paper • Feb 25, 2026 • bioRxiv • Samuel H. A. von der Dunk, Liliana M. Dávalos, Ard A. Louis
Directed evolution in the laboratory is constrained by physical limits on library size and mutation rates. We present an entirely *in silico* framework utilizing Generative Agents that simulate the...
Paper • Apr 30, 2020 • arXiv • Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever
We introduce Jukebox, a generative model that produces high-fidelity, highly diverse music with singing in the raw audio domain. We model music as a sequence of discrete tokens by using a multi-sca...