Fetching the paper…
Reading the bibliography…
Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images.
Microsoft coco: Common objects in context. In ECCV . Springer, 740–755
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation. In MICCAI . Springer, 234–241
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol. 2021 · 2021
Earlier work this paper cites.
Cogview: Mastering text-to-image generation via transformers
Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al · 2021
Earlier work this paper cites.
Classifier-Free Diffusion Guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications
Jonathan Ho and Tim Salimans. [n. d.] · 2021
Earlier work this paper cites.
Improved denoising diffusion probabilistic models. In ICML . PMLR, 8162–8171
Alexander Quinn Nichol and Prafulla Dhariwal. 2021 · 2021
Earlier work this paper cites.
Benchmark for compositional text-to-image synthesis. In NeurIPS Datasets and Benchmarks Track (Round 1)
Dong Huk Park, Samaneh Azadi, Xihui Liu, Trevor Darrell, and Anna Rohrbach. 2021 · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision. In ICML . PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Zero-shot text-to-image generation. In ICML . PMLR, 8821–8831
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
Denoising Diffusion Implicit Models. In ICLR
Jiaming Song, Chenlin Meng, and Stefano Ermon. 2021 · 2021
Cited alongside, same era.
You only learn one representation: Unified network for multiple tasks
Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao. 2021 · 2021
Cited alongside, same era.
SpaText: Spatio-Textual Representation for Controllable Image Generation
Omri Avrahami, Thomas Hayes, Oran Gafni, Sonal Gupta, Yaniv Taigman, Devi Parikh, Dani Lischinski, Ohad Fried, and Xi Yin. 2022 · 2022
Diffusionclip: Text-guided diffusion models for robust image manipulation. In CVPR . 2426–2435
Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. 2022 · 2022
Later among the works it cites.
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. In ICML . PMLR, 16784–16804
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen. 2022 · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models. In CVPR . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, et al · 2022
Cited alongside, same era.
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang. 2022 · 2022
Cited alongside, same era.
Make-a-scene: Scene-based text-to-image generation with human priors. In ECCV . Springer, 89–106
Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman. 2022 · 2022
Cited alongside, same era.
Prompt-to-prompt image editing with cross attention control
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. 2022 · 2022
Cited alongside, same era.
Imagic: Text-based real image editing with diffusion models
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. 2022 · 2022
Cited alongside, same era.
Pseudo Numerical Methods for Diffusion Models on Manifolds. In ICLR
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao. 2022b
Cited in the paper.
Compositional visual generation with composable diffusion models. In ECCV . Springer, 423–439
Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum. 2022a
Cited in the paper.
Andrey Voynov, Kfir Aberman, and Daniel Cohen-Or. 2022 · 2022
Later among the works it cites.
Pretraining is all you need for image-to-image translation
Tengfei Wang, Ting Zhang, Bo Zhang, Hao Ouyang, Dong Chen, Qifeng Chen, and Fang Wen. 2022 · 2022
Later among the works it cites.
Composer: Creative and controllable image synthesis with composable conditions
Lianghua Huang, Di Chen, Yu Liu, Yujun Shen, Deli Zhao, and Jingren Zhou. 2023 · 2023
Closest in time.
Training-Free Location-Aware Text-to-Image Synthesis
Jiafeng Mao and Xueting Wang. 2023 · 2023
Closest in time.
T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie. 2023 · 2023
Closest in time.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang and Maneesh Agrawala. 2023 · 2023
Closest in time.