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Text-to-image generative models have garnered immense attention for their ability to produce high-fidelity images from text prompts.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel · 1965
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ImageNet Large Scale Visual Recognition Challenge
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Very deep convolutional networks for large-scale image recognition
K Simonyan and A Zisserman · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh · 2015
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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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
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Demystifying mmd gans
Mikołaj Bińkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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Transferring gans: generating images from limited data
Yaxing Wang, Chenshen Wu, Luis Herranz, Joost Van de Weijer, Abel Gonzalez-Garcia, and Bogdan Raducanu · 2018
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Attngan: Fine-grained text to image generation with attentional generative adversarial networks
Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He · 2018
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Semantic photo manipulation with a generative image prior
David Bau, Hendrik Strobelt, William Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, and Antonio Torralba · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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FIESTA: Fast IdEntification of state-of-the-art models using adaptive bandit algorithms
Henry Moss, Andrew Moore, David Leslie, and Paul Rayson · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Improved ArtGAN for conditional synthesis of natural image and artwork
Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, and Kiyoshi Tanaka · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment
V. Hosu, H. Lin, T. Sziranyi, and D. Saupe · 2020
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Few-shot adaptation of generative adversarial networks
Esther Robb, Wen-Sheng Chu, Abhishek Kumar, and Jia-Bin Huang · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al · 2020
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Clipscore: A reference-free evaluation metric for image captioning
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi · 2021
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LoRA: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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OpenCLIP, July 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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Deep double descent: Where bigger models and more data hurt
Preetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, and Ilya Sutskever · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Edwin Arkel Rios, Wen-Huang Cheng, and Bo-Cheng Lai · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020
Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, and Isabelle Guyon · 2021
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Hongyi Wang, Saurabh Agarwal, and Dimitris Papailiopoulos · 2021
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Old book illustrations, 2007
gigant · 2023
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eDiff-I: Text-to-image diffusion models with an ensemble of expert denoisers
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DALL-EVAL: Probing the reasoning skills and social biases of text-to-image generative models
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8-bit optimizers via block-wise quantization
Tim Dettmers, Mike Lewis, Sam Shleifer, and Luke Zettlemoyer · 2022
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TISE: Bag of metrics for text-to-image synthesis evaluation
Tan M Dinh, Rang Nguyen, and Binh-Son Hua · 2022
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DreamArtist: Towards controllable one-shot text-to-image generation via contrastive prompt-tuning
Ziyi Dong, Pengxu Wei, and Liang Lin · 2022
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KronA: Parameter efficient tuning with kronecker adapter, 2022
Ali Edalati, Marzieh Tahaei, Ivan Kobyzev, Vahid Partovi Nia, James J. Clark, and Mehdi Rezagholizadeh · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith · 2023
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T2I-CompBench: A comprehensive benchmark for open-world compositional text-to-image generation
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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 · 2023
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Pick-a-pic: An open dataset of user preferences for text-to-image generation
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy · 2023
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Large-scale text-to-image generation models for visual artists’ creative works
Hyung-Kwon Ko, Gwanmo Park, Hyeon Jeon, Jaemin Jo, Juho Kim, and Jinwook Seo · 2023
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Multi-concept customization of text-to-image diffusion
Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu · 2023
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Cones: Concept neurons in diffusion models for customized generation
Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao · 2023
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Subject-diffusion: Open domain personalized text-to-image generation without test-time fine-tuning
Jian Ma, Junhao Liang, Chen Chen, and Haonan Lu · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Toward verifiable and reproducible human evaluation for text-to-image generation
Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, and Shin’ichi Satoh · 2023
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Controlling text-to-image diffusion by orthogonal finetuning
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Instantbooth: Personalized text-to-image generation without test-time finetuning
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Low-rank adaptation for fast text-to-image diffusion fine-tuning
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Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
George Stein, Jesse C Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L Caterini, J Eric T Taylor, and Gabriel Loaiza-Ganem · 2023
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Key-locked rank one editing for text-to-image personalization
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Diffusers: State-of-the-art diffusion models
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p + p+ : Extended textual conditioning in text-to-image generation
Andrey Voynov, Qinghao Chu, Daniel Cohen-Or, and Kfir Aberman · 2023
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Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation
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Pytorch image models
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Convnext v2: Co-designing and scaling convnets with masked autoencoders
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Star Wars dataset, 2019
Joe Young · 2023
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When and why vision-language models behave like bags-of-words, and what to do about it?
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