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Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing evaluation metrics.
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Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
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Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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John David Pressman, Katherine Crowson, and Simulacra Captions Contributors · 2022
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Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy · 2023
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Aligning text-to-image models using human feedback
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Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images, 2023
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StyleGAN-T: Unlocking the power of gans for fast large-scale text-to-image synthesis
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Better Aligning Text-to-Image Models with Human Preference, 2023
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ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation, 2023
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A Perceptual Quality Assessment Exploration for AIGC Images, 2023
Zicheng Zhang, Chunyi Li, Wei Sun, Xiaohong Liu, Xiongkuo Min, and Guangtao Zhai · 2023
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