This content originally appeared on HackerNoon and was authored by Auto Encoder: How to Ignore the Signal Noise
:::info Authors:
(1) Tony Lee, Stanford with Equal contribution;
(2) Michihiro Yasunaga, Stanford with Equal contribution;
(3) Chenlin Meng, Stanford with Equal contribution;
(4) Yifan Mai, Stanford;
(5) Joon Sung Park, Stanford;
(6) Agrim Gupta, Stanford;
(7) Yunzhi Zhang, Stanford;
(8) Deepak Narayanan, Microsoft;
(9) Hannah Benita Teufel, Aleph Alpha;
(10) Marco Bellagente, Aleph Alpha;
(11) Minguk Kang, POSTECH;
(12) Taesung Park, Adobe;
(13) Jure Leskovec, Stanford;
(14) Jun-Yan Zhu, CMU;
(15) Li Fei-Fei, Stanford;
(16) Jiajun Wu, Stanford;
(17) Stefano Ermon, Stanford;
(18) Percy Liang, Stanford.
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Table of Links
Author contributions, Acknowledgments and References
6 Models
We evaluate 26 recent text-to-image models, encompassing various types (e.g., diffusion, autoregressive, GAN), sizes (ranging from 0.4B to 13B parameters), organizations, and accessibility (open or closed). Table 4 presents an overview of the models and their corresponding properties. In our evaluation, we employ the default inference configurations provided in the respective model’s API, GitHub, or Hugging Face repositories.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Auto Encoder: How to Ignore the Signal Noise
Auto Encoder: How to Ignore the Signal Noise | Sciencx (2024-10-12T22:44:30+00:00) A Comprehensive Evaluation of 26 State-of-the-Art Text-to-Image Models. Retrieved from https://www.scien.cx/2024/10/12/a-comprehensive-evaluation-of-26-state-of-the-art-text-to-image-models/
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