A Comprehensive Evaluation of 26 State-of-the-Art Text-to-Image Models
2024-10-13 06:44:30 Author: hackernoon.com(查看原文) 阅读量:2 收藏

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.

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.


文章来源: https://hackernoon.com/a-comprehensive-evaluation-of-26-state-of-the-art-text-to-image-models?source=rss
如有侵权请联系:admin#unsafe.sh