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. 2022 Jun 2;13:3094. doi: 10.1038/s41467-022-30761-2

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — a Generation examples of VQGAN inversion with CLIP (w/ ResNet-50x4). b Generation examples of VQGAN inversion with our BriVL. c A series of generation examples by VQGAN inversion with our BriVL. d More generation examples by VQGAN inversion with our BriVL, where concepts/scenes are rarely seen by us humans (e.g., “blazing sea” and “glowing forest”) or even do not exist in real life (e.g., “cyberpunk-styled city” and “castle in the clouds”). Note that VQGAN is pre-trained on ILSVRC-2012. BriVL, CLIP and VQGAN are all frozen during text-to-image generation.