TVQRAP
If you find our work useful, please consider citing: @article{li2025texture, title={Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution}, author={Li, Qifan and Zou, Jiale...
Source du modèle
- Type
- Other
- Taille du fichier
- 275–586 MB
- Version
- vSr stage2 model
- Mis à jour
- 20 oct. 2026
- Source
- Hugging Face
Description de la source
TVQRAP: Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution (ICLR 2026)
If you find our work useful, please consider citing:
@article{li2025texture,
title={Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution},
author={Li, Qifan and Zou, Jiale and Zhang, Jinhua and Long, Wei and Zhou, Xingyu and Gu, Shuhang},
journal={arXiv preprint arXiv:2509.23774},
year={2025}
}
Sources
1 source
Sources
1 sourceVérifié 20 oct.
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Artefacts du modèle
3 artefacts
Artefacts du modèle
3 artefactsExtraits de sources
2 extraits
Extraits de sources
2 extraits--- license: apache-2.0 pipeline_tag: image-to-image arxiv: 2509.23774 tags: - super-resolution - generative-models - quantization - pytorch --- # TVQRAP: Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution (ICLR 2026) [paper](https://arxiv.org/abs/2509.23774) | [code](https://github.com/CVL-UESTC/TVQ-RAP) If you find our work useful, please consider citing: ```bibtex @article{li2025texture, title={Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution}, author={Li, Qifan and Zou, Jiale and Zhang, Jinhua and Long, Wei and Zhou, Xingyu and Gu, Shuhang}, journal={arXiv preprint arXiv:2509.23774}, year={2025} }
Source context: 0 downloads · 1 likes · Pipeline image-to-image · Repo CVLUESTC/TVQRAP
