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...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 275–586 MB
- Versión
- vSr stage2 model
- Actualizado
- 20 oct 2026
- Fuente
- Hugging Face
Descripción de la fuente
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}
}
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 20 oct
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Artefactos del modelo
3 artefactos
Artefactos del modelo
3 artefactosExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- 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
