No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.
Cataloged from Qwen/Qwen-Image-Bench
All models by Qwen →
A fine-tuned judge model for evaluating text-to-image (T2I) generation quality. Built on top of Qwen3.6-27B, it scores generated images across 5 hierarchical dimensions using structured checklists and outputs JSON-formatted evaluation results.
Q-Judger is a vision-language model fine-tuned specifically for automated evaluation of text-to-image generated images. Given a text prompt and a generated image, the model evaluates the image on fine-grained quality criteria organized in a 3-level hierarchy and outputs structured JSON scores.
The model evaluates images across 5 top-level dimensions, each with multiple sub-dimensions:
We validate the judge model against human expert rankings by computing Spearman rank correlation ($\rho$) between the model's rankings and human expert rankings across the five L1 pillars and overall. All correlations are statistically significant ($p < 10^{-4}$, $N = 18$ models).
1. Create and activate a virtual environment with uv:
2. Install PyTorch (select the command matching your CUDA version):
See the official guide: https://pytorch.org/get-started/locally/
3. Install Python dependencies:
This installs all required dependencies including ms-swift.
Prepare a CSV, JSON, or JSONL file with the following columns:
The model outputs a JSON object per dimension, structured as:
Example (Quality dimension):
The judge model uses fixed inference parameters for reproducibility:
If you find this model useful, please cite our paper:
This project is licensed under the Apache License 2.0.
git clone https://github.com/QwenLM/Qwen-Image-Bench.gitcd Qwen-Image-Benchuv venv myenv --python 3.11source myenv/bin/activateuv pip install -r requirements.txtpython judge.py \ --input your_data.jsonl \ --model Qwen/Qwen-Image-Bench{ "Level-2 Dimension": { "Level-3 Dimension": {"score": 0|1|2|"N/A"} }}{ "Realism": { "Physical Logic": {"score": 1}, "Material Texture": {"score": 2} }, "Detail": { "Noise": {"score": 1}, "Edge Clarity": {"score": 1}, "Naturalness": {"score": 1} }, "Resolution": { "Resolution": {"score": 2} }}@misc{li2026qwenimagebenchgenerationcreationtexttoimage, title={Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation}, author={Niantong Li and Guangzheng Hu and Weixu Qiao and Ying Ba and Qichen Hong and Shijun Shen and Jinlin Wang and Fan Zhou and Jianye Kang and Xin Shang and Ziyi He and Wei Wang and Dalin Li and Jiahao Li and Jie Zhang and Kaiyuan Gao and Kun Yan and Lihan Jiang and Ningyuan Tang and Shengming Yin and Tianhe Wu and Xiao Xu and Xiaoyue Chen and Yuxiang Chen and Yan Shu and Yanran Zhang and Yilei Chen and Yixian Xu and Zekai Zhang and Zhendong Wang and Zihao Liu and Zikai Zhou and Hongzhu Shi and Yi Wang and Bing Zhao and Hu Wei and Lin Qu and Chenfei Wu}, year={2026}, eprint={2605.28091}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2605.28091}, }