metadata
base_model: Qwen/Qwen3-VL-8B-Instruct
license: mit
library_name: peft
pipeline_tag: image-text-to-text
PVC-Judge: Pairwise Visual Consistency Judge
PVC-Judge is a state-of-the-art 8B assessment model for evaluating image editing models in visual consistency. It is a pairwise preference model designed to capture the preservation of identity, structure, and semantic coherence between edited and original images.
The model was introduced in the paper GEditBench v2: A Human-Aligned Benchmark for General Image Editing and is implemented as a LoRA adapter for Qwen/Qwen3-VL-8B-Instruct.
🚀 Quick Start
To use PVC-Judge, you typically need to merge the LoRA weights with the base model.
1. Merge LoRA weights
This step requires torch, peft, and transformers.
python ./scripts/merge_lora.py \
--base-model-path /path/to/Qwen3/VL/8B/Instruct \
--lora-weights-path /path/to/LoRA/Weights \
--model-save-dir /path/to/save/PVC/Judge/model
2. Deployment or Local Inference
You can serve the merged model via vLLM or run local evaluation as described in the official repository.
Local Inference:
# Setup environment
conda env create -f environments/pvc_judge.yml
conda activate pvc_judge
# Run evaluation
bash ./scripts/local_eval.sh vc_reward
Citation
@article{jiang2026geditbenchv2,
title={GEditBench v2: A Human-Aligned Benchmark for General Image Editing},
author={Zhangqi Jiang and Zheng Sun and Xianfang Zeng and Yufeng Yang and Xuanyang Zhang and Yongliang Wu and Wei Cheng and Gang Yu and Xu Yang and Bihan Wen},
journal={arXiv preprint arXiv:2603.28547},
year={2026}
}