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README.md
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---
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license: mit
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base_model:
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- Qwen/Qwen3-VL-8B-Instruct
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---
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<h1 align="center">PVC-Judge is a state-of-the-art 8B assessment model for evaluating image editing models in visual consistency.</h1>
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<p align="center">
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<!-- <a href="https://arxiv.org/abs/xxxxx"><img src="https://img.shields.io/badge/Paper-arXiv%3Aedit this-b31b1b?logo=arxiv&logoColor=red"></a> -->
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<a href="https://zhangqijiang07.github.io/gedit2_web/"><img src="https://img.shields.io/badge/%F0%9F%8C%90%20Project%20Page-Website-8A2BE2"></a>
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<a href="https://huggingface.co/datasets/GEditBench-v2/GEditBench-v2"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HF-GEditBench v2-blue"></a>
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<a href="https://huggingface.co/datasets/GEditBench-v2/VCReward-Bench"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HF-VCReward Bench-blue"></a>
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<a href="https://huggingface.co/GEditBench-v2/PVC-Judge"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20HF-PVC Judge-blue"></a>
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## 🚀 Quick Start!
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### Clone github repo
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```bash
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git clone https://github.com/ZhangqiJiang07/GEditBench_v2.git
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cd GEditBench_v2
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```
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### Option 1: Packaged as an online client
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- Merge LoRA weights to models, required env `torch/peft/transformers`
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```bash
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python ./scripts/merge_lora.py \
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--base-model-path /path/to/Qwen3/VL/8B/Instruct \
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--lora-weights-path /path/to/LoRA/Weights \
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--model-save-dir /path/to/save/PVC/Judge/model
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```
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- Implement online server via vLLM
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model /path/to/save/PVC/Judge/model \
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--served-model-name PVC-Judge \
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--tensor-parallel-size 1 \
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--mm-encoder-tp-mode data \
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--limit-mm-per-prompt.video 0 \
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--host 0.0.0.0 \
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--port 25930 \
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--dtype bfloat16 \
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--gpu-memory-utilization 0.80 \
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--max_num_seqs 32 \
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--max-model-len 48000 \
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--distributed-executor-backend mp
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```
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- Use `autopipeline` for inference.
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See our [repo](https://github.com/ZhangqiJiang07/GEditBench_v2/tree/main) for detailed usage!
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### Option 2: Offline Inference
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```bash
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# For local judge inference
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conda env create -f environments/pvc_judge.yml
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conda activate pvc_judge
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# or:
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python3.12 -m venv .venvs/pvc_judge
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source .venvs/pvc_judge/bin/activate
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python -m pip install -r environments/requirements/pvc_judge.lock.txt
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# Run
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bash ./scripts/local_eval.sh vc_reward
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```
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