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--- |
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license: apache-2.0 |
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task_categories: |
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- image-text-to-image |
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- image-to-image |
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- text-to-image |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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--- |
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# GenArena |
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A unified evaluation framework for visual generation tasks using VLM-based pairwise comparison and Elo ranking. |
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[](https://arxiv.org/abs/2602.XXXXX) |
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[](https://genarena.github.io) |
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[](https://huggingface.co/spaces/genarena/leaderboard) |
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[](https://huggingface.co/datasets/rhli/genarena) |
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## Abstract |
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The rapid advancement of visual generation models has outpaced traditional evaluation approaches, necessitating the adoption of Vision-Language Models as surrogate judges. In this work, we systematically investigate the reliability of the prevailing absolute pointwise scoring standard, across a wide spectrum of visual generation tasks. Our analysis reveals that this paradigm is limited due to stochastic inconsistency and poor alignment with human perception. To resolve these limitations, we introduce **GenArena**, a unified evaluation framework that leverages a *pairwise comparison* paradigm to ensure stable and human-aligned evaluation. Crucially, our experiments uncover a transformative finding that simply adopting this pairwise protocol enables off-the-shelf open-source models to outperform top-tier proprietary models. Notably, our method boosts evaluation accuracy by over 20% and achieves a Spearman correlation of 0.86 with the authoritative LMArena leaderboard, drastically surpassing the 0.36 correlation of pointwise methods. Based on GenArena, we benchmark state-of-the-art visual generation models across diverse tasks, providing the community with a rigorous and automated evaluation standard for visual generation. |
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## Quick Start |
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### Installation |
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```bash |
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pip install genarena |
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``` |
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Or install from source: |
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```bash |
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git clone https://github.com/ruihanglix/genarena.git |
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cd genarena |
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pip install -e . |
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``` |
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### Initialize Arena |
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Download benchmark data and official arena data with one command: |
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```bash |
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genarena init --arena_dir ./arena --data_dir ./data |
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``` |
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This downloads: |
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- Benchmark Parquet data from `rhli/genarena` (HuggingFace) |
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- Official arena data (model outputs + battle logs) from `rhli/genarena-battlefield` |
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### Environment Setup |
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Set your VLM API credentials: |
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```bash |
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export OPENAI_API_KEY="your-api-key" |
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export OPENAI_BASE_URL="https://api.example.com/v1" |
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``` |
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For multi-endpoint support (load balancing and failover), use comma-separated values: |
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```bash |
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export OPENAI_BASE_URLS="https://api1.example.com/v1,https://api2.example.com/v1" |
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export OPENAI_API_KEYS="key1,key2,key3" |
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``` |
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### Run Evaluation |
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```bash |
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genarena run --arena_dir ./arena --data_dir ./data |
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``` |
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### View Leaderboard |
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```bash |
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genarena leaderboard --arena_dir ./arena --subset basic |
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``` |
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### Check Status |
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```bash |
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genarena status --arena_dir ./arena --data_dir ./data |
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``` |
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## Running Your Own Experiments |
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### Directory Structure |
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To add your own model for evaluation, organize outputs in the following structure: |
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``` |
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arena_dir/ |
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└── <subset>/ |
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└── models/ |
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└── <GithubID>_<modelName>_<yyyymmdd>/ |
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└── <model_name>/ |
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├── 000000.png |
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├── 000001.png |
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└── ... |
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``` |
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For example: |
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``` |
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arena/basic/models/johndoe_MyNewModel_20260205/MyNewModel/ |
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``` |
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### Generate Images with Diffgentor |
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Use [Diffgentor](https://github.com/ruihanglix/diffgentor) to batch generate images for evaluation: |
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```bash |
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# Download benchmark data |
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hf download rhli/genarena --repo-type dataset --local-dir ./data |
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# Generate images with your model |
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diffgentor edit --backend diffusers \ |
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--model_name YourModel \ |
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--input ./data/basic/ \ |
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--output_dir ./arena/basic/models/yourname_YourModel_20260205/YourModel/ |
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``` |
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### Run Battles for New Models |
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```bash |
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genarena run --arena_dir ./arena --data_dir ./data \ |
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--subset basic \ |
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--exp_name yourname_YourModel_20260205 |
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``` |
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GenArena automatically detects new models and schedules battles against existing models. |
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## Submit to Official Leaderboard |
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> **Coming Soon**: The `genarena submit` command will allow you to submit your evaluation results to the official GenArena leaderboard via GitHub PR. |
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The workflow will be: |
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1. Run evaluation locally with `genarena run` |
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2. Upload results to your HuggingFace repository |
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3. Submit via `genarena submit` which creates a PR for review |
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## Citation |
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```bibtex |
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TBD |
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``` |
