Trade-offs and Relationships in Image Generation: How Do Different Evaluation Dimensions Interact?

1. v0: Release the TRIG dataset and evaluation pipeline.
2. v1: Release the Finetune pipeline and experiments.
3. v1: Release the RL (DDPO) pipeline and experiments.
4. v1.5: TBD.
5. v2: TBD.

conda create -n trig python=3.10 -y
conda activate trig
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt

We recommand to use TRIG score by vllm. Please install with

# for Qwen2.5vl, please update your transformers
pip install git+https://github.com/huggingface/transformers@f3f6c86582611976e72be054675e2bf0abb5f775
pip install accelerate
pip install 'vllm>=0.7.2'
# vllm==0.7.3 and torch=2.6.0 work well

Then deploy the selected VLM models, currently the TRIG score support GPT series, Qwen2.5-VL series, and LLaVA-NeXT Series. For more information, please visit vllm document.

# use Qwen2.5-VL-7B
vllm serve Qwen/Qwen2.5-VL-7B-Instruct --port 8000 --device cuda --host 0.0.0.0 --dtype bfloat16 --limit-mm-per-prompt image=5,video=5
# or use Qwen2.5-VL-72B with quantize version
vllm serve Qwen/Qwen2-VL-72B-Instruct-AWQ --dtype float16 --port 8000 --gpu-memory-utilization 0.85 --tensor-parallel-size 2 --quantization awq --limit_mm_per_prompt image=4

1. First Please set up a yaml file in config folder to run an experiment, as the format below:

name: "test" # name for this experiment
task: "t2i" # chosen from t2i/p2p/sub, Support one task at a time
# json path for the selected task
prompt_path: "/home/zsc/TRIG/dataset/Trig/Trig-text-to-image/text-to-imgae-new1.json"
generation:
    # selected models
    models: ["flux",]
    
evaluation:
    image_dir: ["data/output/demo",]
    result_dir: "data/result"
dimensions:
    IQ-O:
        metrics: ["GPTLogitMetric"]
    TA-R:
        metrics: ["GPTLogitMetric"]
    TA-S:
        metrics: ["GPTLogitMetric", "AnotherMetric"]
    Other Dimensions You Want:
        metrics: ["OtherMetric"]
relation:
  models: ["flux"]
  res: "formatted_flux"
  metric: "spearman_corr"
  plot: true
  heatmap: true
  tsne: true
  tradeoff: true
  quadrant_analysis: true
  thresholds:
    synergy: 0.8 
    bottleneck: 0.5 
    
  insight_thresholds:
    synergy_density: 0.4
    bottleneck_density: 0.4
    dominance_ratio: 0.8
    tradeoff_corr: 0.6

More examples could be found in the config folder.

2. Run main.py

python main.py --config your_config.yaml

3. Outputs:

• Generated images will be saved to data/output/your_task/your_model/
• Evaluation result will be saved to data/output/your_task/your_model.json
• Relation result will be saved to data/output/your_model/

4. Notes: TBD

All the metrics could be used independently. For example:

metric_class = trig.metrics.import_metric("aesthetic_predictor")
metric_instance = metric_class()
# Single Evaluation
score = metric_instance.compute(image_path="/path/to/image", prompt="prompt")
# Batch Evaluation
score = metric_instance.compute_batch_manual(images=["/path/to/image"], prompts=["prompt"])

1. Select the dimension and trade-off type you want to optimize. for example, in the paper, we choose Knowledge & Ambiguity, and try to balance these two dimensions.
2. Follow the TRIG principle, we create a original set which covers the two dim.
3. We generate images with this set, the ouput images are in flux_ft_train.zip.
4. Test these images, select good samples with trade-off as expected.
5. Use these selected image to do LoRA finetune on flux.
6. Then we got the balanced flux model.

use model name 'sd35_dtm_dim', 'sana_dtm_dim', 'xflux_dtm_dim' and 'hqedit_dtm_dim' in the yaml config file to generate with Prompt Engineering.

Many thanks to the great works in GenAI Models like FLUX, Benchmarks like HEIM, Metric like VQAScore.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

@article{zhang2025trade,
  title={Trade-offs in Image Generation: How Do Different Dimensions Interact?},
  author={Zhang, Sicheng and Xie, Binzhu and Yan, Zhonghao and Zhang, Yuli and Zhou, Donghao and Chen, Xiaofei and Qiu, Shi and Liu, Jiaqi and Xie, Guoyang and Lu, Zhichao},
  journal={arXiv preprint arXiv:2507.22100},
  year={2025}
}