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Karine-Huang / T2I-CompBench

[Neurips 2023] T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation

Home Page:https://arxiv.org/pdf/2307.06350.pdf

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T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generation

Kaiyi Huang1, Chengqi Duan3, Kaiyue Sun1, Enze Xie2, Zhenguo Li2, and Xihui Liu1.

1The University of Hong Kong, 2Huawei Noah’s Ark Lab, 3Tsinghua University

🚩 New Features/Updates

  • βœ… Jun. 27, 2024. Release human evaluation of image-score pairs.
  • βœ… Mar. 14, 2024. Release a more comprehensive version of compositional benchmark T2I-CompBench++.
  • βœ… Mar. 05, 2024. πŸ’₯ Evaluation metric adopted by 🧨 Stable Diffusion 3.
  • βœ… Dec. 02, 2023. Release the inference code for generating images in metric evaluation.
  • βœ… Oct. 20, 2023. πŸ’₯ Evaluation metric adopted by 🧨 DALL-E 3 as the evaluation metric for compositionality.
  • βœ… Sep. 30, 2023. πŸ’₯ Evaluation metric adopted by 🧨 PixArt-Ξ± as the evaluation metric for compositionality.
  • βœ… Sep. 22, 2023. πŸ’₯ Paper accepted to Neurips 2023.
  • βœ… Jul. 9, 2023. Release the dataset, training and evaluation code.

Installing the dependencies

Before running the scripts, make sure to install the library's training dependencies:

Important

We recommend using the latest code to ensure consistency with the results presented in the paper. To make sure you can successfully run the example scripts, execute the following steps in a new virtual environment. We use the diffusers version as 0.15.0.dev0 You can either install the development version from PyPI:

pip install diffusers==0.15.0.dev0

or install from the provided source:

unzip diffusers.zip
cd diffusers
pip install .

Then cd in the example folder and run

pip install -r requirements.txt

And initialize an πŸ€—Accelerate environment with:

accelerate config

Finetuning

  1. LoRA finetuning

Use LoRA finetuning method, please refer to the link for downloading "lora_diffusion" directory:

https://github.com/cloneofsimo/lora/tree/master
  1. Example usage
export project_dir=/T2I-CompBench
cd $project_dir

export train_data_dir="examples/samples/"
export output_dir="examples/output/"
export reward_root="examples/reward/"
export dataset_root="examples/dataset/color.txt"
export script=GORS_finetune/train_text_to_image.py

accelerate launch --multi_gpu --mixed_precision=fp16 \
--num_processes=8 --num_machines=1 \
--dynamo_backend=no "${script}" \
--train_data_dir="${train_data_dir}" \
--output_dir="${output_dir}" \
--reward_root="${reward_root}" \
--dataset_root="${dataset_root}"

or run

cd T2I-CompBench
bash GORS_finetune/train.sh

The image directory should be a directory containing the images, e.g.,

examples/samples/
        β”œβ”€β”€ a green bench and a blue bowl_000000.png
        β”œβ”€β”€ a green bench and a blue bowl_000001.png
        └──...

The reward directory should include a json file named "vqa_result.json", and the json file should be a dictionary that maps from {"question_id", "answer"}, e.g.,

[{"question_id": 0, "answer": "0.7110"},
 {"question_id": 1, "answer": "0.7110"},
 ...]

The dataset should be placed in the directory "examples/dataset/".

Evaluation

  1. Install the requirements

MiniGPT4 and ShareGPT4V are based on their repositories, please refer to the links for environment dependencies and weights:

https://github.com/Vision-CAIR/MiniGPT-4
https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V

For convenience, you can try the following commands to install ShareGPT4V's environment and download the required weights.

export project_dir=MLLM_eval/ShareGPT4V-CoT_eval/
cd $project_dir
conda create -n share4v python=3.10 -y
conda activate share4v
pip install --upgrade pip
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
pip install spacy
python -m spacy download en_core_web_sm
mkdir -p Lin-Chen/
cd Lin-Chen/
git lfs install
git clone https://huggingface.co/Lin-Chen/ShareGPT4V-7B_Pretrained_vit-large336-l12
  1. Example usage

For evaluation, the input images files are stored in the directory "examples/samples/", with the format the same as the training data.

BLIP-VQA:

export project_dir="BLIPvqa_eval/"
cd $project_dir
out_dir="../examples/"
python BLIP_vqa.py --out_dir=$out_dir

or run

cd T2I-CompBench
bash BLIPvqa_eval/test.sh

The output files are formatted as a json file named "vqa_result.json" in "examples/annotation_blip/" directory.

UniDet:

download weight and put under repo experts/expert_weights:

mkdir -p UniDet_eval/experts/expert_weights
cd UniDet_eval/experts/expert_weights
wget https://huggingface.co/shikunl/prismer/resolve/main/expert_weights/Unified_learned_OCIM_RS200_6x%2B2x.pth
wget https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt
pip install gdown
gdown https://docs.google.com/uc?id=1C4sgkirmgMumKXXiLOPmCKNTZAc3oVbq

for 2D-spatial evaluation, run:

export project_dir=UniDet_eval
cd $project_dir

python 2D_spatial_eval.py

To calculate prompts from the "complex" category, set the "--complex" parameter to True; otherwise, set it to False. The output files are formatted as a json file named "vqa_result.json" in "examples/labels/annotation_obj_detection_2d" directory.

for numeracy evaluation, run:

export project_dir=UniDet_eval
cd $project_dir

python numeracy_eval.py

The output files are formatted as a json file named "vqa_result.json" in "examples/annotation_num" directory.

for 3D spatial evaluation, run:

export project_dir=UniDet_eval
cd $project_dir
python 3D_spatial_eval.py

The output files are formatted as a json file named "vqa_result.json" in "examples/labels/annotation_obj_detection_3d" directory.

CLIPScore:

outpath="examples/"
python CLIPScore_eval/CLIP_similarity.py --outpath=${outpath}

or run

cd T2I-CompBench
bash CLIPScore_eval/test.sh

To calculate prompts from the "complex" category, set the "--complex" parameter to True; otherwise, set it to False. The output files are formatted as a json file named "vqa_result.json" in "examples/annotation_clip" directory.

3-in-1:

export project_dir="3_in_1_eval/"
cd $project_dir
outpath="../examples/"
python "3_in_1.py" --outpath=${outpath}

The output files are formatted as a json file named "vqa_result.json" in "examples/annotation_3_in_1" directory.

MLLM_eval:

GPT-4V:

Add your openai api key (instructions) at line 13.

export project_dir=MLLM_eval
cd $project_dir
python MLLM_eval/gpt4v_eval.py --category "color" --start 0 --step 10

The output files are formatted as a json file named "gpt4v_result_{start}_{step}.json" in "examples/gpt4v" directory.

In the paper we test 600 images, setting {start=0, step=10}, and {start=1, step=10} from existing 3000 images each category.

ShareGPT4V-CoT:

For ShareGPT4V evaluation, run the following commands:

export project_dir=MLLM_eval/ShareGPT4V-CoT_eval/
cd $project_dir
category="color"
output_path="../../examples/"
python Share_eval.py --category ${category} --file-path ${output_path} --cot

The output files are formatted as a json file named "vqa_result.json" in "examples/sharegpt4v" directory.

MiniGPT4-CoT:

If the category to be evaluated is one of color, shape and texture:

export project_dir=MLLM_eval/MiniGPT4-CoT_eval/
cd $project_dir
category="color"
img_file="../../examples/samples/"
output_path="../../examples/"
python mGPT_cot_attribute.py --category=${category} --img_file=${img_file} --output_path=${output_path}

If the category to be evaluated is one of spatial, non-spatial and complex:

export project_dir=MLLM_eval/MiniGPT4_CoT_eval/
cd $project_dir
category="non-spatial"
img_file="../../examples/samples/"
output_path="../../examples"
python mGPT_cot_general.py --category=${category} --img_file=${img_file} --output_path=${output_path}

The output files are formatted as a csv file named "mGPT_cot_output.csv" in output_path.

Inference

Run the inference.py to visualize the image.

export pretrained_model_path="checkpoint/color/lora_weight_e357_s124500.pt.pt"
export prompt="A bathroom with green tile and a red shower curtain"
python inference.py --pretrained_model_path "${pretrained_model_path}" --prompt "${prompt}"

Generate images for metric calculation. Run the inference_eval.py to generate images in the test set. As stated in the paper, 10 images are generated per prompt for metric calculation, and we use the fixed seed across all methods. You can specify the test set by changing the "from_file" parameter among {color_val.txt, shape_val.txt, texture_val.txt, spatial_val.txt, non_spatial_val.txt, complex_val.txt}.

export from_file="../examples/dataset/color_val.txt"
python inference_eval.py  --from_file "${from_file}"

Citation

If you're using T2I-CompBench in your research or applications, please cite using this BibTeX:

@article{huang2023t2icompbench,
      title={T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation}, 
      author={Kaiyi Huang and Kaiyue Sun and Enze Xie and Zhenguo Li and Xihui Liu},
      journal={arXiv preprint arXiv:2307.06350},
      year={2023},
}

License

This project is licensed under the MIT License. See the "License.txt" file for details.

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[Neurips 2023] T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation

https://arxiv.org/pdf/2307.06350.pdf

License:MIT License

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