Kaiyi Huang1, Chengqi Duan3, Kaiyue Sun1, Enze Xie2, Zhenguo Li2, and Xihui Liu1.
1The University of Hong Kong, 2Huawei Noahβs Ark Lab, 3Tsinghua University
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 π€Accelerate environment with:
accelerate config
For evaluation, the input images files are stored in the directory "examples/samples/", with the following structures:
examples/samples/
βββ a green bench and a blue bowl_000000.png
βββ a green bench and a blue bowl_000001.png
βββ...
The evaluation result 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": "0.6900"},
{"question_id": 1, "answer": "0.7110"},
...]
The question_id is the last 6 digits of the image file name, for example, _a green bench and a blue bowl000000.png is with question_id 0, and the evaluation score 0.6900.
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.
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 $project_dir
python 3D_spatial_eval.py
The output files are formatted as a json file named "vqa_result.json" in "examples/labels/annotation_obj_detection_3d" directory.
outpath="examples/"
python CLIPScore_eval/CLIP_similarity.py --outpath=${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.
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.
If you want to use ShareGPT4V or GPT-4V as evaluation metrics as in TABLE XIII in T2I-CompBench++, you can evaluate as follows. Prepare the environment for ShareGPT4V.
ShareGPT4V is based on its repositories, please refer to the link for environment dependencies and weights:
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
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 "gpt4vresult_{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.
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.
If you want to use GORS method to finetune your diffusion model, please follow the steps to prepare the training code and training images.
The training images are stored in the directory "examples/samples/", with the format the same as the evaluation data.
Use LoRA finetuning method, please refer to the link for downloading "lora_diffusion" directory:
https://github.com/cloneofsimo/lora/tree/master
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/
βββ a green bench and a blue bowl_000000.png
βββ a green bench and a blue bowl_000001.png
βββ...
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": "0.6900"},
{"question_id": 1, "answer": "0.7110"},
...]
The dataset should be placed in the directory "examples/dataset/".
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}"
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},
}
This project is licensed under the MIT License. See the "License.txt" file for details.