Harmonizing Visual Text Comprehension and Generation

Environment

step 1: set up the environment

git clone https://github.com/bytedance/TextHarmony
cd TextHarmony
pip install -r requirements.txt
# install `MultiScaleDeformableAttention` module
cd TextHarmony/models/utils/ops
python setup.py install

some of the packages like mmcv and flash_attn in requirements.txt may need to be installed manually.

step 2: download pretraining weights

cd TextHarmony
python TextHarmony/scripts/download_hf_models.py

step 3: download the model weight of TextHarmony

# concatenate the model files
cat pytorch_model.binaa pytorch_model.binab pytorch_model.binac > pytorch_model.bin

Inference

step1: modify ‘load_from’, ‘llm_model_path’, ‘encoder_model_path’ and ‘pretrained_model_name_or_path’ in example_inference.yaml

step 2: run the following command:

torchrun --nproc_per_node 1 --nnodes 1 --master_port 2333 inference.py  --config_file=TextHarmony/TextHarmony/configs/release/example_inference.yaml

Evaluation

image comprehension

step1: modify ‘data_root’ and ‘data_path’ in 896-moe-eval.yaml. The structure of ‘data_path’ should be as follows:

[
    {
        "image": image_path,
        "question": question,
        "answer": answer
    },
]

step 2: run the following command

torchrun --nproc_per_node 1 --nnodes 1 --master_port 2333 evaluate.py --config_file=TextHarmony/TextHarmony/configs/release/896-moe-eval.yaml

image generation

step 1: download AnyText-Benchmark

step 2: generate the target images

torchrun --nproc_per_node 1 --nnodes 1 --master_port 2333 inference.py  --config_file=TextHarmony/TextHarmony/configs/release/896-moe-inference.yaml

step 3: calculate the results

python TextHarmony/image_eval/eval_dgocr.py

Training

• TODO

Acknowledgment

We thank the great work of MM-Interleaved, TextDiffuser, AnyText and LoRAMoE

Citation

@article{zhao2024harmonizing,
  title={Harmonizing Visual Text Comprehension and Generation},
  author={Zhao, Zhen and Tang, Jingqun and Wu, Binghong and Lin, Chunhui and Wei, Shu and Liu, Hao and Tan, Xin and Zhang, Zhizhong and Huang, Can and Xie, Yuan},
  journal={arXiv preprint arXiv:2407.16364},
  year={2024}
}