Groma: Grounded Multimodal Assistant

Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Chuofan Ma, Yi Jiang, Jiannan Wu, Zehuan Yuan, Xiaojuan Qi

Groma is an MLLM with exceptional region understanding and visual grounding capabilities. It can take user-defined region inputs (boxes) as well as generate long-form responses that are grounded to visual context.

Groma presents a novel paradigm of grounded MLLMs. (a) LLM for localization (e.g., Kosmos-2, Shikra); (b) External modules for localization (e.g., Lisa); and (c) Visual tokenier for localization (Groma).

• Install
• Model
• Data
• Training
• Inference
• Evaluation

State-of-the-art performance on referring expression comprehension (REC) benchmarks among multimodal large language models.

Clone the repository

git clone https://github.com/FoundationVision/Groma.git
cd Groma

Create the conda environment and install dependencies

conda create -n groma python=3.9 -y
conda activate groma
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=11.8 -c pytorch -c nvidia
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

cd mmcv
MMCV_WITH_OPS=1 pip install -e .
cd ..

Install falsh-attention for training

pip install ninja
pip install flash-attn --no-build-isolation

To play with Groma, please download the model weights from huggingface.

We additionally provide pretrained checkpoints from intermediate training stages. You can start from any point to customize training.

We provide instructions to download datasets used at different training stages of Groma, including Groma Instruct, a 30k viusally grounded conversation dataset constructed with GPT-4V. You don't have to download all of them unless you want to train Groma from scratch. Please follow instructions in DATA.md to prepare datasets.

For detection pretraining, please run

bash scripts/det_pretrain.sh {path_to_dinov2_ckpt} {output_dir}

For alignment pretraining, please run

bash scripts/vl_pretrain.sh {path_to_vicuna_ckpt} {path_to_groma_det_pretrain_ckpt} {output_dir}

For instruction finetuning, please run

bash scripts/vl_finetune.sh {path_to_groma_7b_pretrain_ckpt} {output_dir}

To test on single image, you can run

python -m groma.eval.run_groma \
    --model-name {path_to_groma_7b_finetune} \
    --image-file {path_to_img} \
    --query {user_query} \
    --quant_type 'none' # support ['none', 'fp16', '8bit', '4bit'] for inference

For evaluation, please refer to EVAL.md for more details.

If you find this repo useful for your research, feel free to give us a star ⭐ or cite our paper:

@article{ma2024groma,
  title={Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models},
  author={Ma, Chuofan and Jiang, Yi and Wu, Jiannan and Yuan, Zehuan and Qi, Xiaojuan},
  journal={arXiv preprint arXiv:2404.13013},
  year={2024}
}

Groma is built upon the awesome works LLaVA and GPT4ROI.

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.