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Comparisons with existing methods 💡

Updates 📌

• [2024/10/22] We integrated TokenPacker-HD framework with Osprey to achieve fine-grained high-resolution pixel-level understanding with large performance gains. We will release the codes soon for your reference.
• [2024/7/25] We released checkpoints, please check them.
• [2024/7/3] We released the paper of our TokenPacker on Arxiv.
• [2024/7/3] We released the training and inference codes.

What is TokenPacker 👀

TokenPacker is a novel visual projector, which adopts a coarse-to-fine scheme to inject the enriched characteristics to generate the condensed visual tokens. Using TokenPacker, we can compress the visual tokens by 75%∼89%, while achieves comparable or even better performance across diverse benchmarks with significantly higher efficiency.

Algorithms

We provide the pseudo-codes to showcase the detailed processing flow.

Core codes

As a visual projector, TokenPacker is implemented by a class TokenPacker, which can be found in multimodal_projector/builder.py

Comparisons with various projectors

High-Resolution Image Understanding with TokenPacker 🔬

To support efficient high-resolution image understanding, we further develop an effective image cropping method TokenPacker-HD.

Install 🛠️

1. Clone this repository and navigate to TokenPacker folder

git clone https://github.com/CircleRadon/TokenPacker.git
cd TokenPacker

2. Install packages

conda create -n tokenpacker python=3.10 -y
conda activate tokenpacker
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

3. Install additional packages for training cases

pip install -e ".[train]"
pip install flash-attn --no-build-isolation

Training 🚀

LLaVA-TokenPacker

Dataset

To make a fair comparison, we use the same training data as in LLaVA-1.5, i.e., LLaVA-Pretrain-558K for stage 1, and Mix665k for stage 2.

Training

• Stage1: Image-Text Alignment Pre-training

bash scripts/v1_5/pretrain.sh

• Stage2: Visual Instruction Tuning

bash scripts/v1_5/finetune.sh

Note: Using --scale_factor to control compression ratio, support [2,3,4]

LLaVA-TokenPacker-HD

Dataset

To obtain the competitive high-resolution performance, we use 2.7M data as organized by Mini-Gemini, i.e., 1.2M for stage 1 and 1.5M for stage 2.

Training

• Stage1: Image-Text Alignment Pre-training

bash scripts/v1_5/pretrain_hd.sh

• Stage2: Visual Instruction Tuning

bash scripts/v1_5/finetune_hd.sh

Note:

• Using --scale_factor to control compression ratio, support [2,3,4].
• Using --patch_num to control max patch dividing number, support [9,16,25].

Experiments

Model Zoo

Model	Max Res.	Compre. Ratio	Token Num.	Max Patch Num.	Training Data	Download
TokenPacker-7b	336x336	1/4	144	-	558K+665K	checkpoints
TokenPacker-13b	336x336	1/4	144	-	558K+665K	checkpoints
TokenPacker-HD-7b	1088x1088	1/4	~954	9	1.2M+1.5M	checkpoints
TokenPacker-HD-13b	1088x1088	1/4	~954	9	1.2M+1.5M	checkpoints
TokenPacker-HD-13b	1344x1344	1/4	~1393	16	1.2M+1.5M	checkpoints
TokenPacker-HD-13b	1344x1344	1/9	~619	16	1.2M+1.5M	checkpoints
TokenPacker-HD-13b	1344x1344	1/16	~347	16	1.2M+1.5M	checkpoints

Note:

• The token number of TokenPacker-HD is the average statistically across all training and test data.
• The training data of 558K+665K follows LLaVA-1.5, the one of 1.2M+1.5M follows Mini-Gemini.
• All LLMs use Vicuna-7b/13b as based LLM.

Visualization

We provide some visual examples.

High-resolution image understanding.

TODO List 📝

• Release the training and inference codes.
• Release all checkpoints.

Acknowledgement 💌

• LLaVA-v1.5: the codebase we built upon.
• Mini-Gemini: the organized data we used for training high-resolution method.

BibTeX 🖊️

@misc{TokenPacker,
  title={TokenPacker: Efficient Visual Projector for Multimodal LLM},
  author={Wentong Li, Yuqian Yuan, Jian Liu, Dongqi Tang, Song Wang, Jianke Zhu and Lei Zhang},
  year={2024},
  eprint={2407.02392},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}