đ LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token
LLaVA-Mini is a unified large multimodal model that efficiently supports the understanding of images, high-resolution images, and videos. Guided by the interpretability within LMM, it significantly improves efficiency while ensuring vision capabilities. The Code, model, and demo of LLaVA-Mini are now available!
đ Quick Start
Requirements
conda create -n llavamini python=3.10 -y
conda activate llavamini
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
Command Interaction
- Image understanding, using
--image-file
:
CUDA_VISIBLE_DEVICES=0 python llavamini/eval/run_llava_mini.py \
--model-path ICTNLP/llava-mini-llama-3.1-8b \
--image-file llavamini/serve/examples/baby_cake.png \
--conv-mode llava_llama_3_1 --model-name "llava-mini" \
--query "What's the text on the cake?"
- Video understanding, using
--video-file
:
CUDA_VISIBLE_DEVICES=0 python llavamini/eval/run_llava_mini.py \
--model-path ICTNLP/llava-mini-llama-3.1-8b \
--video-file llavamini/serve/examples/fifa.mp4 \
--conv-mode llava_llama_3_1 --model-name "llava-mini" \
--query "What happened in this video?"
Reproduction and Evaluation
- Refer to Evaluation.md for the evaluation of LLaVA-Mini on image/video benchmarks.
Cases
- LLaVA-Mini achieves high-quality image understanding and video understanding.
More cases
- LLaVA-Mini dynamically compresses image to capture important visual information (brighter areas are more heavily weighted during compression).
⨠Features
- Good Performance: LLaVA-Mini achieves performance comparable to LLaVA-v1.5 while using only 1 vision token instead of 576 (compression rate of 0.17%).
- High Efficiency: LLaVA-Mini can reduce FLOPs by 77%, deliver low-latency responses within 40 milliseconds, and process over 10,000 frames of video on the GPU hardware with 24GB of memory.
- Insights: To develop LLaVA-Mini, which reduces vision tokens while maintaining visual understanding, we conduct a preliminary analysis to explore how large multimodal models (LMMs) process visual tokens. Please refer to our paper for a detailed analysis and our conclusions.
â ī¸ Important Note
LLaVA-Mini only requires 1 token to represent each image, which improves the efficiency of image and video understanding, including:
- Computational effort: 77% FLOPs reduction
- Response latency: reduce from 100 milliseconds to 40 milliseconds
- VRAM memory usage: reduce from 360 MB/image to 0.6 MB/image, support 3-hour video processing
đģ Usage Examples
Basic Usage
Download LLaVA-Mini model from here.
Run these scripts and Interact with LLaVA-Mini in your browser:
python -m llavamini.serve.controller --host 0.0.0.0 --port 10000 &
CUDA_VISIBLE_DEVICES=0 python -m llavamini.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path ICTNLP/llava-mini-llama-3.1-8b --model-name llava-mini &
python -m llavamini.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload --port 7860
đ License
This project is licensed under the GPL-3.0 license.
đ Citation
If this repository is useful for you, please cite as:
@misc{llavamini,
title={LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token},
author={Shaolei Zhang and Qingkai Fang and Zhe Yang and Yang Feng},
year={2025},
eprint={2501.03895},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.03895},
}
If you have any questions, please feel free to submit an issue or contact zhangshaolei20z@ict.ac.cn
.