拥有图像对话能力的AI开源项目:MiniGPT-4

MiniGPT-4是一个拥有类似 GPT-4 图像对话能力的AI开源项目，由阿卜杜拉国王科技大学的几位博士牵头开发。基于Python，遵守BSD-3-Clause开源协议。

项目团队成员将一个冻结的视觉编码器与一个冻结的 Vicuna 进行对齐，造出了 MiniGPT-4。 MiniGPT-4 具有许多类似于 GPT-4 的能力，如详细的图像描述生成、从手写草稿创建网站等。 MiniGPT-4 还能根据图像创作故事和诗歌，为图像中显示的问题提供解决方案，教用户如何根据食物照片做饭等等。

源代码：https://github.com/Vision-CAIR/MiniGPT-4

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Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)

minigpt-4.github.io 

MiniGPT-V

MiniGPT-v2: Large Language Model as a Unified Interface for Vision-Language Multi-task Learning

Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong☨, Mohamed Elhoseiny☨

☨equal last author

MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models

Deyao Zhu*, Jun Chen*, Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny

*equal contribution

King Abdullah University of Science and Technology

💡 Get help - Q&A or Discord 💬

**Example Community Efforts Built on Top of MiniGPT-4 **

• InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4 Lai Wei, Zihao Jiang, Weiran Huang, Lichao Sun, Arxiv, 2023

• PatFig: Generating Short and Long Captions for Patent Figures.", Aubakirova, Dana, Kim Gerdes, and Lufei Liu, ICCVW, 2023

• SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model, Juexiao Zhou and Xiaonan He and Liyuan Sun and Jiannan Xu and Xiuying Chen and Yuetan Chu and Longxi Zhou and Xingyu Liao and Bin Zhang and Xin Gao, Arxiv, 2023

• ArtGPT-4: Artistic Vision-Language Understanding with Adapter-enhanced MiniGPT-4.", Yuan, Zhengqing, Huiwen Xue, Xinyi Wang, Yongming Liu, Zhuanzhe Zhao, and Kun Wang, Arxiv, 2023

News

[Oct.31 2023] We release the evaluation code of our MiniGPT-v2.

[Oct.24 2023] We release the finetuning code of our MiniGPT-v2.

[Oct.13 2023] Breaking! We release the first major update with our MiniGPT-v2

[Aug.28 2023] We now provide a llama 2 version of MiniGPT-4

Online Demo

Click the image to chat with MiniGPT-v2 around your images

Click the image to chat with MiniGPT-4 around your images

MiniGPT-v2 Examples

MiniGPT-4 Examples

More examples can be found in the project page.

Getting Started

Installation

1. Prepare the code and the environment

Git clone our repository, creating a python environment and activate it via the following command

git clone https://github.com/Vision-CAIR/MiniGPT-4.git
cd MiniGPT-4
conda env create -f environment.yml
conda activate minigptv

2. Prepare the pretrained LLM weights

MiniGPT-v2 is based on Llama2 Chat 7B. For MiniGPT-4, we have both Vicuna V0 and Llama 2 version. Download the corresponding LLM weights from the following huggingface space via clone the repository using git-lfs.

Llama 2 Chat 7B	Vicuna V0 13B	Vicuna V0 7B
Download	Downlad	Download

Then, set the variable llama_model in the model config file to the LLM weight path.

• For MiniGPT-v2, set the LLM path here at Line 14.

• For MiniGPT-4 (Llama2), set the LLM path here at Line 15.

• For MiniGPT-4 (Vicuna), set the LLM path here at Line 18

3. Prepare the pretrained model checkpoints

Download the pretrained model checkpoints

MiniGPT-v2 (after stage-2)	MiniGPT-v2 (after stage-3)	MiniGPT-v2 (online developing demo)
Download	Download	Download

For MiniGPT-v2, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/minigptv2_eval.yaml at Line 8.

MiniGPT-4 (Vicuna 13B)	MiniGPT-4 (Vicuna 7B)	MiniGPT-4 (LLaMA-2 Chat 7B)
Download	Download	Download

For MiniGPT-4, set the path to the pretrained checkpoint in the evaluation config file in eval_configs/minigpt4_eval.yaml at Line 8 for Vicuna version or eval_configs/minigpt4_llama2_eval.yaml for LLama2 version.

Launching Demo Locally

For MiniGPT-v2, run

python demo_v2.py --cfg-path eval_configs/minigptv2_eval.yaml  --gpu-id 0

For MiniGPT-4 (Vicuna version), run

python demo.py --cfg-path eval_configs/minigpt4_eval.yaml  --gpu-id 0

For MiniGPT-4 (Llama2 version), run

python demo.py --cfg-path eval_configs/minigpt4_llama2_eval.yaml  --gpu-id 0

To save GPU memory, LLMs loads as 8 bit by default, with a beam search width of 1. This configuration requires about 23G GPU memory for 13B LLM and 11.5G GPU memory for 7B LLM. For more powerful GPUs, you can run the model in 16 bit by setting low_resource to False in the relevant config file:

• MiniGPT-v2: minigptv2_eval.yaml
• MiniGPT-4 (Llama2): minigpt4_llama2_eval.yaml
• MiniGPT-4 (Vicuna): minigpt4_eval.yaml

Thanks @WangRongsheng, you can also run MiniGPT-4 on Colab

Training

For training details of MiniGPT-4, check here.

For finetuning details of MiniGPT-v2, check here

Evaluation

For finetuning details of MiniGPT-v2, check here

Acknowledgement

• BLIP2 The model architecture of MiniGPT-4 follows BLIP-2. Don't forget to check this great open-source work if you don't know it before!
• Lavis This repository is built upon Lavis!
• Vicuna The fantastic language ability of Vicuna with only 13B parameters is just amazing. And it is open-source!
• LLaMA The strong open-sourced LLaMA 2 language model.

from https://github.com/Vision-CAIR/MiniGPT-4