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Visual ChatGPT

Official repo for the paper: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Installation

Installing for Claude Desktop

Manual Configuration Required

This MCP server requires manual configuration. Run the command below to open your configuration file:

npx mcpbar@latest edit -c claude

This will open your configuration file where you can add the Visual ChatGPT MCP server manually.

Visual ChatGPT

Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting.

See our paper: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Open in Spaces

Updates:

  • Add custom GPU/CPU assignment
  • Add windows support
  • Merge HuggingFace ControlNet, Remove download.sh
  • Add Prompt Decorator
  • Add HuggingFace and Colab Demo
  • Clean Requirements

Insight & Goal:

One the one hand, ChatGPT (or LLMs) serves as a general interface that provides a broad and diverse understanding of a wide range of topics. On the other hand, Foundation Models serve as domain experts by providing deep knowledge in specific domains. By leveraging both general and deep knowledge, we aim at building an AI that is capable of handling a various of tasks.

Demo

image

System Architecture

Logo

Quick Start

# clone the repo
git clone https://github.com/microsoft/visual-chatgpt.git && cd visual-chatgpt

# create a new environment
conda create -n visgpt python=3.8

# activate the new environment
conda activate visgpt

#  prepare the basic environments
pip install -r requirements.txt

# prepare your private openAI private key
export OPENAI_API_KEY={Your_Private_Openai_Key}

# Start Visual ChatGPT !
# Advice for CPU Users
python visual_chatgpt.py --load ImageCaptioning_cpu,Text2Image_cpu
    
    
# Advice for 1 Tesla T4 15GB                         
python visual_chatgpt.py --load "ImageCaptioning_cuda:0,ImageEditing_cuda:0,Text2Image_cuda:0,
                                VisualQuestionAnswering_cuda:0,InstructPix2Pix_cuda:0"
                                
# Advice for 4 Tesla V100 32GB                            
python visual_chatgpt.py --load "ImageCaptioning_cuda:0,ImageEditing_cuda:0,
    Text2Image_cuda:1,Image2Canny_cpu,CannyText2Image_cuda:1,
    Image2Depth_cpu,DepthText2Image_cuda:1,VisualQuestionAnswering_cuda:2,
    InstructPix2Pix_cuda:2,Image2Scribble_cpu,ScribbleText2Image_cuda:2,
    Image2Seg_cpu,SegText2Image_cuda:2,Image2Pose_cpu,PoseText2Image_cuda:2,
    Image2Hed_cpu,HedText2Image_cuda:3,Image2Normal_cpu,
    NormalText2Image_cuda:3,Image2Line_cpu,LineText2Image_cuda:3"
                             

GPU memory usage

Here we list the GPU memory usage of each visual foundation model, you can specify which one you like:

Foundation ModelGPU Memory (MB)
ImageEditing6667
InstructPix2Pix4929
Text2Image6677
ImageCaptioning1755
Image2Canny0
CannyText2Image5540
Image2Line0
LineText2Image6679
Image2Hed0
HedText2Image6679
Image2Scribble0
ScribbleText2Image6679
Image2Pose0
PoseText2Image6681
Image2Seg919
SegText2Image5540
Image2Depth0
DepthText2Image6677
Image2Normal0
NormalText2Image6303
VisualQuestionAnswering2709

Acknowledgement

We appreciate the open source of the following projects:

Hugging FaceLangChainStable DiffusionControlNetInstructPix2PixCLIPSegBLIP

Contact Information

For help or issues using the Visual ChatGPT, please submit a GitHub issue.

For other communications, please contact Chenfei WU ([email protected]) or Nan DUAN ([email protected]).

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Visual ChatGPT: MCP Server – MCP.Bar