TaskMatrix 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
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Now TaskMatrix supports GroundingDINO and segment-anything! Thanks @jordddan for his efforts. For the image editing case,
GroundingDINO
is first used to locate bounding boxes guided by given text, thensegment-anything
is used to generate the related mask, and finally stable diffusion inpainting is used to edit image based on the mask.- Firstly, run
python visual_chatgpt.py --load "Text2Box_cuda:0,Segmenting_cuda:0,Inpainting_cuda:0,ImageCaptioning_cuda:0"
- Then, say
find xxx in the image
orsegment xxx in the image
.xxx
is an object. TaskMatrix will return the detection or segmentation result!
- Firstly, run
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Now TaskMatrix can support Chinese! Thanks to @Wang-Xiaodong1899 for his efforts.
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We propose the template idea in TaskMatrix!
- A template is a pre-defined execution flow that assists ChatGPT in assembling complex tasks involving multiple foundation models.
- A template contains the experiential solution to complex tasks as determined by humans.
- A template can invoke multiple foundation models or even establish a new ChatGPT session
- To define a template, simply adding a class with attributes
template_model = True
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Thanks to @ShengmingYin and @thebestannie for providing a template example in
InfinityOutPainting
class (see the following gif)- Firstly, run
python visual_chatgpt.py --load "Inpainting_cuda:0,ImageCaptioning_cuda:0,VisualQuestionAnswering_cuda:0"
- Secondly, say
extend the image to 2048x1024
to TaskMatrix! - By simply creating an
InfinityOutPainting
template, TaskMatrix can seamlessly extend images to any size through collaboration with existingImageCaptioning
,Inpainting
, andVisualQuestionAnswering
foundation models, without the need for additional training.
- Firstly, run
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TaskMatrix needs the effort of the community! We crave your contribution to add new and interesting features!
On 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 various tasks.
# clone the repo
git clone https://github.com/microsoft/TaskMatrix.git
# Go to directory
cd TaskMatrix
# 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
pip install git+https://github.com/IDEA-Research/GroundingDINO.git
pip install git+https://github.com/facebookresearch/segment-anything.git
# prepare your private OpenAI key (for Linux)
export OPENAI_API_KEY={Your_Private_Openai_Key}
# prepare your private OpenAI key (for Windows)
set OPENAI_API_KEY={Your_Private_Openai_Key}
# Start TaskMatrix !
# You can specify the GPU/CPU assignment by "--load", the parameter indicates which
# Visual Foundation Model to use and where it will be loaded to
# The model and device are separated by underline '_', the different models are separated by comma ','
# The available Visual Foundation Models can be found in the following table
# For example, if you want to load ImageCaptioning to cpu and Text2Image to cuda:0
# You can use: "ImageCaptioning_cpu,Text2Image_cuda:0"
# Advice for CPU Users
python visual_chatgpt.py --load ImageCaptioning_cpu,Text2Image_cpu
# Advice for 1 Tesla T4 15GB (Google Colab)
python visual_chatgpt.py --load "ImageCaptioning_cuda:0,Text2Image_cuda:0"
# Advice for 4 Tesla V100 32GB
python visual_chatgpt.py --load "Text2Box_cuda:0,Segmenting_cuda:0,
Inpainting_cuda:0,ImageCaptioning_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,
SegText2Image_cuda:2,Image2Pose_cpu,PoseText2Image_cuda:2,
Image2Hed_cpu,HedText2Image_cuda:3,Image2Normal_cpu,
NormalText2Image_cuda:3,Image2Line_cpu,LineText2Image_cuda:3"
Here we list the GPU memory usage of each visual foundation model, you can specify which one you like:
Foundation Model | GPU Memory (MB) |
---|---|
InstructPix2Pix | 2827 |
Text2Image | 3385 |
ImageCaptioning | 1209 |
Image2Canny | 0 |
CannyText2Image | 3531 |
Image2Line | 0 |
LineText2Image | 3529 |
Image2Hed | 0 |
HedText2Image | 3529 |
Image2Scribble | 0 |
ScribbleText2Image | 3531 |
Image2Pose | 0 |
PoseText2Image | 3529 |
SegText2Image | 3529 |
Image2Depth | 0 |
DepthText2Image | 3531 |
Image2Normal | 0 |
NormalText2Image | 3529 |
VisualQuestionAnswering | 1495 |
We appreciate the open source of the following projects:
Hugging Face LangChain Stable Diffusion ControlNet InstructPix2Pix CLIPSeg BLIP
For help or issues using the TaskMatrix, please submit a GitHub issue.
For other communications, please contact Chenfei WU (chewu@microsoft.com) or Nan DUAN (nanduan@microsoft.com).