Till now we have seen many AI tools for music, videos, content, visuals, images, management and many more tasks. But this time, here is a new version of AI where we can track and edit the points in given images.
Yes, sounds interesting and it is. Just pick up a point from your image and extend any character or element of the image up to that point.
Let us quickly know what this tool is and what exactly it does.
What is DraGAN AI?
DraGAN AI stands for DRAG your GAN – A point-based manipulation system that helps to edit images by moving the elements to the given point.
It helps to manipulate the shape, expressions, pose, actions and directions. It works on GANs i.e. Generative Adversarial Networks which were used to do annotations manually.
It is a feature-based motion system that uses novel point-tracking systems.
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DraGAN AI Features
Superior to traditional approaches: With a point tracking system, DraGAN is superior to the other traditional methods.
Drag and create: It only requires dragging the points on the image and creating the updated image.
It is free of cost and can be used by anyone. The entire code is available on GIT.
Alternatives to DraGAN AI
We found only 2 proper alternatives to DraGAN AI.
- Frameright: Edits images as per requirements.
- Baseten: Helps to edit images.
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How to Use DraGAN AI?
To use this system, one need to understand how a code runs as there is a link to the GIT code on the website.
Once you click on View code, you move to GitHub and later you can use the code directly.
Two options are available to get a direct Web Demo: OpenXLab and HuggingFace.
Apart from that the code can be opened using GoogleColab and further the Python Pickel file can be downloaded and used with your images.
There are a few fixed installation steps that are listed on the website as below. once the installation is successful, you can run the through Docker and lastly, to test it, you need to install the pre-trained model images.
It can be said that to use this platform there are a few complex steps but they have provided detailed descriptions for each step over GitHub.
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