Welcome to the episode 4 of AI Tribe 1-1-1 : A Biweekly newsletter designed to spark your interest in AI tools, concepts, applications and research !
Controlnet is one of the trending topics in AI world. From this research paper being published in Feb , to all the exciting applications it has resulted until now, I’ll try to cover most of them today.
So Let’s jump straight in !
P.S : Refer a friend and unlock exclusive offers waiting for you !
⚙️ Tool : Room AI - Easy Interior Design
Restyle your room and generate interior design ideas.
Powerful AI with an intuitive interface that's easy-to-use for both home owners and interior design professionals.
Try for free 👉 https://roomai.com
My creations can be found on twitter :
https://twitter.com/NexusInsightsHQ/status/1670095172359843843
😇 Today’s Recipe : AI-generated brand promo !
AI is a game-changer for marketing. Create eye grabbing promo video using Controlnet in a few seconds.
Here's how you can make your very own:
Choose the Lineart model for this demo.
Upload your logo and write your prompt for the kind of background you want. That's it. You will get the result in just seconds.
📈 Another trending Application of ControlNet is AI generated QR code. Checkout.
https://www.theinsaneapp.com/2023/06/free-qr-code-ai-art-generator.html
P.S : Refer a friend and unlock exclusive offers waiting for you !
🔗 Article : What is ControlNet ?
ControlNet is a neural network structure that allows for conditional control of diffusion models. This means that you can use ControlNet to specify the desired content of the generated image. Just like the one you saw in above demo! ControlNet is also more powerful than stable diffusion, and it can generate more realistic images. However, ControlNet is also more difficult to train.
Here’s another example :
Controlling image generation with (1) edge detection and (2) human pose detection.
Here’s how it works :
During training, ControlNet uses a technique called “zero convolution,” which is a 1x1 convolution with both weight and bias initialized as zeros. Before training, all zero convolutions output zeros, and ControlNet does not cause any distortion to the original model. The “trainable” copy learns the specified conditions, while the “locked” copy remains unchanged.
This approach has several advantages. First, it allows for training on small datasets of image pairs without compromising the production-ready diffusion models. Second, no layer is trained from scratch, which means that the original model is safe. Third, this approach allows for training on small-scale or even personal devices, which makes it practical for a wide range of applications.
Recent Research
There has been a lot of recent research on ControlNet. Some of the most interesting recent work includes:
The use of ControlNet for image inpainting : Adobe Generative Fill is a feature in Adobe Photoshop that allows users to generate new content in images using text prompts.
The use of ControlNet for style transfer along with Stable Diffusion ( i.e , The interior design tool mentioned above )
The use of ControlNet for generating images from text descriptions along with Stable Diffusion ( i.e, the one in the example - edge and human pose detection )
Thanks for reading.
I really enjoyed making this for you and sincerely hope you find it useful.