Stable Diffusion – Oil Painting Vol.1

Experimenting on nice oil paints generated art. Let me know if you need help for your project. I’ve been experimenting with some prompts and understanding what it takes to make very impressive art.

Prompt – “seed: 918873146, Professional, oil painting of establishing shot of canal surrounded by verdant blue modern curved rustic Greek-tiled-building”

So, what IS “Stable diffusion”

Stable Diffusion is a process in Artificial Intelligence (AI) where a model’s parameters are updated gradually over time in response to incoming data. This approach is used to avoid the issue of overfitting, where a model becomes too closely tied to the training data and therefore fails to generalize well to new, unseen data.

By allowing the model’s parameters to change slowly, Stable Diffusion helps to ensure that the model’s updates are well-behaved and don’t cause rapid fluctuations in its behavior.

Stable Diffusion is used in a variety of applications, including recommendation systems, natural language processing, computer vision, and reinforcement learning. In recommendation systems, for example, Stable Diffusion is used to adjust the parameters of a model over time in response to user feedback, so that it can continuously improve its recommendations based on the evolving preferences of users.

In natural language processing, Stable Diffusion is used to train models that can process and understand human language, by gradually adjusting the model’s parameters in response to incoming text data.

Generate art with Machine Learning Algorithms

“Generate Magic the Gathering Card Art with StyleGAN”. So, you can teach an “AI robot” (“Machine learning”) to help you generate an art based on what you teach it. The more quality input you have, the better the results are 😉.

If you have an idea about a project, let me know at hello@matichek.com 😀

First example I played around is Magic The Gathering cards.

The second experiment was pots.

Good starting places are https://www.deeplearningbook.org/, http://neuralnetworksanddeeplearning.com/chap1.html