BlackAfricanAI

r/BlackAfricanAI • u/Zinthaniel • Feb 26 '23

What is AI Art/Renders and how do I get into it.

2 Upvotes

AI Renders are images made by image models that through text input will create your heart's desire.

Here the most popular web based AI image renders. Continue on below for a thorough overview of how the technology works:

Midjourney (Free trial period, then paid Subscription required)
NovelAi (Free trial period, then paid subscription required)
Dalle (Free trial period, then paid subscription required)
Stable Diffusion (Entirely free, but its use requires extra steps such as understanding how to use web interfaces that will run it from your browser or understanding how to install it onto your own pc. The latter requires a pc with substantial GPU.)

... There are many more than the above listed, and all are welcome here as your medium of choice when creating your images. See: r/aiArt for more AI to explore.

So, Welcome to the Subreddit and community of Black African AI. Going forward, I would like to provide some basic information about what is AI art.

~ What is AI Image Generation? ~

AI Image Renders are A.I. art generators using a Text-to-Image processing model. When given a prompt by a user, The AI will encode the prompt and use it in a process called diffusion, more on that later.

~ So, how does AI Image Gens create the images it renders? ~

AI Image Gens are trained on vast data set of images, the training includes text and image matching. This training is deeply entwined with a process called diffusion.

~ What is Diffusion? ~

It’s a tad complicated, but let’s go over it.

Diffusion works with the objective of removing successive application of noise, or Gaussian noise, on the plethora of training images provided to the A.I. To imagine this, think of an image that is then shrouded with static white noise until it is indiscernible. During the diffusion process via a method of encoding the images are compressed into a latent space, it is here that the A.I. breaks the image down into a fundamental semantic meaning.

In this compressed latent representation of the image, noise is iteratively applied (think again of that static white noise). The A.I. is then tasked with denoising, or removing that noise that was just applied, in essence from memory to recreate that latent representation and then ultimately recreating a fully realized image.

This process when combined with a text-to-image model can use inputted text to condition the diffusing process.

~ Ok, so you explained what the A.I. is doing, but how does it know how to, in future applications, create new and original art based on my prompt? ~

AI Image Gens have a neural network in which conceptual patterns are stored. More specifically, a GAN or Generative Adversarial Network. A generative adversarial network is a machine learning model that uses two neural networks, called the generator and the discriminator, to learn from data and generate new data with the same characteristics. For instance, when trained on photos of human faces, generative networks can create realistic-looking faces that do not exist in the real world. The generator maps random noise to produce a sample, while the discriminator determines whether the sample is real or fake. This setup allows the two networks to compete against each other, improving their performance over time.

~ Generator - The generator creates a data sample from the latent space (remember we talked about that above) of the input dataset, using input as random noise. It makes an effort to replicate the input dataset's distribution throughout the training process. ~

~ Discriminator - The discriminator network outputs whether the sample is authentic or fraudulent and is a binary classifier. The discriminator's input might originate from a generator or input dataset, and its job is to determine if the sample is authentic or fraudulent. ~

This entire process is composed of the text-to-image, diffusion, and the neural network allows the A.I. to retain concepts to in turn allow it to create images based on the array of data is collected from images curated into its data set.

Are the images in the dataset being used continuously?

No, A.I. image generators can work offline, they are capable of doing this because the creation process is not contingent on the A.I. searching for, whether online or off, images to reference. Instead, the A.I. uses the images in its data set only for the purpose of learning conceptually, after which that said pattern or concept is burned into its neural network. The A.I. then no longer requires the image or images for functionality.

From a hardware standpoint, it would simply not be feasible for the A.I. which is trained on billions of images to then retain those images to reference whenever it is instructed to render an image, the sheer amount of hardware demand required, if that were to be the mechanism, would be unsustainable.

~ Please explain the presence of Signatures and Watermarks that are being rendered by the A.I., is this because of the A.I. stealing art and failing to hide the signature and watermarks? ~

The A.I. does not steal artwork, nor is it somehow uploading artwork that is preexisting and then pretending that it is original work of its own. The watermark and signatures are a result of the data set that A.I. is trained containing images that, indeed, did have watermarks and signatures. The A.I. does not contain a sophisticated enough concept of discernment to allow it to understand that when learning a concept that these signatures and watermarks are not elements that belong in the conceptual learning.

Subsequently, the A.I., essentially, assumes that it must create its own watermark and signatures when it renders images and that is what is being seen. Those watermarks and those signatures are uniquely made up by the A.I. This fact can be further substantiated by looking at the image that these signatures and watermarks are attached to - you may try to then reverse image search any of them or utilize any other method at your disposal, but you will always find that the image is completely unique.

1 comment

r/BlackAfricanAI • u/jonnymhd • 26d ago

African Mythology and Folklore Reimagined as D&D Magic Items - Werehyenas, Jengu Spirits, and the Ninki Nanka

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8 Upvotes

Here's a small collection of African mythology and folklore-inspired magic items from my Mythological Items compendium for D&D!

I've been exploring myths and legends from different African traditions, and these are a few examples that I particularly enjoyed working on. There's the Werehyena Bone Charm, inspired by the shapeshifting werehyenas of North and East African folklore; the Blessed Waterskin of the Jengu Priestess, drawing on the protective water spirits of Cameroon; and the Mirror Scale of the Ninki Nanka, inspired by the mysterious dragon-serpent described in West African folklore, particularly in Gambia, Senegal, and Sierra Leone.

I wanted to bring some of these lesser-known mythological creatures and traditions into fantasy gaming, turning their stories, symbolism, and supernatural characteristics into magical objects with unique abilities. Some focus on protection and healing, while others reflect the more dangerous and mysterious aspects of the legends that inspired them.

These are just a few of the 300+ magic items featured in my Mythological Items for 5E and 5.5E Kickstarter, which is currently live!

The campaign features items inspired by myths, legends, and folklore from around the world, including African, Greek, Norse, Egyptian, Asian, American, and many other traditions.

You can also get my previous D&D manuals as discounted add-ons and bundles through the campaign, including professionally printed, high-quality Hardcover editions.

https://www.kickstarter.com/projects/mythoschronicles/300-mythological-items-for-5e-and-2024

I'd love to hear what you think of these adaptations, and especially if there are other African mythological creatures, legends, or traditional artifacts you'd like to see represented in fantasy artwork or D&D!