Creating an AI Onlyfans: The Power of Computer Science

This article is a summary of the YouTube video ‘Making an AI Onlyfans with Computer Science’ by nang

Written by: Recapz Bot

Written by: Recapz Bot

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How does it work?
The video discusses creating an AI model for generating images based on text descriptions using stable diffusion algorithm, yielding promising initial results of various objects; errors resolved with better dataset and fine-tuning using “Laure” models; training involves machine learning and multiple variables; terabytes of data and “Clip” models can be used; denoising enhances accuracy; personalized AI models created for the same person; the generated AI model can be used for interaction on social platforms; creator’s experience and reactions received shared; ethical concerns lead to the shutdown; video concludes with code, updates, and gratitude for viewers.

Key Insights

  • The video discusses creating an AI-generated OnlyFans model that can generate images based on text descriptions.
  • The core algorithm used is called stable diffusion, which turns text into generated images.
  • The initial results of stable diffusion seem promising, generating images of dogs, sculptures, and rocks in the ocean.
  • Errors in the generated images are due to using the wrong dataset, which can be improved by training on a more fitting dataset.
  • The missing piece to improve the realism of generated faces is using additional trained models called "Laure" to fine-tune image generation.
  • The process of generating AI images is based on machine learning, specifically using an objective function and gradient descent to improve accuracy.
  • The video explains the concept of training AI on multiple variables or dimensions to differentiate between objects.
  • The AI model used in the video requires training on terabytes of data, but available models like "Clip" can be used instead.
  • The denoising process in stable diffusion helps to transform noisy images into accurate representations based on text prompts.
  • Training a personalized AI model focuses on generating images of the same person and involves using AI to create AI.
  • The AI model generated can be used on Twitter and other platforms for interaction and gaining followers.
  • The video shares the creator's experience with his AI-generated OnlyFans account and the reactions received.
  • Due to ethical concerns and the desire for a different coding achievement, the creator decides to shut down the AI-generated OnlyFans model.
  • The video concludes with the code and instructions provided in the description, updates on the creator's life and goals, and appreciation for the viewers and subscribers.

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Transcript

Neil deGrasse Tyson is most famous for saying that it’s the curious people who change the world. However, what has often gone under the radar is another profound quote from him, which is, I love AI-generated Asian girls. So today we’re going to be fulfilling this by making an AI-generated OnlyFans. We’re going to be making an AI-generated model, girlfriend, idol, whatever you want to call it, where you can input any text description and the AI will generate a picture of just that. These are actually getting really popular on Twitter and other sites. There’s both girl and guy versions of this and yeah, they make a lot of money. But yeah, today we’re going to be making it and also just explaining how all that works. So let’s head right in.

So the core of making all this work is something called stable diffusion. It’s an algorithm that is able to turn text into generated images. We’re going to be going through all the explanation stuff later, but first let’s just try out what stable diffusion can do. Here we have the vanilla stable diffusion code and let’s try it out. So we can pretty much generate anything. Like what do you want to see? Let’s try dog on the beach. Okay, now let’s try sculpture in France. All right, seems pretty good to me. And now let’s try rocks in the ocean. And yeah, those are rocks in the ocean if I’ve ever seen them. But who cares? Let’s make some money.

Okay, so let’s try this out right here. Hey, yo, what the hell is this? I mean, why does she have boogers coming out? So this is obviously not going to cut it. So let’s try a prompt from online. Oh, god damn. So this is not working right now because we’re using the wrong data set. We’re using a generic one right now, like one that’s based on real life stuff. But we need a data set that’s more trained for what we’re trying to make. So let’s use a model trained on a more fitting data set and try it again. Damn. We can also try other stuff like outside in a winter coat, inside in the living room next to a fireplace. And lastly, anime beach episode. Okay, so it’s not that bad right now. But I’m not going to lie, sometimes the faces don’t look that realistic. So the missing piece to fix this is something called a Laura. These are additional trained models, which we’re going to be covering how that works in a little bit. But they allow you to fine tune your image generation into a style. Like, for example, here we have the model in an art style. But for our case, we’re going to be using Laura’s trained on AI generated faces. And the end result is that the face is a lot more realistic. But yeah, with all this, I would say that we’re at about 80% of the AI girl image generation capabilities. And you don’t believe me? Roll the intro.

Dude, what are you even watching right now? Anyways, now you need to learn. First, what even is machine learning exactly? Well, it’s actually not that different than how a kid learns. You ask them a question like, “What is this?” and they come up with an answer based on what their brain tells them is most likely. If they’re right, we’ll reward them. And if they’re wrong, we’ll punish them. And they’ll improve for next time.

But how does the brain or a computer even make a guess on what something is? Well, we can think of it like this. Let’s say you’re trying to tell the difference between a gold party balloon and a tree. So a kid or a computer can think, how green is it to differentiate it? As we can see here, this is a one-dimensional space because there’s only one axis. But we can tell the difference between the gold party balloon and the tree. But now let’s make it a green balloon and a tree. Now we can’t differentiate them based on our one axis. So we need another variable, which can be roundness. Now we’re up to two dimensions. And we’re pretty good because we can tell the difference between the two. But with more data, there could be a tree that’s round and a balloon that’s not. So in this case, our brain or the machine learning model will have to add another factor, maybe let’s say shininess, as balloons usually have a shiny spot and trees don’t. Now, as we can see, we’re at a three-dimensional space. And with just these three dimensions, a kid and a computer can determine trees from balloons pretty well. But now let’s say for differentiating a ton of different images and objects, we’re going to need more variables. When training an AI model, we’re asking it a question and telling it if it’s right or wrong. Each time, there’s a ton of variables within the model that will be tweaked in order to help it improve on the task next time. And how it does this is with a process called gradient descent. Basically, what it does is it uses a math equation called an objective function that will change the internal numbers in the right direction to make it more accurate guess next time. All these variables create a space that has way more than three dimensions. And the human brain can’t even picture a space with this many dimensions. With this,

This article is a summary of the YouTube video ‘Making an AI Onlyfans with Computer Science’ by nang