How I made $3000 in 3 weeks selling AI-generated art? π°
Last week I showed you how you can use VQGAN+CLIP to generate interesting images based on text prompts.
Now, I'll tell you how I sold some of these as NFTs for more than $3000 in less than 3 weeks.
Let's go π
Background
I've been interested in NFTs for 2 months now and one collection I find interesting is @cryptoadzNFT. What's special about it is that the creator @supergremplin published all of the art in the public domain. This spurred the creation of many derivative projects.
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The Idea π‘
My idea was to use VQGAN+CLIP to create interesting versions of the CrypToadz. So, I started experimenting with my own toad #6741.
I took the original NFT image as a start and experimented a lot with different text prompts. The results were very promising!
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Monetizing π°
But how to sell these? I got inspired by the GelToadz project by @DannyZareto. People that own a CrypToad would be able to commission an AI Toad from me.
I also wanted people to be able to add some personal touch to the AI Toad by specifying some custom theme
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Website π
I hacked a minimal single-page website with @tailwindcss and launched it on @Netlify in a day. For orders, I used a free Airtable form where people could enter their CrypToad ID, wallet address, and their custom theme. Keep it simple!
I decided to go with an incremental price:
βͺοΈ First 10 Ai Toadz cost 0.05 ETH ($230)
βͺοΈ The next 10 Ai Toadz cost 0.1 ETH ($460)
βͺοΈ After that each Ai Toadz cost 0.2 ETH ($920)
The update of the price on the website is manual by me changing the HTML π€·ββοΈ
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Launch π
I launched it on Twitter and a couple of Discord servers and started getting 1-2 orders per day.
An important step was to also get my project listed on toadztoolz.com - a website acting as a directory of all CrypToadz related projects.
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Creating NFTs π¨
I use a popular Colab notebook modified so I can easily run multiple experiments. For each order, I try different prompts and styles until I have 2-3 variants I like and that fit the theme provided by the client.
Experiments for #6067 "War never changes"
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After that, I contact the client and show them the variants I have. In some cases, I make some additional experiments based on the feedback. Some people want specific features to be visible, while others like particular styles more.
This is the artistic part of the process!
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In some rare cases getting the desired results wasn't easy, so I had to do some tricks. I either modified one of the intermediate images to enforce a particular look or created two separate versions. In the video, I had to blend both versions using DaVinci Resolve.
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Compute π»
Some toadz are easy, but some require a lot of experimentation with the prompt. To be efficient, I define multiple experiments and let them run for hours in the background. I subscribed to Google Colab Pro+ so I can run 2 notebooks in parallel on a P100.
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NFT βοΈ
After an AI Toad is ready I create a video NFT using OpenSea. It is really easy and free! You need to provide:
βͺοΈ The video
βͺοΈ A thumbnail
βͺοΈ Name
βͺοΈ Description
βͺοΈ Optional properties (I didn't specify any)
βͺοΈ Collection
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Collection ποΈ
Creating a collection for the AI Toadz NFTs is also easy - you need a description and some images for banner, logo, and social media previews.
I then list the NFT for the agreed price as a private sale - only for the wallet address holding the original CrypToad.
OpenSea uses a process called lazy minting, so there are no gas fees for the creator! The NFT is minted on the blockchain with the first sale.
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Orders π
Now some statistics on the current state of the orders:
βͺοΈ 15 valid orders for a total of 1 ETH ($4600)
βͺοΈ 11 AI Toadz actually sold for a total of 0.7 ETH ($3300)
βͺοΈ 4 AI Toadz in progress
βͺοΈ Current price 0.1 ETH ($460)
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Summary π
To summarize my process:
βͺοΈ Create a simple website and AIrtable form to collect orders
βͺοΈ Experiment with different prompts for VQGAN+CLIP
βͺοΈ Iterate with the client if needed
βͺοΈ Create NFT on OpenSea and sell it
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Next βΆοΈ
I don't plan any big marketing effort. If orders stop coming I may post more about them on Twitter/Discord and also create some free ones for members of the community to boost the visibility. This is supposed to stay a small side-project.
No roadmap - only !vibes π
The AI Toadz πΈ
I shared some examples across the thread, but if you want to browse all of them check out the collection on OpenSea:
In their latest paper, they introduce the so-called verifiers. The generative model generates 100 solutions, but the verifiers select the one that has the highest chance of being factually correct.
You've probably seen these strangely beautiful AI-generated images on Twitter. Have you wondered how they are created?
In this thread, I'll tell you about a method for generating art with ML known as VQGAN+CLIP.
Let's jump in π
Short History π
In January @OpenAI publicly released CLIP, which is a model that allows matching text to images.
Just days after that, some people like @advadnoun, @RiversHaveWings, and @quasimondo started experimenting using CLIP to guide the output of a GAN using text.
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OpenAI published an image generation model together with CLIP, called DALL-E, but without the full code and the pre-trained models.
The results from guiding StyleGAN2 or BigGAN with CLIP aren't as accurate as DALL-E, but they are weirdly artistic.
There is a problem with how value is distributed in online communities today. It seems we take the status quo for granted and don't discuss it much.
The people that create most of the value, get none of the money! Only badges...
Thread π
Online communities
I'm talking about platforms like Twitter, Reddit, Stack Overflow etc. They're wonderful places, where you can discuss interesting topics, get help with a problem, or read the latest news.
However, the people that make them truly valuable receive nothing π
It usually looks like this:
βͺοΈ Company creates a web 2.0 platform
βͺοΈ Users create content and increase the value
βͺοΈ Company aggregates the demand
βͺοΈ Company monetizes with ads and subscriptions
βͺοΈ Company gets lots of money
βͺοΈ Creators get badges, karma and virtual gold
This is the formula for the Binary Cross Entropy Loss. This loss function is commonly used for binary classification problems.
It may look super confusing, but I promise you that it is actually quite simple!
Let's go step by step π
The Cross-Entropy Loss function is one of the most used losses for classification problems. It tells us how well a machine learning model classifies a dataset compared to the ground truth labels.
The Binary Cross-Entropy Loss is a special case when we have only 2 classes.
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The most important part to understand is this one - this is the core of the whole formula!
Here, Y denotes the ground-truth label, while ΕΆ is the predicted probability of the classifier.
Let's look at a simple example before we talk about the logarithm... π
ROC curves measure the True Positive Rate (also known as Accuracy). So, if you have an imbalanced dataset, the ROC curve will not tell you if your classifier completely ignores the underrepresented class.