TL:DR
A Tutorial for generating a profile picture NFT project which uses Stable Diffusion to train and generate high quality and creative, aesthetically pleasing images. Unique and powerful workflows for character concept design, marketing and promoting the creative project using AI tools is included in this course. Gain the transformational power of Koan's unique creative methodology now!
In this tutorial, Multi-media Artist and Digital Art Curator KOAN aka Alexander H. Mitchell of DRP.IO fame, and Audio-visual artist MECHAGEN aka Technical Philosopher Issiah B. Burckhardt will guide you, through a set-by-step video guide in how to create a profile picture NFT project using Stable Diffusion, a text-to-image model that can generate realistic and diverse images from natural language descriptions, as well as a bunch of other cutting edge tools.
Join them live and take action to create your very own profile picture (PFP) NFT collection.
We will cover the following steps:
Table of Contents
1. Establishing rarity and public vote on art direction
2. Curating input data for the custom model and training a custom model
3. Using the custom model to output art
4. Combining custom model and an LLM to create the support media and promotional and marketing copy (copywriting)
5. Creating the website and promoting the NFT project
6. Curating the outputs from the custom model adding meta data
7. Writing a code in gpt which creates json files for all the artwork in order to prepare it for launch
8. Deploying the collection, creating the contract, the OpenSea page and preparing for launch
1. Establishing rarity and public vote on art direction
The first step is to define the goal and scope of your profile picture NFT project. What kind of images do you want to generate? What is the theme or style of your project? How many NFTs do you want to mint? How will you distribute them? How will you price them? How will you engage with your potential buyers and collectors?
One way to answer these questions is to conduct some market research and see what kind of profil picture NFT projects are popular or trending on platforms like OpenSea or Rarible. You can also look for inspiration from other sources, such as art history, pop culture, or your own imagination.
Another way to answer these questions is to involve your community and potential buyers in the decision-making process. You can create polls or surveys on social media platforms like Twitter or Discord and ask for feedback on your ideas. You can also use tools like Minty or Collab.Land to create a DAO (decentralized autonomous organization) that allows your community members to vote on various aspects of your project, such as the art direction, the rarity distribution, or the pricing strategy.
By involving your community in the early stages of your project, you can build trust and loyalty among your potential buyers and collectors. You can also generate some hype and excitement for your project and attract more attention and exposure.
2. Curating input data for the custom model and training a custom model
The next step is to prepare the input data for your custom model. The input data consists of images which generally represent or will guide in some way; what kind of images you want to generate. For example, if you want to generate profile pictures of cute robots, or our Skeleton Kings!!!
The quality and quantity of your input data will affect the quality and diversity of your output images. Therefore, you should try to feed in image data that is useful for posing a figure (control net-more on this later) or providing structure, thematic visual cues or a specific character for instance. You should also try to avoid input data that inconsistent or too different to the output you might want. But there are lots of interesting way to vary and experiment with generating and continuously refining the output untill were happy with the results.
Once you have your input data ready, you can use Stable Diffusion to train your custom model. Stable Diffusion is a text-to-image model that can generate realistic and diverse images from natural language descriptions. It uses a latent diffusion model (LDM), a kind of deep generative neural network that generates images by iteratively denoising random noise until a configured number of steps have been reached .
To train your custom model with Stable Diffusion, you need to install some software on your PC, such as Git, Miniconda3, Python, CUDA kernels, etc. You also need to download the Stable Diffusion files from GitHub and the latest checkpoints from HuggingFace.co. Then you need to run Stable Diffusion in a special python environment using Miniconda3, Or you can Run Visions of Chaos Software, a free open source application which installs most of the stuff we'll be using for you, specifically the Stable Diffusion Web UI called Automatic 1111.
You'll find the the details of your mimimum system specs requirements at the top including recomended system requirments: but basically you'll want to use a PC running window, a powerful gaming GPU and a decent amount of RAM and an SSD essentially.
The training process may take several hours or days depending on the size of your input data and the power of your GPU. You can monitor the progress of your training by checking the logs or by using TensorBoard. You can also stop the training at any time and resume it later.
3. Using the custom model to output art
The next step is to use your custom model to output art. To do this, you need to run Stable Diffusion again with some arguments that specify what kind of images you want to generate.
You can also use tags or special character arguments, (even emoji!) to customize your output images, such as changing the temperature (a parameter that controls the randomness of the generation), adding prefixes or suffixes (text snippets that modify the original text descriptions), applying filters (functions that modify the generated images), etc.
We'll use Stable Diffusion interactively by entering text descriptions in a graphical user interface (GUI) such as StableDiffusionGUI.
4. Combining custom model and an LLM to create the support media and copy
The next step is to create the support media and copy for your profile picture NFT project. The support media and copy are additional materials that complement your output images and provide more information about your project. They may include:
- A logo or banner for your project
- A website or landing page for your project
- A whitepaper or manifesto for your project
- A description or story for each output image
- A metadata file for each output image
You can use Stable Diffusion again to create some of these materials by using text-to-image generation or image-to-image translation tasks.
You can also use an LLM like GPT-4 or other natural language generation models to create some of these materials by using text-to-text generation tasks. For example, if you want to create a description for each output image based on its text description.
Get notified about the course announcement when we go live and well share details on the remaining steps and you can sign up for the course at a special launch/early adopter rate!
Join the waitlist by typing clicking on the 'JOIN WAITLIST' button below, add your email in the form below and we'll flick you an email 24 hours before we go live, then when we go live, so that you can join us and get access. We will issue ticketed access on a first come first served basis so act quickly as spaces are limited 👽
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