Fwd: SDXL created new image of the day - The inner workings of AI Image Generators and their inner workings Written by Google Bard and Image by NightCafe SDXL

AI image generators work by using a type of machine learning called generative adversarial networks (GANs). GANs are made up of two neural networks: a generator and a discriminator.

The generator is responsible for creating new images, while the discriminator is responsible for determining whether an image is real or fake. The generator is trained on a large dataset of real images, and it learns to create images that are similar to the images in the dataset. The discriminator is also trained on the real image dataset, and it learns to distinguish between real and fake images.

The two networks are trained together in a process called adversarial training. In adversarial training, the generator tries to create images that are so realistic that the discriminator cannot tell them apart from real images. The discriminator, on the other hand, tries to become better at distinguishing between real and fake images.

Over time, the generator and discriminator networks become better and better at their respective tasks. The generator learns to create more realistic images, and the discriminator learns to distinguish between real and fake images more accurately.

Once the generator and discriminator networks are sufficiently trained, they can be used to create new images. The generator can be given a text prompt, such as "a cat sitting on a couch", and it will create an image that matches the description in the text prompt.

The quality of images generated by AI image generators is constantly improving. In recent years, there have been some impressive examples of AI-generated images that are indistinguishable from real images. However, there are still some challenges that need to be addressed, such as the ability to generate images of people and objects that are not commonly seen in real life.

Here are some of the most common types of AI image generators:

• Diffusion models: Diffusion models are a type of GAN that adds noise to an image until it becomes completely obscured. The generator then tries to remove the noise and create an image that is similar to the original image.
• Variational autoencoders (VAEs): VAEs are a type of neural network that learns to represent an image as a distribution of latent variables. The generator can then sample from this distribution to create new images.
• Generative transformer models: Generative transformer models are a type of neural network that learns to generate text descriptions of images. These descriptions can then be used by the generator to create new images.

AI image generators have a wide range of potential applications, such as:

• Creating realistic images for use in movies, video games, and other visual media.
• Generating images for use in advertising and marketing.
• Helping artists and designers create new ideas.
• Assisting scientists and researchers in their work.
• Creating educational materials.
• Making it easier for people to visualize things.

As AI image generators continue to improve, they are likely to have a major impact on many different industries.

The best way to make money off AI generated images right now depends on your skills and resources. Here are a few ideas:

• Sell prints or digital copies of your AI art online or in galleries. This is a traditional way to make money from art, and it can be a good option if you have a portfolio of high-quality AI art. You can sell your art on your own website or through online marketplaces like Etsy or Society6.
• Offer commissions to create AI art for clients. This is a good way to make money if you have experience with AI art and you can meet the specific needs of your clients. You can find clients through online platforms like Fiverr or Upwork, or you can network with people in your local art community.
• Licensing your AI art for use in advertising, design, or other commercial applications. This is a more passive way to make money from AI art, but it can be a good option if you have a high-quality portfolio of images that are relevant to a particular industry. You can find licensing opportunities through online platforms like Shutterstock or Adobe Stock.
• Create and sell custom AI art generators to individuals or businesses. This is a more technical option, but it can be a good way to make money if you have the skills to develop and train AI art generators. You can sell your generators through your own website or through online marketplaces like Gumroad or CodeCanyon.

It is important to note that the market for AI art is still in its early stages, so it is not yet clear which of these methods will be the most profitable in the long run. However, all of these methods have the potential to generate income from AI art, so it is worth exploring them if you are interested in making money from this technology.

Here are some additional tips for making money off AI generated images:

• Focus on creating high-quality images. The quality of your images is the most important factor in determining how much money you can make. Make sure your images are clear, realistic, and creative.
• Market your art effectively. Once you have created some high-quality AI art, you need to market it effectively. This means creating a strong online presence, promoting your art on social media, and networking with other artists and art professionals.
• Be patient. It takes time to build a successful business around AI art. Don't expect to make a lot of money overnight. Just keep creating great art, marketing your work effectively, and be patient.