AI image generators are a lot of fun, but they can also be dangerous. They can create misleading images that could have real-world influence, or harmful ones that are sexually graphic or violent.
So how do they work? Learn how these tools take a simple text prompt and transform it into an amazing picture.
Neural Networks
An AI image generator’s neural network uses a series of algorithms to analyze and interpret the user’s input. It takes in the different elements of the text prompt—for example, “a red apple on a tree”—and converts it into a numerical representation that represents the various elements and their relationships. This is the map that guides the generator as it creates an image to match the prompt.
As the generator produces images, the discriminator—the network that tries to distinguish real and fake images—gradually learns to better identify what is authentic. This is achieved through a process called adversarial training, where the generator and discriminator work simultaneously to improve each other’s abilities. The generator attempts to fool the discriminator by creating more realistic images, and the discriminator teaches the generator what authentic images look like. This feedback loop is the foundation of how AI image generators train and improve themselves.
In early versions of the technology, image generators used generative adversarial networks (GANs), which were essentially two neural networks working concurrently: one producing images and the other trying to judge their accuracy. The generator was trained to create realistic-looking graphics from random noise, while the discriminator tried to determine if the images were real or not. The GANs were able to gradually teach each other how to produce realistic images, and eventually the generator was able to trick the discriminator into believing that its images were real.
Newer versions of image generators take a different approach to generating images. A popular option is a diffusion model, which begins with a field of random noise and iteratively edits the image to match the prompt. It’s sort of like looking up at a cloudy sky and finding a cloud that resembles a dog—and then snapping your fingers to keep making it more and more dog-like.
While AI image generators are becoming increasingly powerful and practical, it’s important to understand how they work and what their limitations are before attempting to use them for commercial purposes. Choosing an AI image generator that prioritizes quality and satisfies your image creation needs is crucial to ensuring that the results meet or exceed your expectations. For example, using an AI image generator that is capable of handling complex prompts while delivering high-quality images at scale can help businesses streamline the product design and marketing processes.
Text Prompts
An AI photo generator works with text prompts, which are commands that dictate what the AI platform will render. This information can be a short description of the desired image, or it can be an extensive and detailed map of what the model should look like.
This process begins with a natural language prompt that is converted into machine-friendly parameters through a Natural Language Processing (NLP) system. This transformation is known as prompt engineering, and it helps the AI understand the meaning of each word in your text. This information is then fed into a diffusion model, similar to OpenAI’s DALL-E and Stable Diffusion, which renders an image based on its understanding of the prompt.
While the results produced by these models are stunning, there are a few issues that can arise when using these tools. First, they can lack originality and creativity, as they are based on existing patterns and data. Second, they can produce images of inconsistent quality. This can be frustrating for artists and designers who want to have a high degree of control over their work.
One solution to these problems is to use a generator that allows you to upload your own reference images. This will help the AI to better understand the desired image, and it can then create an image that is closer to what you envisioned. This feature is available in some image-to-image AI tools, including our own Let’s Enhance Image Generator.
Some AI image generators also allow you to submit other images to help the algorithm learn more about what it is supposed to be rendering. This is known as out-painting, and it can be a powerful tool for artists who need to make specific adjustments to their works. For example, the image above shows a painting by Johannes Vermeer that has been out-painted by DALL.
As the technology evolves, it’s important to keep in mind the limitations and possibilities of AI image generators. However, it’s still an exciting tool that can be used to create unique and creative images. It’s important to experiment with different tools and to find the one that best meets your needs.
Discriminator
AI image generators enable users to type in text prompts and receive a high-quality, original graphic back within seconds. It’s a fascinating concept, but how do these tools actually work? In this blog post, we’ll take a closer look at the mechanics of these standout artificial intelligence tools and see how they create such detailed, complex images.
Most of the standout AI image generators are based on neural networks that are trained to interpret the data inputted by the user. This data is then transformed into a new image using the same process as an image encoder. A popular example is a neural style transfer model that learns to imitate the style of one image on another. The other main approach is to train a neural network to recognize certain patterns in images and generate new ones that emphasize these specific patterns. This is the mechanism behind image synthesis models like Deep Dream and variational autoencoders.
A key feature of these AI image generators is their ability to distinguish real and fake images. This is accomplished through a process known as adversarial training, which involves training two different neural networks simultaneously. The discriminator network tries to distinguish between real and generated data, while the generator network aims to fool the discriminator into thinking that its fake data is real.
During this process, the generator network updates its weights in a feedback loop to improve its performance. It does so by showing the generated data to the discriminator and asking it whether it thinks that the data is real or fake. The discriminator then adjusts its parameters to help the generator learn, and vice versa. This cycle continues until the generator can produce data that is indistinguishable from real data.
This dynamic between the generator and discriminator is at the core of most AI image generation techniques. Some of the most advanced are generative adversarial networks (GANs), which pair two neural networks to train them both to produce realistic images and distinguish between real and fake. To get the best results from GANs, they are typically trained on datasets that include both real and generated images.
Training
Generative image generation uses deep learning algorithms to create new images that are unique and realistic. They can be used for a wide variety of creative projects, including art and design. For example, an AI image generator can create a stylized landscape, a portrait, or even a unique product mockup using the style of another image.
The process of creating an image starts by training the neural network to recognize patterns in a dataset. This process is called “modeling” the image. Once the model is trained, it is ready to produce an image based on any text prompt. The process of producing an image is a iterative cycle of modeling, training, and testing. Each time the model produces an image, the discriminator evaluates it and compares it to real images in a dataset. If the generated image is similar to a real photo, the discriminator will provide feedback and tell the generator to improve its next image.
After a few iterations, the generator will be better equipped to produce an image that matches your prompt. The image will also be more likely to match the style of real-world images.
In addition to generating images, some AI image generators can also be used for augmented reality (AR) and virtual reality (VR). In AR, the generated image is overlayed on top of the user’s view of the real world. In VR, the generated image is displayed inside a headset.
The ability to generate high-quality, creative imagery is a powerful tool that can revolutionize the way we work. However, it’s important to keep in mind the limitations of AI image generators and how these limitations may impact your business.
One challenge is detecting AI image manipulations, or “deepfakes,” which are fake images that use artificial intelligence to replicate the appearance of real photos and videos. These images are often created with the intent of spreading misinformation or satire, and can be difficult to distinguish from authentic content. For example, in March 2023, a series of deepfake images illustrating a fictional arrest of President Donald Trump spread across social media and were shared by users who believed them to be true. This type of manipulation requires a level of sophistication that many artificial intelligence models are currently incapable of achieving.
