Text-to-Image Generation is an AI feature that creates images based on a given prompt. This type of image generator has many impressive capabilities and can be a useful tool for creative people.
However, some users are concerned that the images produced by these tools may reinforce societal biases and stereotypes. This can be avoided by ensuring that the image-generating AI has access to comprehensive and properly annotated data.
1. Realistic Images
A state-of-the-art deep learning text-to-image model can create realistic images based on natural language descriptions. These models can save users a lot of time in creating a specific image or concept. For example, a blogger can quickly get the right picture of a dinosaur to go with their blog post, without having to search for one or draw it themselves.
The technology that allows this to happen is called GANs (Generative Adversarial Networks). These networks have two components — the Generator and the Discriminator. The Generator network generates images based on the prompt, while the Discriminator compares them to real-life examples and penalizes the generator if it is too far from reality. This process is repeated until the generator produces a result that is close enough to be accepted by the Discriminator.
2. Creative Doodles
Using this type of AI technology to create creative content can have a positive impact on society. However, it is important to be mindful of its potential to increase the spread of deepfakes and fake content.
Typically, these algorithms work by converting the text description into a meaningful representation (perhaps through word embeddings) and then generating an image that matches this representation. Using this technique, you can produce images that are more realistic or more abstract, depending on your preferences.
For example, Midjourney uses a variational autoencoder model to generate images from text prompts. This allows users to add context to their conversational chatbots by providing contextual images that match the user’s description. This is a great way to add personality to the conversation and enhance customer engagement.
3. 3D Renderings
In the world of text-to-image generation, 3D renderings are some of the most impressive. These models can transform a line of text into an image that looks like it was drawn by hand or taken from a video game.
For example, OpenAI’s DALL-E software can create images from text prompts by generating meaningful word embeddings and translating them into objects in the image that imply the same meaning. This kind of multimodal modeling is an important advance in the broader field of machine learning.
However, the same kind of model can ingest and replicate prevailing social biases and stereotypes that exist in many corners of the internet. For this reason, it’s imperative that we have tighter filtration processes and promote digital literacy to help users avoid the toxic content these models may produce.
5. Images for Marketing
For marketers, text-to-image generators can be a useful tool. They can be used to create engaging visuals for social media campaigns quickly and without having to worry about copyright violations.
AI image generators have come a long way in recent years. They can now generate incredibly realistic drawings, 3D renderings, and more from a single prompt. They can also alter existing photos and turn them into fantasy illustrations or paintings.
While this technology is impressive, it does have some limitations. For example, it can reproduce prevailing biases in the images it produces. For example, if you ask it to produce pictures of flight attendants, it will produce a majority of women. Similarly, if you ask it to produce pictures for a CEO, it will likely show only white men.