AI Can Generate Creative and Better Images

As we get used to an AI-driven world, it is becoming harder to differentiate human skills from AI skills. Already, OpenAI’s GPT-3 language model can create smooth and meaningful text that reads like human-written text.

Many questions abound about the long-term capability of AI. Questions like “can AI generate photo?” are already getting answers. And the answers are quite positive.

In this article, we look closely at image creation and artificial intelligence.

person holding black dslr camera
Photo by Inkredo Designer on Unsplash

Can AI Generate Photo?

The simple answer to this question is a resounding YES! AI can generate photos. AI and machine learning (ML) have gotten so advanced that Picasso and Von Gogh will be envious of the output. Models have been built to generate photos and images in any form. From random face generators to text-to-image tools, there are quite a lot of tools available to generate images.

How Can AI Generate Photo?

Now that we’ve established the fact that AI can generate photos, it’s time to understand how it does it. To start with, AI can use different systems or models to create images. We’ll explore two of the most common types – GANs and Diffusion models.


GANS (Generative Adversarial Networks) is an ML system that pitches two neural networks against each other. New York Times once described GANs as a game of cat and mouse.

As indicated earlier, there are two competing (adversarial) networks in a GAN setup – the generative network and the discriminative network. The generative network attempts to create fake images.

On the other hand, the discriminative network assesses the generated photo to determine its authenticity. In the process, the generative network learns how to generate believable yet fake images. Once the generative network has a handle on how to create fake images, the discriminative network eventually accepts the fake image as authentic. 

A prominent example of art generated with GANs is the Portrait of Edmond Bellamy. The art piece is a print on canvas measuring 700 mm x 700 mm. The algorithm that painted the portrait was trained with a data set of over 14,000 portraits. These portraits were painted between the 14th century and the 20th century.

Diffusion Model

Diffusion models are transformer-based generative models. In simple terms, this model destroys to create. It takes a piece of data and adds noise to it until it is no longer recognizable. Subsequently, the model tries to recreate the data to its original form. This process allows the model to learn to perform various tasks, including image generation.

OpenAI’s Dall-E 2 and Google’s Imagen are two of the most popular AI image generators that use the diffusion model. They create highly realistic images with great attention to detail.

These tools have a high level of language understanding that makes the text-to-image process a joy to behold. You get the best of both worlds – quality image results and a deep level of language understanding.

The Future of AI and Image Generation

AI might seem so advanced that one might be tempted to think we’ve reached the pinnacle. As true as this assertion sounds, there are still some improvements to be made.

We have AI writing software like INK that has made many forms of writing easier for people. We can envisage this sort of liberalization in the area of AI image generation as well. 


AI will change the way we work. For some, it’s already changing it. People can now give form to their imagination with AI image generators.

These AI machines can simulate the creative process by using randomness. They can also grow and improve with more input. With this, they can visualize things that are not currently within their capability.

Abir is a data analyst and researcher. Among her interests are artificial intelligence, machine learning, and natural language processing. As a humanitarian and educator, she actively supports women in tech and promotes diversity.

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