Customize Your Experience with AI-Generated Imagery
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Updated Sept. 20, 1:40 p.m. | Originally published Dec. 17, 2022
Step into a world where your imagination collides with cutting-edge technology. Artificial intelligence (AI) is now empowering anyone with a smart phone to create breathtaking and unique scenes simply by providing a few words of description.
With AI-generated imagery, the possibilities are truly astonishing. The resulting images can be crisp, beautiful, fantastical, and even eerily realistic. OpenAI’s DALL-E 3, the latest image generator released on Wednesday, brings improved text rendering capabilities, refining elements such as billboards and office logos.
Curious about how it works? Keep scrolling to unravel the step-by-step process.
AI-Generated Images, Unleash Your Creativity
Try out AI-generated images by selecting a prompt from the options below:
- a photo
- Van Gogh
- stained glass
- a magazine cover
This groundbreaking technology of AI-generated artwork, however, brings forth a multitude of legal, ethical, and moral questions. The models used to train the AI are based on raw data sourced from the internet, resulting in image generators inadvertently echoing the biases prevalent online. This can perpetuate incorrect assumptions about race, class, age, and gender.
Moreover, these training data sets often include copyrighted images, leading to controversy as artists and photographers find their work “ingested” by computers without permission or compensation.
At the same time, there is a significant risk of creating and amplifying disinformation. It is crucial to understand the inner workings of this technology, whether it’s creating a never-painted Van Gogh or depicting events like the Jan. 6 attack on the U.S. Capitol that no photographer captured.
Artificial intelligence is advancing at a pace that society struggles to keep up with, leaving behind legal, ethical, and moral quandaries.
About This Story
The Washington Post has generated the AI images presented in this article using stable diffusion 2.0. Each image was created using the same settings and seed, meaning the starting point (“noise”) was identical for all images.
The animations you see on this page demonstrate the actual de-noising process. We have customized the stable diffusion code to capture and save intermediate images as the de-noising progresses.
In addition to interviews with researchers and a detailed examination of the diffusion model, The Washington Post analyzed the images used to train stable diffusion for the database section of this story. The images selected for this explanatory piece either came from stable diffusion’s public domain database, were licensed by The Post, or closely resembled the images used in training. It’s important to note that the database contains copyrighted images that we do not have the rights to publish.
Editing by Karly Domb Sadof, Reuben Fischer-Baum, and Ann Gerhart. Copy-editing by Paola Ruano.
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