Reversediffusion.xyz visualizes how AI-generated images emerge from a complex mathematical space built on millions of human-created training images. By zooming in on this space as a context for everything the model produces, it provides a reversed perspective on generative AI.
Where do AI-generated images come from?
It’s widely known that generative AI relies on training data scraped from the public web, but the connection between this input and the final output often remains elusive. When you ask the model to create a picture of a landscape, the image you get isn’t copied from any single training image. Instead, it emerges from the complex mathematical space, also known as latent space, that lies between all the landscape images and their descriptions that the model has learned from. Although the output can’t be traced back to one exact source, it can be positioned somewhere within this space of training data to help understand the relationship between the model's input and output. Reversediffusion.xyz visualizes the latent space of the open-source Stable Diffusion model by probing it with the very training data used to build it. Like many other models, Stable Diffusion was trained on the LAION dataset—a massive collection of billions of images scraped from the web. While the full dataset is enormous, many models focus on a curated subset called LAION-5B Aesthetic. This subset includes only images that another human-trained AI model has rated as having high aesthetic quality. Though aesthetics are subjective, this filtering explains why generative AI often produces hyperrealistic and oversaturated images.
By encoding the 625,000 images from the LAION-5B Aesthetic >6.5 dataset into Stable Diffusion’s latent space, we can see where each training image fits within the model. Using the UMAP algorithm to reduce this complex, high-dimensional space to three dimensions creates a spatial map—a visual representation of the model’s structure that helps us contextualize its outputs.
On this page, you can generate new images with Stable Diffusion and see their position on the map. This shows where the synthetic image belongs in latent space and which training images—whose features may have influenced the output—are nearby. A generated image of a landscape might cluster with training images of landscapes, or it might group with images sharing a similar visual style, like oil paintings or black-and-white photos. Because image generation begins with a random seed, each landscape you create will be different and appear in different locations on the map.
Once your generated image is placed in latent space, you can explore the surrounding training images. Click “See training images” to browse a selection of nearby training data.[3] Here you’ll find the original human-created images scraped from the web that informed your AI image. Clicking on any of them takes you to their original source. This way, generating an image exposes the human-made images from which it originated. And if you decide to use them, don't forget to credit the authors. :-)
S+T+ARTS
