r/deepdream

47K archived posts · Jul 2015 – Sep 2026

Dreaming Neural Nets

r/deepdream

Effective imminently, r/DeepDream is going dark for 48 hours in support of third party apps and NSFW API access. Check out /r/Save3rdPartyApps and /r/ModCoord for more information.

Created Jul 2, 2015

104KMembers (last snapshot)

47KArchived posts

Welcome to r/DeepDream!

We are a community dedicated to art produced with the help of artificial neural networks, which are themselves inspired by the human brain. Advances in the machine learning sub field of artificial intelligence brought on by the information age have made it possible for machines to create art that rivals that of what a human being can do. We here at r/DeepDream mainly focus on applications of deep learning which itself is a sub field of machine learning. As the largest online AI art community, we routinely push the bounds of technology in the pursuit of better-looking artwork.

The DeepDream wiki is available here.

What is DeepDream?

DeepDream uses a sort of algorithmic pareidolia to see and then enhance patterns in an image. This creates a hallucinogenic type effect which resembles dream-like hallucinations, which sometimes resemble the effects of hallucinogenic drugs. In machine learning, DeepDream can be used to both examine a trained neural network model and to speed up the training of a neural network model.

What Is Artistic Style Transfer?

Artistic style transfer utilizes convolutional neural networks (CNNs) to recreate an input image (content image) in the style of one or more style inputs (style images). Specifically, CNNs using a Visual Geometry Group (VGG) architecture have been found to work the best for artistic style transfer. The idea originates from a research paper titled A Neural Algorithm of Artistic Style, which was published in August of 2015. Since then subsequent research has greatly expanded and improved our knowledge of artistic style transfer.

Synonyms for artistic style transfer include: "deep style" and just simply "style transfer".

What Are GANs?

Generative adversarial networks (GANs) are a system of neural networks that compete against each other. Normally GANs are made up of a generator that creates images and a discriminator, which tries to tell the different between training images and images from the generator network. Over the course of training, the discriminator gets better at detecting fake images while the generator gets better at creating images that fool the discriminator. GANs can be used to create anything from photo-realistic images to artwork, and everything in between.

Due to the duality of the generator versus discriminator setup, GANs often need more computing power to both train and use effectively. Combined with instability during training and the added complexity of having two neural networks in competition with each other, the difficulty of using GANs has prevented them from dominating the competition.

What Are Diffusion Models?

Diffusion models are a type of probabilistic generative model that has in recent times surpassed GANs in generating novel image renderings. They are trained by slowly adding noise to inputs, after which they attempt to recreate those inputs.

How Do I Make My Own AI Art?

While some may make the technology look extremely easy to use, it is still highly experimental and often not very user friendly. Online services have sprung up which try to make the process easier, but these services often place strict limitations on the parameters which you can use. Serious neural network artists tend to use their own computers and/or cloud services if they don't have a good enough GPU. Running the code yourself also lets you fine tune all the individual parameters to your liking.

CPUs are extremely slow at performing the mathematical calculation required for neural networks and thus are not recommended. GPUs are the preferred approach. In regards to GPUs, VRAM is the most important resource for creating artwork or doing any sort of machine learning research. Basically, the more VRAM you have access to, the larger the images that you can work with.

A quick start guide for neural style transfer is available on the Wiki here. If your computer isn't good enough to run AI projects, then check out the AWS Setup Wiki Guide here.

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