artificial intelligence

cosmic love and attention

fire in the sky

a pyramid made of ice

a lonely house in the woods

marriage in the mountains

lantern dangling from a tree in a foggy graveyard

a vivid dream

balloons over the ruins of a city

the death of the lonesome astronomer – by moirage

Big Sleep

Ryan Murdock has done it again, combining OpenAI’s CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU.

You will be able to have the GAN dream up images using natural language with a one-line command in the terminal.

Original notebook Open In Colab

Simplified notebook Open In Colab


$ pip install big-sleep


$ dream "a pyramid made of ice"

Images will be saved to wherever the command is invoked


You can invoke this in code with

from big_sleep import Imagine

dream = Imagine(
    text = "fire in the sky",
    lr = 5e-2,
    save_every = 25,
    save_progress = True


You can also set a new text by using the .set_text() command

dream.set_text("a quiet pond underneath the midnight moon")

And reset the latents with .reset()


To save the progression of images during training, you simply have to supply the --save-progress flag

$ dream "a bowl of apples next to the fireplace" --save-progress --save-every 100

Due to the class conditioned nature of the GAN, Big Sleep often steers off the manifold into noise. You can use a flag to save the best high scoring image (per CLIP critic) to {filepath}.best.png in your folder.

$ dream "a room with a view of the ocean" --save-best


You can set the number of classes that you wish to restrict Big Sleep to use for the Big GAN with the --max-classes flag as follows (ex. 15 classes). This may lead to extra stability during training, at the cost of lost expressivity.

$ dream 'a single flower in a withered field' --max-classes 15


Deep Daze – CLIP and a deep SIREN network

Used By


    title  = {CLIP: Connecting Text and Images},
    author = {Alec Radford, Ilya Sutskever, Jong Wook Kim, Gretchen Krueger, Sandhini Agarwal},
    year   = {2021}
    title   = {Large Scale GAN Training for High Fidelity Natural Image Synthesis}, 
    author  = {Andrew Brock and Jeff Donahue and Karen Simonyan},
    year    = {2019},
    eprint  = {1809.11096},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}