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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

the tragic intimacy of the eternal conversation with oneself - by moirage

demon fire - by WiseNat

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

User-made notebook with bugfixes and added features, like google drive integration Open In Colab

Install

$ pip install big-sleep

Usage

$ dream "a pyramid made of ice"

Images will be saved to wherever the command is invoked

Advanced

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
)

dream()

You can now train more than one phrase using the delimiter "|"

Train on Multiple Phrases

In this example we train on three phrases:

  • an armchair in the form of pikachu
  • an armchair imitating pikachu
  • abstract
from big_sleep import Imagine

dream = Imagine(
    text = "an armchair in the form of pikachu|an armchair imitating pikachu|abstract",
    lr = 5e-2,
    save_every = 25,
    save_progress = True
)

dream()

Penalize certain prompts as well!

In this example we train on the three phrases from before,

and penalize the phrases:

  • blur
  • zoom
from big_sleep import Imagine

dream = Imagine(
    text = "an armchair in the form of pikachu|an armchair imitating pikachu|abstract",
    text_min = "blur|zoom",
)
dream()

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

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

And reset the latents with .reset()

dream.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

Larger model

If you have enough memory, you can also try using a bigger vision model released by OpenAI for improved generations.

$ dream "storm clouds rolling in over a white barnyard" --larger-model

Experimentation

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

Alternatives

Deep Daze - CLIP and a deep SIREN network

Citations

@misc{unpublished2021clip,
    title  = {CLIP: Connecting Text and Images},
    author = {Alec Radford, Ilya Sutskever, Jong Wook Kim, Gretchen Krueger, Sandhini Agarwal},
    year   = {2021}
}
@misc{brock2019large,
    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}
}