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Deep Daze

mist over green hills

shattered plates on the grass

cosmic love and attention

a time traveler in the crowd

life during the plague

meditative peace in a sunlit forest

a man painting a completely red image

a psychedelic experience on LSD

What is this?

Simple command line tool for text to image generation using OpenAI's CLIP and Siren. Credit goes to Ryan Murdock for the discovery of this technique (and for coming up with the great name)!

Original notebook Open In Colab

New simplified notebook Open In Colab

This will require that you have an Nvidia GPU or AMD GPU

  • Recommended: 16GB VRAM
  • Minimum Requirements: 4GB VRAM (Using VERY LOW settings, see usage instructions below)


$ pip install deep-daze

Windows Install

Presuming Python is installed:

  • Open command prompt and navigate to the directory of your current version of Python
  pip install deep-daze


$ imagine "a house in the forest"

For Windows:

  • Open command prompt as administrator
  imagine "a house in the forest"

That's it.

If you have enough memory, you can get better quality by adding a --deeper flag

$ imagine "shattered plates on the ground" --deeper


In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources.

$ imagine "stranger in strange lands" --num-layers 32




    imagine TEXT <flags>

        (required) A phrase less than 77 tokens which you would like to visualize.

        Default: None
        Path to png/jpg image or PIL image to optimize on
        Default: None
        User-created custom CLIP encoding. If used, replaces any text or image that was used.
        Default: False
        Creates a story by optimizing each epoch on a new sliding-window of the input words. If this is enabled, much longer texts than 77 tokens can be used. Requires save_progress to visualize the transitions of the story.
        Default: 5
        Only used if create_story is True. How many words to optimize on for the first epoch.
        Default: 5
        Only used if create_story is True. How many words to add to the optimization goal per epoch after the first one.
        Default: None
        Only used if create_story is True. Defines a separator like '.' that splits the text into groups for each epoch. Separator needs to be in the text otherwise it will be ignored
        Default: 0.1
        Lower bound of the sampling of the size of the random cut-out of the SIREN image per batch. Should be smaller than 0.8.
        Default: 1.0
        Upper bound of the sampling of the size of the random cut-out of the SIREN image per batch. Should probably stay at 1.0.
        Default: False
        If True, the LOWER_BOUND_CUTOUT is linearly increased to 0.75 during training.
        Default: 1e-05
        The learning rate of the neural net.
        Default: 16
        The number of hidden layers to use in the Siren neural net.
        Default: 4
        The number of generated images to pass into Siren before calculating loss. Decreasing this can lower memory and accuracy.
        Default: 4
        Calculate a weighted loss of n samples for each iteration. Increasing this can help increase accuracy with lower batch sizes.
        Default: 20
        The number of epochs to run.
        Default: 1050
        The number of times to calculate and backpropagate loss in a given epoch.
        Default: 100
        Generate an image every time iterations is a multiple of this number.
        Default: 512
        The desired resolution of the image.
        Default: False
        Uses a Siren neural net with 32 hidden layers.
        Default: False
        Whether or not to overwrite existing generated images of the same name.
        Default: False
        Whether or not to save images generated before training Siren is complete.
        Type: Optional[]
        Default: None
        A seed to be used for deterministic runs.
        Default: True
        Whether or not to open a folder showing your generated images.
        Default: False
        Save files with a timestamp prepended e.g. `%y%m%d-%H%M%S-my_phrase_here`
        Default: None
        The generator is trained first on a starting image before steered towards the textual input
        Default: 50
        The number of steps for the initial training on the starting image
        Default: 30.0
        Hyperparameter describing the frequency of the color space. Only applies to the first layer of the network.
        Default: 30.0
        Hyperparameter describing the frequency of the color space. Only applies to the hidden layers of the network.
        Default: False
        Whether or not to save a GIF animation of the generation procedure. Only works if save_progress is set to True.


Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text.

Simply specify the path to the image you wish to use, and optionally the number of initial training steps.

$ imagine 'a clear night sky filled with stars' --start_image_path ./cloudy-night-sky.jpg

Primed starting image

Then trained with the prompt A pizza with green pepper.

Optimize for the interpretation of an image

We can also feed in an image as an optimization goal, instead of only priming the generator network. Deepdaze will then render its own interpretation of that image:

$ imagine --img samples/Autumn_1875_Frederic_Edwin_Church.jpg

Original image:

The network's interpretation:

Original image:

The network's interpretation:

Optimize for text and image combined

$ imagine "A psychedelic experience." --img samples/hot-dog.jpg

The network's interpretation:

New: Create a story

The regular mode for texts only allows 77 tokens. If you want to visualize a full story/paragraph/song/poem, set create_story to True.

Given the poem “Stopping by Woods On a Snowy Evening” by Robert Frost - "Whose woods these are I think I know. His house is in the village though; He will not see me stopping here To watch his woods fill up with snow. My little horse must think it queer To stop without a farmhouse near Between the woods and frozen lake The darkest evening of the year. He gives his harness bells a shake To ask if there is some mistake. The only other sound’s the sweep Of easy wind and downy flake. The woods are lovely, dark and deep, But I have promises to keep, And miles to go before I sleep, And miles to go before I sleep.".

We get:



Invoke deep_daze.Imagine in Python

from deep_daze import Imagine

imagine = Imagine(
    text = 'cosmic love and attention',
    num_layers = 24,

Save progress every fourth iteration

Save images in the format insert_text_here.00001.png, insert_text_here.00002.png, ...up to (total_iterations % save_every)

imagine = Imagine(

Prepend current timestamp on each image.

Creates files with both the timestamp and the sequence number.

e.g. 210129-043928_328751_insert_text_here.00001.png, 210129-043928_512351_insert_text_here.00002.png, ...

imagine = Imagine(

High GPU memory usage

If you have at least 16 GiB of vram available, you should be able to run these settings with some wiggle room.

imagine = Imagine(

Average GPU memory usage

imagine = Imagine(

Very low GPU memory usage (less than 4 GiB)

If you are desperate to run this on a card with less than 8 GiB vram, you can lower the image_width.

imagine = Imagine(
    gradient_accumulate_every=16 # Increase gradient_accumulate_every to correct for loss in low batch sizes

VRAM and speed benchmarks:

These experiments were conducted with a 2060 Super RTX and a 3700X Ryzen 5. We first mention the parameters (bs = batch size), then the memory usage and in some cases the training iterations per second:

For an image resolution of 512:

  • bs 1, num_layers 22: 7.96 GB
  • bs 2, num_layers 20: 7.5 GB
  • bs 16, num_layers 16: 6.5 GB

For an image resolution of 256:

  • bs 8, num_layers 48: 5.3 GB
  • bs 16, num_layers 48: 5.46 GB - 2.0 it/s
  • bs 32, num_layers 48: 5.92 GB - 1.67 it/s
  • bs 8, num_layers 44: 5 GB - 2.39 it/s
  • bs 32, num_layers 44, grad_acc 1: 5.62 GB - 4.83 it/s
  • bs 96, num_layers 44, grad_acc 1: 7.51 GB - 2.77 it/s
  • bs 32, num_layers 66, grad_acc 1: 7.09 GB - 3.7 it/s

@NotNANtoN recommends a batch size of 32 with 44 layers and training 1-8 epochs.

Where is this going?

This is just a teaser. We will be able to generate images, sound, anything at will, with natural language. The holodeck is about to become real in our lifetimes.

Please join replication efforts for DALL-E for Pytorch or Mesh Tensorflow if you are interested in furthering this technology.


Big Sleep - CLIP and the generator from Big GAN


    title  = {CLIP: Connecting Text and Images},
    author = {Alec Radford, Ilya Sutskever, Jong Wook Kim, Gretchen Krueger, Sandhini Agarwal},
    year   = {2021}
    title   = {Implicit Neural Representations with Periodic Activation Functions},
    author  = {Vincent Sitzmann and Julien N. P. Martel and Alexander W. Bergman and David B. Lindell and Gordon Wetzstein},
    year    = {2020},
    eprint  = {2006.09661},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}