Deep Daze Fourier Text-to-Image

NOTE: Make sure you also see this post that has a summary of all the Text-to-Image scripts supported by Visions of Chaos with example images.

More Fascinating Text-to-Image

This time “Deep Daze Fourier” from Vadim Epstein. Code available in this notebook.

Compared to the last Deep Daze that generated washed out and pastel shaded results this Deep Daze creates images with sharp, crisp bright colors.

Sample results

“Shrek eating pizza”

“H R Giger”

“Freddy Krueger”

“Surrealist Homer Simpson”

“rose bush”

Availability

This and the previous Text-to-Image systems I have experimented with (here, here and here) are now supported by a GUI front end in Visions of Chaos. As long as you install these prerequisites and have a decent GPU you will be able to run these systems yourself.

For those who love to tinker I have now added a bunch more of the script parameters so you no longer have to edit the Python source code outside Visions of Chaos.

Other Text-to-Image

If you know of any other Text-to-Image systems (with sharable open-source code) then please let me know. All of the Text-to-Image systems I have tested so far all have their own unique behaviors and outputs so I will always be on the lookout for more new variations.

Jason.

Aleph2Image Text-to-Image

NOTE: Make sure you also see this post that has a summary of all the Text-to-Image scripts supported by Visions of Chaos with example images.

Previously I experimented with Big Sleep and other Text-to-Image systems.

This post covers variations of Aleph2Image Text-to_Image. Originally coded by Ryan Murdock.

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Aleph2Image “Gamma”

Code from this colab. This one seems to evolve white blotches that grow and take over the entire image. Before the white out stage the images tend to have too much contrast. Previous results from Deep Daze were too washed out, this one is too “contrasty”. If they could both be pushed towards that “sweet spot” they would both look much better.

“surrealism”

“H R Giger”

“seascape oil painting”

“frogs in the rain”

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Aleph2Image “Delta”

Code from this colab. A newer revision of Aleph2Image that doesn’t have the white out issues. The resulting images have much more vibrant colors.

“surrealism”

“H R Giger”

“seascape oil painting”

“frogs in the rain”

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Improved Aleph2Image “Delta” v2

Code from this colab. A newer revision of Aleph2Image Delta that gives much better results, although the results tend to be similar to each other for each prompt text. This and Big Sleep would be the best 2 Text-to-Image systems I have experimented with so far.

“surrealism”

“H R Giger”

“seascape oil painting”

“frogs in the rain”

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Easy GUI Front End

I include a simple GUI dialog front end for these Text-to-Image systems in Visions of Chaos. As long as you have the prerequisites installed you will be able to convert text prompts into single or multiple images.

You do need a GPU with lots of VRAM for these to work (especially the 512×512 image models).

Jason.

Further Explorations Into Text-to-Image Machine Learning

NOTE: Make sure you also see this post that has a summary of all the Text-to-Image scripts supported by Visions of Chaos with example images.

After my initial experiments with Big Sleep Text-to-Image generation I looked around for some more examples to play with. I was really impressed with Big Sleep and you can see some examples of Big Sleep output in that original post. I still think Big Sleep is the best Text-to-Image code I have used so far and better than what is in this post.

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Deep Daze

Deep Daze is by Phil Wang and the source code is available here.

Deep Daze tends to generate collage-like images. As the first example image shows the resulting images have a washed out or faded look. I put the rest of the example Deep Daze images through a quick Auto White Balance pass in GIMP.

“H R Giger”

“Rainforest”

“night club”

“seascape painting”

“flowing water”

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VQGAN-CLIP z+quantize

VQGAN-CLIP using a z+quantize method is from Katherine Crowson. Source code is available here.

This method also has the option to use an image to seed the initial model rather than just random noise, but the following examples were all seeded with noise. The resulting images tend to be divided up into rectangular regions, but the resulting imagery is interesting.

“H R Giger”

“rainforest”

“night club”

“seascape painting”

“flowing water”

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VQGAN-CLIP codebook

VQGAN-CLIP using a codebook method is also from Katherine Crowson. Source code is available here.

VQGAN-CLIP codebook seem to give very similar images for different seeds, so I have only shown two examples for each phrase.

“H R Giger”

“rainforest”

“night club”

“seascape painting”

“flowing water”

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Other Text-to-Image Models?

If you know of any other available Text-to-Image systems (that are freely available and shareable) let me know.

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Availability

You can follow the above links and download the Python code yourself if you are so inclined.

I do include a basic GUI front-end for these Text-to-Image generators in Visions of Chaos. As long as you have the prerequisites installed (which you would need to install to run these outside Visions of Chaos) then you can experiment with these models yourself without needing to use the command line.

Jason.