(Yet another) CLIP Image Generator

Dear seeker!

This is probably one of many generative gradient-descent engines using OpenAI's CLIP network to rate the similarity between generated pixels and a CLIP-feature, typically given in the form of natural language:

"kissing cthulhu"

CLIPig uses no sophisticated image generation network, just ordinary RGB pixel planes.

The outstanding thing, maybe, and the reason for developing it, is it's configuration interface. I got pretty tired of constantly changing actual code during various experiments and then loosing stuff i did before so i started this new project which fulfills most desires through yaml configuration files.

Please read the documentation (or as markdown) or gather everything from this example:

example experiment

resolution: 512
epochs: 200

learnrate: 1.5
learnrate_scale: 1. - .95 * pow(t, 5.)   # 't' is training epoch in [0, 1] range

targets:

  - name: random sampler
    batch_size: 10

    transforms:
      - noise: 0.1 0.2 0.3
      - random_scale: .1 1.
      - random_crop: 224
      - blur:
          kernel_size: 13

    features:
      - text: some text feature to match
      - text: some text feature to avoid
        weight: -1.
      - image: /path/to/image.png
        loss: L2  # 'cosine' is default, 'L1' and 'L2' are also possible

  - name: image adjustments
    end: 30%
    constraints:
      - mean:
          above: .1 .2 .3
      
# post-processing is applied after each back-propagation step
postproc:
  - blur: 
      kernel_size: 3
    end: 10%    

As you can see, it supports

• expressions
• multiple targets
• multiple features per target
• CLIP features from texts or images
• negative weights!
• arbitrary pixel transformation chains
• a couple of other constraints that can be added to the loss
• a couple of image post-processing effects
• scheduling via start and end parameters

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Currently, to get started switch to a virtual environment that contains the torch library matching your CUDA drivers and then

python clipig.py examples/strawberries.yaml -o ./images/

It will save an image to ./images/strawberries.png. If such a file exists, a number is attached to the filename or incremented automatically. It will never overwrite an existing file.

The GUI version for interactive fun but it's even more hacked together than the rest. Install pyqt5 and start it with:

python clipig-gui.py examples/strawberries.yaml

It's possible to change the configuration during training which is funny and horrible alike. It will not save snapshots by default, instead they are immediately displayed for your pleasure and can be saved with CTRL-S.

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Here's a motivating article whose images where created with some former code and another one where the excellent DALL-E examples have been tried to reproduce.

Known issues

The author does not have enough time!

Then there are bugs in there and stuff that is not yet tested or even implemented.

Docs is missing an installation section.

But the major concern is the image quality. Just adjusting pixels so that CLIP does like them generally creates horrible grids and artifacts. A good first step to avoid them is a relatively small learning rate and random rotation like:

- random_rotate:
    degree: -10 10
    center: 0 1

Still it's not eye-friendly to look at without a little gaussian blur. It's possible to use a gaussian blur as a training constraint and add it to the loss function of the particular target window. That leads to much better results already. And after a few hours of staring at those images they get better and better anyways.

The other problem is the uniform depth of images. CLIP seems to be good with object boundaries and textures. But that does not automatically help to divide an image into significant foreground and less significant background. Generally, the image often tends to be a uniform something with some objects carved out. Although, there are counter examples that show promise:

"2d platformer"

If you feel you can help or want to discuss things, please open an issue.

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"strawberry" - well, i did not make this up. things are sometimes strangely interconnected in CLIP