Original Source Here
You can see how the contrast adjustment “punches up” the colors. The source code for adjusting the image contrast in Python is here.
Here are some results of running MAGnet with various text queries. Be sure to check out the appendix to see even more results.
Discussion and Future Work
The MAGnet system works fairly well. It seems to do a better job generating abstract paintings than representational paintings. It converges on a solution fairly quickly, within five generations.
Although the results of MAGnet are pretty good, if you play with it, you may see the same visual themes recur occasionally. This may be due to the limited number of source images used during the training (about 4,000 initial images, augmented to over 12,000 via cropping, and filtered to 10,000). Training with more images will probably reduce the frequency of repeated visual themes.
Another area that could be improved is the textures within the images. There seem to be some “computery” artifacts in the flat areas. This could be due to the reimplementation of the CUDA operations.
I noticed that rosinality is currently working on a new open source project on GitHub called alias-free-gan-pytorch. Sounds promising!
All of the source code for this project is available on GitHub. Images of the paintings on Kaggle and the source code are released under the CC BY-SA license.
I want to thank Jennifer Lim for her help with this article.
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Appendix — Gallery of MAGnet Images
Here is a sampling of images created by MAGnet. You can click on each image to see a larger version.
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