example
gist
The steps you need are very simple.
Step1. clone repo
Step2. install packages
Step3. download pre-trained model
Step4. run a script
https://colab.research.google.com/gist/koji/b02b81ef5cdf0b9a6a69cd26a8f62bf5/stargan2.ipynb
Original repo
StarGAN2 for practice
This version of StarGAN2 (coined as 'Post-modern Style Transfer') is intended mostly for fellow artists, who rarely look at scientific metrics, but rather need a working creative tool. At least, this is what I use nearly daily myself.
Here are few pieces, made with it: Terminal Blink, Ghosts, Occurro, etc.
Tested on Pytorch 1.4-1.8. Sequence-to-video conversions require FFMPEG. For more explicit details refer to the original implementation.
Features
- streamlined workflow, focused on practical tasks
- cleaned up and simplified code for better readability
- stricter memory management to fit bigger batches on consumer GPUs
- models mixing (SWA) for better stability
Presumed file structure
| stargan2 | root |
|---|---|
| ├ _in | input data for processing |
| ├ _out | generation output (sequences & videos) |
| ├ data | datasets for training |
| │ └ afhq | [example] some dataset |
| │ ├ cats | [example] images for training |
| │ │ └ test | [example] images for validation |
| │ |
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