| VQGAN is great, but leaves artifacts that are especially visible around things like faces. | |
| It's be great to be able to train a model to fix ('devqganify') these flaws. | |
| For this purpose, I've made this dataset, which contains 100k examples, each with | |
| - A 512px image | |
| - A smaller 256px version of the same image | |
| - A reconstructed version, which is made by encoding the 256px image with VQGAN (f16, 1024 version from https://heibox.uni-heidelberg.de/d/8088892a516d4e3baf92, one of the ones from taming-transformers) and then decoding the result. | |
| The idea is to train a model to go from the 256px vqgan output back to something as close to the original image as possible, or even to try and output an up-scaled 512px version for extra points. | |
| Let me know what you come up with :) | |
| Usage: | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset('johnowhitaker/vqgan1024_reconstruction') | |
| dataset['train'][0]['image_256'] # Original image | |
| dataset['train'][0]['reconstruction_256'] # Reconstructed version | |
| ```` | |
| Approximate code used to prepare this data: https://colab.research.google.com/drive/1AXzlRMvAIE6krkpFwFnFr2c5SnOsygf-?usp=sharing (let me know if you hit issues) | |
| I'll be making a similar dataset with other VQGAN variants and posting progress on devqganify models soon, feel free to get in touch for more info (@johnowhitaker) |