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Please install survae first
pip install git+https://github.com/didriknielsen/survae_flows.git
# the install corresponding environment
conda create -n colorvae --file requirements.txt-
COCO train2017 & val2017 dataset Please extract to
./cocofolder. -
Tiny imagenet provided on Kaggle
kaggle datasets download -d akash2sharma/tiny-imagenet
Please delete the duplicated folder in the zip file before training
python predict.py --resume models/dil256-vae_model.pt --img_path par37351-teaser-story-big.jpgpython main.py --img_size 256 --dataset COCO --lr 0.001 --exp_name dil256 --batch_size 32- For image output
python predict.py --img_size 256 --resume models/dil256-vae_model.pt --img_path coco/val2017 --sample_num 8 --separate- For image output with VAE hint
python predict.py --img_size 256 --resume models/dil256-vae_model.pt --img_path coco/val2017 --sample_num 8 --separate --vae_hint- For PNSR score
python predict.py --img_size 256 --resume models/dil256-vae_model.pt --img_path coco/val2017 --sample_num 8 --psnrOn COCO val2017 dataset
| ColorVAE | No VAE | ECCV16 | SIGGRAPH17 | w/o semantic pre-train | w/ VAE hint |
|---|---|---|---|---|---|
| 26.0008 | 24.6531 | 21.9863 | 25.6168 | 21.5979 | 27.0091 |
| No VAE | ECCV16 | SIGGRAPH17 | w/ VAE hint |
|---|---|---|---|
| 3.555 | 3.188 | 3.546 | 3.691 |