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paper: Idempotent Generative Network,https://arxiv.org/abs/2311.01462

This is a simple unofficial implementation. We trained it on the Celeba dataset.

first, download the Celeba dataset and unzip it to thedata folder.

then, install the requirements:

pip install -r requirements.txt

modify the parameters inconfig.yml as your needed and run:

python train.py

model checkpoint with 1000 epoch training,download here.

These are the parameters ofgenerate.py:

• -cp: the path of checkpoint. default:./checkpoints/model.pth
• --config: the path of config file. default:./config.yml
• -bs: how many images to generate at once. default:16
• --nrow: how many images are displayed in a row, only valid whensteps=1. default:4
• --steps: the times of applying model. default:1
• --show: whether to show the generated images. default:False
• -sp: save path of the result image. default: None
• --device: the device to use. default:cuda
• --to_grayscale: whether to convert the generated images to grayscale. default:False

generate one step images:

python generate.py -cp"./checkpoints/model.pth" --config"./config.yml" -bs 128 --nrow 16 --show -sp"./result/one_step.png"

generate multi step images:

python generate.py -cp"./checkpoints/model.pth" --config"./config.yml" -bs 8 --steps 3 --show -sp"./result/three_steps.png"