This is a PyTorch implementation of different continual learning strategies for class-incremental image generation with Denoising Diffusion Probabilistic Models (DDPM). Part of the repository is adopted from the labml.ai DDPM implementation and the official SupSup code repository.
python=3.9
pytorch=1.10
torchvision=0.11
tqdm
scipy
The experiment config files can be found in the experiments/
folder. Here are some examples:
Unconditional image generation on MNIST with knowledge distillation:
python -m experiments.uncond.mnist.kd
Conditional image generation on MNIST with sequential fine-tuning:
python -m experiments.cond.mnist.sft
Unconditional image generation on CIFAR with joint training:
python -m experiments.uncond.cifar.joint
Change the save_dir
variable and the checkpoint loading path in metrics/save_images.py
. Then run
python -m metrics.save_images
python -m metrics.save_images