PyTorch Implementation of “Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)”
Unofficial PyTorch Implementation of Progressive Distillation for Fast Sampling of Diffusion Models
V-Diffusion is an algorithm that creates a new model capable of sampling images in 2^N fewer diffusion steps.
What’s different from official paper?
DDPM model was used without authors modification.
Images generation
Unpack pretrained weights in the checkpoints folder.
To sample from the original model run:
!python ./sample.py --out_file ./images/celeba_original_ts128.png --module celeba_u --time_scale 1 --checkpoint ./checkpoints/celeba/original/checkpoint.pt --batch_size 1 --clipping_value 1.2
Using the parameter time_scale, you can specify the number of skipped steps.
Try different values of clipping_value to see how the image quality changes.
Sampling from the distilled model:
python ./sample.py --out_file ./images/celeba_u_6.png --module celeba_u --checkpoint ./checkpoints/celeba/base_6/checkpoint.pt --batch_size 1 --clipping_value 1.2
Training
Don’t afraid the artifacts like this during training the base model and the first stages of distillation. They are caused by the features of the image sampling algorithm and will not appear at later stages.
Prepare lmdb dataset:
python prepare_data.py --size 256 --out celeba_256 [PATH TO CELEBA_HQ IMAGES]
Move celeba_256 to ./data folder.
Run tensorboard --logdir ./ in checkpoints folder to watch the results.
Run training celeba_u_script.ipynb notebook.
