README.md

S$^2$-DMs: Skip-Step Diffusion Models (S2-DM)

paper

Abstract:

Diffusion models have emerged as powerful generative tools, rivaling GANs in sample quality and mirroring the likelihood scores of autoregressive models. A subset of these models, exemplified by DDIMs, exhibit an inherent asymmetry: they are trained over $T$ steps but only sample from a subset of $T$ during generation. This selective sampling approach, though optimized for speed, inadvertently misses out on vital information from the unsampled steps, leading to potential compromises in sample quality. To address this issue, we present the S^2-DMs, which is a new training method by using an innovative $L_{skip}$, meticulously designed to reintegrate the information omitted during the selective sampling phase. The benefits of this approach are manifold: it notably enhances sample quality, is exceptionally simple to implement, requires minimal code modifications, and is flexible enough to be compatible with various sampling algorithms. On the CIFAR10 dataset, models trained using our algorithm showed an improvement of 3.27% to 14.06% over models trained with traditional methods across various sampling algorithms (DDIMs, PNDMs, DEIS) and different numbers of sampling steps (10, 20, ..., 1000). On the CELEBA dataset, the improvement ranged from 8.97% to 27.08%. Access to the code and additional resources is provided in the supplementary material.

Running the Experiments

The code has been tested on PyTorch 1.6.

Train a model

Training is exactly the same as DDPM with the following:

python main.py --config {DATASET}.yml --exp {PROJECT_PATH} --doc {MODEL_NAME} --ni

Sampling from the model

Sampling from the generalized model for FID evaluation

python main.py --config {DATASET}.yml --exp {PROJECT_PATH} --doc {MODEL_NAME} --sample --fid --timesteps {STEPS} --eta {ETA} --ni

where

• ETA controls the scale of the variance (0 is DDIM, and 1 is one type of DDPM).
• STEPS controls how many timesteps used in the process.
• MODEL_NAME finds the pre-trained checkpoint according to its inferred path.

If you want to use the DDPM pretrained model:

python main.py --config {DATASET}.yml --exp {PROJECT_PATH} --use_pretrained --sample --fid --timesteps {STEPS} --eta {ETA} --ni

the --use_pretrained option will automatically load the model according to the dataset.

If you want to use the version with the larger variance in DDPM: use the --sample_type ddpm_noisy option.

Sampling from the model for image inpainting

Use --interpolation option instead of --fid.

Sampling from the sequence of images that lead to the sample

Use --sequence option instead.

The above two cases contain some hard-coded lines specific to producing the image, so modify them according to your needs.

This implementation is based on / inspired by:

• https://github.com/ermongroup/ddim (the DDIM repo).