Diffusion Transformer

Implementation of the Diffusion Transformer model in the paper:

Scalable Diffusion Models with Transformers.

See here for the official Pytorch implementation.

Dependencies

• Python 3.9
• Pytorch 2.1.1

Training Diffusion Transformer

Use --data_dir=<data_dir> to specify the dataset path.

python train.py --data_dir=./data/

Samples

Sample output from minDiT (39.89M parameters) on CIFAR-10:

Sample output from minDiT on CelebA:

Hparams setting

Adjust hyperparameters in the config.py file.

Implementation notes:

• minDiT is designed to offer reasonable performance using a single GPU (RTX 3080 TI).
• minDiT largely follows the original DiT model.
• DiT Block with adaLN-Zero.
• Diffusion Transformer with Linformer attention.
• EDM sampler.
• FID evaluation.

todo

• Add Classifier-Free Diffusion Guidance and conditional pipeline.
• Add Latent Diffusion and Autoencoder training.
• Add generate.py file.

Licence

MIT