Face Attribute Manipulation with Diffusion Autoencoders and StyleFlow

🚀 Unofficial implementation of Diffusion Autoencoders: Toward a Meaningful and Decodable Representation for face attribute manipulation.

Image generation

Download pretrained weights to checkpoints directory.

Run styleflow_script.ipynb.

Training

Download Celeba-HQ dataset.

Run celeba_ae_script.ipynb.

Requirements

• pytorch
• torchdiffeq==0.0.1
• kornia

Acknowledgement

1. Diffusion Autoencoders: Toward a Meaningful and Decodable Representation.
2. rosinality/denoising-diffusion-pytorch.
3. StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021).