cjwbw / diffae

Image Manipulatinon with Diffusion Autoencoders

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  • 16.8K runs
  • GitHub
  • Paper
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Run with an API
Playground API Examples README Versions

Examples

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Run time and cost

This model costs approximately $0.0069 to run on Replicate, or 144 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 31 seconds.

Readme

This is a cog implementation of face manipulation from https://github.com/phizaz/diffae

Official implementation of Diffusion Autoencoders

A CVPR 2022 (ORAL) paper (paper, site, 5-min video):

@inproceedings{preechakul2021diffusion,
      title={Diffusion Autoencoders: Toward a Meaningful and Decodable Representation}, 
      author={Preechakul, Konpat and Chatthee, Nattanat and Wizadwongsa, Suttisak and Suwajanakorn, Supasorn},
      booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
      year={2022},
}
Original in imgs directory
 Aligned with align.py
 Using manipulate.ipynb