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DiffusionModel (WIP)

  • Re-implementing Denoising Diffusion Probabilistic Models and Denoising Diffusion Implicit Models using Pytorch

Concept of DDPM & DDIM

  • DDPM

concept_of_ddpm

  • DDIM

concept_of_ddim

Model Architecture (WIP)

Trining Process (WIP)

Evaluation Process (WIP)

Reference

  • Denoising Diffusion Probabilistic Models (Paper link)
  • Denoising Diffusion Implicit Models (Paper link)

Usage of Training

  • DDPM
python main.py --model_type=ddpm
  • DDIM
python main.py --model_type=ddim

Get a Awesome Generating Images ! (WIP)

  • DDPM
  • DDIM

Experiments

  • Quantitative result
Model FID IS #Params
DDPM - - -
DDIM - - -
  • Qualitative result (WIP, attach more images later !, Below images are trained model result !)

    • DDPM

    model_460000_t_1000_num_0 model_460000_t_900_num_0 model_460000_t_800_num_0 model_460000_t_700_num_0 model_460000_t_600_num_0 model_460000_t_500_num_0 model_460000_t_400_num_0 model_460000_t_300_num_0 model_460000_t_200_num_0 model_460000_t_100_num_0 model_460000_t_2_num_0

    • DDIM

    model_700000_t_1000_num_0 model_700000_t_900_num_0 model_700000_t_800_num_0 model_700000_t_700_num_0 model_700000_t_600_num_0 model_700000_t_500_num_0 model_700000_t_400_num_0 model_700000_t_300_num_0 model_700000_t_200_num_0 model_700000_t_100_num_0 model_700000_t_2_num_0

Interpolations Experiments (WIP)

  • Example (Below image is paper result)

interpolation_imgs

Metric (FID) Evaluation

  • Usage
python metric/fid_test.py --cuda=True

Metric (IS) Evalution (WIP)

  • Usage
WIP

@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6840--6851},
  year={2020}
}
@article{song2020denoising,
  title={Denoising diffusion implicit models},
  author={Song, Jiaming and Meng, Chenlin and Ermon, Stefano},
  journal={arXiv preprint arXiv:2010.02502},
  year={2020}
}

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