├── README.md ├── config ├── bedroom_latent.yml ├── bedroom_representation_learning.yml ├── celeba64_latent.yml ├── celeba64_representation_learning.yml ├── celebahq_manipulation.yml ├── ffhq_latent.yml ├── ffhq_representation_learning.yml ├── horse_latent.yml ├── horse_representation_learning.yml └── mnist_regular.yml ├── dataset ├── __init__.py ├── bedroom.py ├── celeba64.py ├── celebahq.py ├── ffhq.py ├── horse.py └── mnist.py ├── diffusion ├── ddim.py └── gaussian_diffusion.py ├── images ├── autoencoding_example_result.png ├── denoise_one_step_result.png ├── gap_measure_result.png ├── interpolation_result.png ├── manipulation_result.png └── unconditional_sample_result.png ├── metric ├── base_metric.py ├── fid │ ├── fid_metric.py │ └── inception.py ├── lpips │ └── lpips_metric.py ├── mse │ └── mse_metric.py ├── ssim │ └── ssim_metric.py └── utils.py ├── model ├── denoise_fn │ ├── __init__.py │ └── mnist.py ├── mlp_skip_net.py ├── module.py ├── representation_learning │ ├── decoder │ │ ├── __init__.py │ │ ├── bedroom.py │ │ ├── celeba64.py │ │ ├── celebahq.py │ │ ├── ffhq.py │ │ └── horse.py │ ├── encoder │ │ ├── __init__.py │ │ ├── bedroom.py │ │ ├── celeba64.py │ │ ├── celebahq.py │ │ ├── ffhq.py │ │ └── horse.py │ └── latent_denoise_fn │ │ ├── __init__.py │ │ ├── bedroom.py │ │ ├── celeba64.py │ │ ├── ffhq.py │ │ └── horse.py ├── shift_unet.py └── unet.py ├── requirements.txt ├── sampler ├── autoencoding_eval.py ├── autoencoding_example.py ├── base_sampler.py ├── denoise_one_step.py ├── gap_measure.py ├── infer_latents.py ├── interpolation.py ├── manipulation.py ├── test_dpms.py └── unconditional_sample.py ├── scripts ├── dist_sample.sh ├── dist_train_latent_diffusion.sh ├── dist_train_manipulation.sh ├── dist_train_regular_diffusion.sh └── dist_train_representation_learning.sh ├── trainer ├── base_trainer.py ├── train_latent_diffusion.py ├── train_manipulation.py ├── train_regular_diffusion.py └── train_representation_learning.py └── utils └── utils.py 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