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.pre-commit-config.yaml

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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.6.0
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rev: v5.0.0
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hooks:
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- id: end-of-file-fixer
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- id: trailing-whitespace
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- id: detect-private-key
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- repo: https://github.com/psf/black
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rev: "24.4.2"
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rev: "24.10.0"
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hooks:
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- id: black
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- id: black-jupyter

generation/README.md

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## [Training a 2D VQ-VAE + Autoregressive Transformers](./2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb):
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Example shows how to train a Vector-Quantized Variation Autoencoder + Transformers on the MedNIST dataset.
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## Training VQ-VAEs and VQ-GANs: [2D VAE](./2d_vqvae/2d_vqvae_tutorial.ipynb), [3D VAE](./3d_vqvae/3d_vqvae_tutorial.ipynb) and [2D GAN](./3d_autoencoderkl/2d_vqgan_tutorial.ipynb)
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## Training VQ-VAEs and VQ-GANs: [2D VAE](./2d_vqvae/2d_vqvae_tutorial.ipynb), [3D VAE](./3d_vqvae/3d_vqvae_tutorial.ipynb) and [2D GAN](./2d_vqgan/2d_vqgan_tutorial.ipynb)
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Examples show how to train Vector Quantized Variation Autoencoder on [2D](./2d_vqvae/2d_vqvae_tutorial.ipynb) and [3D](./3d_vqvae/3d_vqvae_tutorial.ipynb), and how to use the PatchDiscriminator class to train a [VQ-GAN](./2d_vqgan/2d_vqgan_tutorial.ipynb) and improve the quality of the generated images.
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## [Training a 2D Denoising Diffusion Probabilistic Model](./2d_ddpm/2d_ddpm_tutorial.ipynb):

pathology/tumor_detection/ignite/profiling_camelyon_pipeline.ipynb

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"!nsys profile \\\n",
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" --trace nvtx,osrt,cudnn,cuda, \\\n",
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" --delay 5 \\\n",
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" --duration 10 \\\n",
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" --duration 20 \\\n",
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" --show-output true \\\n",
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" --force-overwrite true \\\n",
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" --output profile_report.nsys-rep \\\n",

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