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Merge branch 'main' into omniverse-tutorial
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‎generation/2d_diffusion_autoencoder/2d_diffusion_autoencoder_tutorial.ipynb‎

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‎generation/README.md‎

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## 2D super-resolution using diffusion models: [using torch](./2d_super_resolution/2d_sd_super_resolution.ipynb) and [using torch lightning](./2d_super_resolution/2d_sd_super_resolution_lightning.ipynb).
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Examples show how to perform super-resolution in 2D, using PyTorch and PyTorch Lightning.
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## [Guiding the synthetic process using a semantic encoder](./2d_diffusion_autoencoder/2d_diffusion_autoencoder.ipynb)
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Example shows how to train a DDPM and an encoder simultaneously, resulting in the latents of the encoder guiding the inference process of the DDPM.

‎generation/anomaly_detection/2d_classifierfree_guidance_anomalydetection_tutorial.ipynb‎

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},
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"source": [
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"condition_dropout = 0.15\n",
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"max_epochs = 2e4\n",
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"max_epochs = 20000\n",
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"batch_size = 64\n",
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"val_interval = 100\n",
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"iter_loss_list = []\n",

‎generation/maisi/README.md‎

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‎generation/maisi/configs/config_infer_16g_256x256x128.json‎

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{
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"num_output_samples": 1,
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"body_region": ["abdomen"],
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"anatomy_list": ["liver","hepatic tumor"],
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"controllable_anatomy_size": [],
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"num_inference_steps": 1000,
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"mask_generation_num_inference_steps": 1000,
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"output_size": [
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256,
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256,
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128
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],
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"image_output_ext": ".nii.gz",
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"label_output_ext": ".nii.gz",
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"spacing": [
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1.5,
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1.5,
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4.0
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],
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"autoencoder_sliding_window_infer_size": [96,96,96],
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"autoencoder_sliding_window_infer_overlap": 0.25,
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"autoencoder_tp_num_splits": 2
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}

‎generation/maisi/configs/config_infer_16g_256x256x256.json‎

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{
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"num_output_samples": 1,
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"body_region": ["abdomen"],
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"anatomy_list": ["liver","hepatic tumor"],
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"controllable_anatomy_size": [],
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"num_inference_steps": 1000,
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"mask_generation_num_inference_steps": 1000,
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"output_size": [
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256,
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256,
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256
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],
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"image_output_ext": ".nii.gz",
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"label_output_ext": ".nii.gz",
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"spacing": [
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1.5,
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1.5,
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2.0
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],
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"autoencoder_sliding_window_infer_size": [48,48,64],
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"autoencoder_sliding_window_infer_overlap": 0.25,
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"autoencoder_tp_num_splits": 2
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}

‎generation/maisi/configs/config_infer_16g_512x512x128.json‎

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{
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"num_output_samples": 1,
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"body_region": ["abdomen"],
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"anatomy_list": ["liver","hepatic tumor"],
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"controllable_anatomy_size": [],
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"num_inference_steps": 1000,
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"mask_generation_num_inference_steps": 1000,
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"output_size": [
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512,
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128
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],
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"image_output_ext": ".nii.gz",
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"label_output_ext": ".nii.gz",
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"spacing": [
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0.75,
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],
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"autoencoder_sliding_window_infer_size": [64,64,32],
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"autoencoder_sliding_window_infer_overlap": 0.25,
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"autoencoder_tp_num_splits": 1
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}

‎generation/maisi/configs/config_infer_24g_256x256x256.json‎

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{
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"num_output_samples": 1,
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"body_region": ["abdomen"],
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"anatomy_list": ["liver","hepatic tumor"],
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"controllable_anatomy_size": [],
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"num_inference_steps": 1000,
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"mask_generation_num_inference_steps": 1000,
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"output_size": [
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],
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"image_output_ext": ".nii.gz",
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"label_output_ext": ".nii.gz",
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"spacing": [
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],
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"autoencoder_sliding_window_infer_size": [64,64,64],
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"autoencoder_sliding_window_infer_overlap": 0.25,
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"autoencoder_tp_num_splits": 4
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}

‎generation/maisi/configs/config_infer_24g_512x512x128.json‎

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{
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"num_output_samples": 1,
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"body_region": ["abdomen"],
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"anatomy_list": ["liver","hepatic tumor"],
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"controllable_anatomy_size": [],
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"num_inference_steps": 1000,
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"mask_generation_num_inference_steps": 1000,
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"output_size": [
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],
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"image_output_ext": ".nii.gz",
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"label_output_ext": ".nii.gz",
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"spacing": [
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],
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"autoencoder_sliding_window_infer_size": [80,80,32],
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"autoencoder_sliding_window_infer_overlap": 0.25,
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"autoencoder_tp_num_splits": 1
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}

‎generation/maisi/configs/config_infer_24g_512x512x512.json‎

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{
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"num_output_samples": 1,
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"body_region": ["abdomen"],
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"anatomy_list": ["liver","hepatic tumor"],
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"controllable_anatomy_size": [],
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"num_inference_steps": 1000,
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"mask_generation_num_inference_steps": 1000,
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"output_size": [
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512,
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],
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"image_output_ext": ".nii.gz",
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"label_output_ext": ".nii.gz",
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"spacing": [
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0.75,
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0.75,
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],
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"autoencoder_sliding_window_infer_size": [64,64,48],
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"autoencoder_sliding_window_infer_overlap": 0.25,
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"autoencoder_tp_num_splits": 2
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}

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