diff --git a/.gitignore b/.gitignore
index fedd499d..351e54b8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -129,3 +129,5 @@ temp/
# VSCode
.vscode/
*.zip
+models/*/models/*
+models/*/output/*
diff --git a/ci/bundle_custom_data.py b/ci/bundle_custom_data.py
index 0ac2bdef..6f20d112 100644
--- a/ci/bundle_custom_data.py
+++ b/ci/bundle_custom_data.py
@@ -22,6 +22,7 @@
"brats_mri_axial_slices_generative_diffusion",
"maisi_ct_generative",
"cxr_image_synthesis_latent_diffusion_model",
+ "brain_image_synthesis_latent_diffusion_model",
]
# This list is used for our CI tests to determine whether a bundle contains the preferred files.
@@ -45,6 +46,7 @@
"vista2d",
"mednist_ddpm",
"cxr_image_synthesis_latent_diffusion_model",
+ "brain_image_synthesis_latent_diffusion_model",
]
# This list is used for our CI tests to determine whether a bundle needs to be tested after downloading
diff --git a/ci/unit_tests/test_brain_image_synthesis_latent_diffusion_model.py b/ci/unit_tests/test_brain_image_synthesis_latent_diffusion_model.py
new file mode 100644
index 00000000..93fa0c40
--- /dev/null
+++ b/ci/unit_tests/test_brain_image_synthesis_latent_diffusion_model.py
@@ -0,0 +1,47 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import unittest
+
+from monai.bundle import ConfigWorkflow
+from parameterized import parameterized
+from utils import check_workflow
+
+TEST_CASE_1 = [ # inference
+ {
+ "bundle_root": "models/brain_image_synthesis_latent_diffusion_model",
+ "gender": 1.0,
+ "age": 0.7,
+ "ventricular_vol": 0.7,
+ "brain_vol": 0.5,
+ }
+]
+
+
+class BrainImageSynthesisLatentDiffusionModel(unittest.TestCase):
+ @parameterized.expand([TEST_CASE_1])
+ def test_inference(self, params):
+ bundle_root = params["bundle_root"]
+ inference_file = os.path.join(bundle_root, "configs/inference.json")
+ trainer = ConfigWorkflow(
+ workflow_type="inference",
+ config_file=inference_file,
+ logging_file=os.path.join(bundle_root, "configs/logging.conf"),
+ meta_file=os.path.join(bundle_root, "configs/metadata.json"),
+ **params,
+ )
+ check_workflow(trainer, check_properties=True)
+
+
+if __name__ == "__main__":
+ loader = unittest.TestLoader()
+ unittest.main(testLoader=loader)
diff --git a/ci/verify_bundle.py b/ci/verify_bundle.py
index 7beca3e5..283c09b1 100644
--- a/ci/verify_bundle.py
+++ b/ci/verify_bundle.py
@@ -54,6 +54,8 @@ def _get_weights_names(bundle: str):
if bundle == "pediatric_abdominal_ct_segmentation":
# skip test for this bundle's ts file
return "dynunet_FT.pt", None
+ if bundle == "brain_image_synthesis_latent_diffusion":
+ return "autoencoder.pt", "model.pt"
if bundle == "cxr_image_synthesis_latent_diffusion_model":
return "autoencoder.pt", None
return "model.pt", "model.ts"
diff --git a/models/brain_image_synthesis_latent_diffusion_model/LICENSE b/models/brain_image_synthesis_latent_diffusion_model/LICENSE
new file mode 100644
index 00000000..261eeb9e
--- /dev/null
+++ b/models/brain_image_synthesis_latent_diffusion_model/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
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+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
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+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
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diff --git a/models/brain_image_synthesis_latent_diffusion_model/configs/inference.json b/models/brain_image_synthesis_latent_diffusion_model/configs/inference.json
new file mode 100644
index 00000000..206d1130
--- /dev/null
+++ b/models/brain_image_synthesis_latent_diffusion_model/configs/inference.json
@@ -0,0 +1,112 @@
+{
+ "imports": [
+ "$import torch",
+ "$from datetime import datetime",
+ "$from pathlib import Path"
+ ],
+ "bundle_root": ".",
+ "dataset_dir": "",
+ "dataset": "",
+ "evaluator": "",
+ "inferer": "",
+ "load_old": 1,
+ "model_dir": "$@bundle_root + '/models'",
+ "output_dir": "$@bundle_root + '/output'",
+ "create_output_dir": "$Path(@output_dir).mkdir(exist_ok=True)",
+ "gender": 0.0,
+ "age": 0.1,
+ "ventricular_vol": 0.2,
+ "brain_vol": 0.4,
+ "device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
+ "conditioning": "$torch.tensor([[@gender, @age, @ventricular_vol, @brain_vol]]).to(@device).unsqueeze(1)",
+ "out_file": "$datetime.now().strftime('sample_%H%M%S_%d%m%Y') + '_' + str(@gender) + '_' + str(@age) + '_' + str(@ventricular_vol) + '_' + str(@brain_vol)",
+ "autoencoder_def": {
+ "_target_": "monai.networks.nets.AutoencoderKL",
+ "spatial_dims": 3,
+ "in_channels": 1,
+ "out_channels": 1,
+ "latent_channels": 3,
+ "channels": [
+ 64,
+ 128,
+ 128,
+ 128
+ ],
+ "num_res_blocks": 2,
+ "norm_num_groups": 32,
+ "norm_eps": 1e-06,
+ "attention_levels": [
+ false,
+ false,
+ false,
+ false
+ ],
+ "with_encoder_nonlocal_attn": false,
+ "with_decoder_nonlocal_attn": false
+ },
+ "network_def": "@autoencoder_def",
+ "load_autoencoder_path": "$@model_dir + '/autoencoder.pt'",
+ "load_autoencoder_func": "$@autoencoder_def.load_old_state_dict if bool(@load_old) else @autoencoder_def.load_state_dict",
+ "load_autoencoder": "$@load_autoencoder_func(torch.load(@load_autoencoder_path))",
+ "autoencoder": "$@autoencoder_def.to(@device)",
+ "diffusion_def": {
+ "_target_": "monai.networks.nets.DiffusionModelUNet",
+ "spatial_dims": 3,
+ "in_channels": 7,
+ "out_channels": 3,
+ "channels": [
+ 256,
+ 512,
+ 768
+ ],
+ "num_res_blocks": 2,
+ "attention_levels": [
+ false,
+ true,
+ true
+ ],
+ "norm_num_groups": 32,
+ "norm_eps": 1e-06,
+ "resblock_updown": true,
+ "num_head_channels": [
+ 0,
+ 512,
+ 768
+ ],
+ "with_conditioning": true,
+ "transformer_num_layers": 1,
+ "cross_attention_dim": 4,
+ "upcast_attention": true,
+ "use_flash_attention": false
+ },
+ "load_diffusion_path": "$@model_dir + '/model.pt'",
+ "load_diffusion_func": "$@diffusion_def.load_old_state_dict if bool(@load_old) else @diffusion_def.load_state_dict",
+ "load_diffusion": "$@load_diffusion_func(torch.load(@load_diffusion_path))",
+ "diffusion": "$@diffusion_def.to(@device)",
+ "scheduler": {
+ "_target_": "monai.networks.schedulers.DDIMScheduler",
+ "_requires_": [
+ "@load_diffusion",
+ "@load_autoencoder"
+ ],
+ "beta_start": 0.0015,
+ "beta_end": 0.0205,
+ "num_train_timesteps": 1000,
+ "schedule": "scaled_linear_beta",
+ "clip_sample": false
+ },
+ "noise": "$torch.randn((1, 3, 20, 28, 20)).to(@device)",
+ "set_timesteps": "$@scheduler.set_timesteps(num_inference_steps=50)",
+ "sampler": {
+ "_target_": "scripts.sampler.Sampler",
+ "_requires_": "@set_timesteps"
+ },
+ "sample": "$@sampler.sampling_fn(@noise, @autoencoder, @diffusion, @scheduler, @conditioning)",
+ "saver": {
+ "_target_": "SaveImage",
+ "_requires_": "@create_output_dir",
+ "output_dir": "@output_dir",
+ "output_postfix": "@out_file"
+ },
+ "run": "$@saver(@sample[0][0])"
+}
diff --git a/models/brain_image_synthesis_latent_diffusion_model/configs/logging.conf b/models/brain_image_synthesis_latent_diffusion_model/configs/logging.conf
new file mode 100644
index 00000000..91c1a21c
--- /dev/null
+++ b/models/brain_image_synthesis_latent_diffusion_model/configs/logging.conf
@@ -0,0 +1,21 @@
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
diff --git a/models/brain_image_synthesis_latent_diffusion_model/configs/metadata.json b/models/brain_image_synthesis_latent_diffusion_model/configs/metadata.json
new file mode 100644
index 00000000..40da5460
--- /dev/null
+++ b/models/brain_image_synthesis_latent_diffusion_model/configs/metadata.json
@@ -0,0 +1,77 @@
+{
+ "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20240725.json",
+ "version": "1.0.0",
+ "changelog": {
+ "1.0.0": "Initial release"
+ },
+ "monai_version": "1.4.0",
+ "pytorch_version": "2.5.1",
+ "numpy_version": "1.26.4",
+ "required_packages_version": {
+ "nibabel": "5.3.2"
+ },
+ "task": "Brain image synthesis",
+ "description": "A generative model for creating high-resolution 3D brain MRI based on UK Biobank",
+ "authors": "Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, and M. Jorge Cardoso",
+ "copyright": "Copyright (c) MONAI Consortium",
+ "data_source": "https://www.ukbiobank.ac.uk/",
+ "data_type": "nibabel",
+ "image_classes": "T1w head MRI with 1x1x1 mm voxel size",
+ "eval_metrics": {
+ "fid": 0.0076,
+ "msssim": 0.6555,
+ "4gmsssim": 0.3883
+ },
+ "intended_use": "This is a research tool/prototype and not to be used clinically",
+ "references": [
+ "Pinaya, Walter HL, et al. \"Brain imaging generation with latent diffusion models.\" MICCAI Workshop on Deep Generative Models. Springer, Cham, 2022."
+ ],
+ "network_data_format": {
+ "inputs": {
+ "image": {
+ "type": "tabular",
+ "num_channels": 1,
+ "dtype": "float32",
+ "value_range": [
+ 0,
+ 1
+ ],
+ "format": "nii",
+ "spatial_shape": [
+ 160,
+ 224,
+ 160
+ ],
+ "is_patch_data": false,
+ "channel_def": {
+ "0": "Gender",
+ "1": "Age",
+ "2": "Ventricular volume",
+ "3": "Brain volume"
+ }
+ }
+ },
+ "outputs": {
+ "pred": {
+ "type": "image",
+ "format": "image",
+ "num_channels": 1,
+ "spatial_shape": [
+ 160,
+ 224,
+ 160
+ ],
+ "dtype": "float32",
+ "value_range": [
+ 0,
+ 1
+ ],
+ "modality": "MR",
+ "is_patch_data": false,
+ "channel_def": {
+ "0": "T1w"
+ }
+ }
+ }
+ }
+}
diff --git a/models/brain_image_synthesis_latent_diffusion_model/docs/README.md b/models/brain_image_synthesis_latent_diffusion_model/docs/README.md
new file mode 100644
index 00000000..3d80474b
--- /dev/null
+++ b/models/brain_image_synthesis_latent_diffusion_model/docs/README.md
@@ -0,0 +1,75 @@
+# Brain Imaging Generation with Latent Diffusion Models
+
+### **Authors**
+
+Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F Da Costa, Virginia Fernandez, Parashkev Nachev,
+Sebastien Ourselin, and M. Jorge Cardoso
+
+### **Tags**
+Synthetic data, Latent Diffusion Model, Generative model, Brain Imaging
+
+## **Model Description**
+This model is trained using the Latent Diffusion Model architecture [1] and is used for the synthesis of conditioned 3D
+brain MRI data. The model is divided into two parts: an autoencoder with a KL-regularisation model that compresses data
+into a latent space and a diffusion model that learns to generate conditioned synthetic latent representations. This
+model is conditioned on age, sex, the volume of ventricular cerebrospinal fluid, and brain volume normalised for head size.
+
+
+
+Figure 1 - Synthetic image from the model.
+
+
+## **Data**
+The model was trained on brain data from 31,740 participants from the UK Biobank [2]. We used high-resolution 3D T1w MRI with voxel size of 1mm3, resulting in volumes with 160 x 224 x 160 voxels
+
+#### **Preprocessing**
+We used UniRes [3] to perform a rigid body registration to a common MNI space for image pre-processing. The voxel intensity was normalised to be between [0, 1].
+
+## **Performance**
+This model achieves the following results on UK Biobank: an FID of 0.0076, an MS-SSIM of 0.6555, and a 4-G-R-SSIM of 0.3883.
+
+Please, check Table 1 of the original paper for more details regarding evaluation results.
+
+
+## **commands example**
+
+Execute sampling:
+
+```shell
+python -m monai.bundle run --config_file configs/inference.json --gender 1.0 --age 0.7 --ventricular_vol 0.7 --brain_vol 0.5
+```
+
+All conditioning are expected to have values between 0 and 1
+
+## Using a new version of the model
+
+If you want to use the checkpoints from a newly fine-tuned model, you need to set parameter load_old to 0 when you run inference,
+to avoid the function load_old_state_dict being called instead of load_state_dict to be called, currently default, as it is
+required to load the checkpoint from the original GenerativeModels repository.
+
+```shell
+python -m monai.bundle run --config_file configs/inference.json --gender 1.0 --age 0.7 --ventricular_vol 0.7 --brain_vol 0.5 --load_old 0
+```
+
+## **Citation Info**
+
+```bibtex
+@inproceedings{pinaya2022brain,
+ title={Brain imaging generation with latent diffusion models},
+ author={Pinaya, Walter HL and Tudosiu, Petru-Daniel and Dafflon, Jessica and Da Costa, Pedro F and Fernandez, Virginia and Nachev, Parashkev and Ourselin, Sebastien and Cardoso, M Jorge},
+ booktitle={MICCAI Workshop on Deep Generative Models},
+ pages={117--126},
+ year={2022},
+ organization={Springer}
+}
+```
+
+## **References**
+
+Example:
+
+[1] Pinaya, Walter HL, et al. "Brain imaging generation with latent diffusion models." MICCAI Workshop on Deep Generative Models. Springer, Cham, 2022.
+
+[2] Sudlow, Cathie, et al. "UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age." PLoS medicine 12.3 (2015): e1001779.
+
+[3] Brudfors, Mikael, et al. "MRI super-resolution using multi-channel total variation." Annual Conference on Medical Image Understanding and Analysis. Springer, Cham, 2018.
diff --git a/models/brain_image_synthesis_latent_diffusion_model/docs/figure_1.png b/models/brain_image_synthesis_latent_diffusion_model/docs/figure_1.png
new file mode 100644
index 00000000..b3bed96a
Binary files /dev/null and b/models/brain_image_synthesis_latent_diffusion_model/docs/figure_1.png differ
diff --git a/models/brain_image_synthesis_latent_diffusion_model/large_files.yml b/models/brain_image_synthesis_latent_diffusion_model/large_files.yml
new file mode 100644
index 00000000..5dbed08f
--- /dev/null
+++ b/models/brain_image_synthesis_latent_diffusion_model/large_files.yml
@@ -0,0 +1,9 @@
+large_files:
+ - path: "models/autoencoder.pt"
+ url: "https://drive.google.com/uc?export=download&id=1CZHwxHJWybOsDavipD0EorDPOo_mzNeX"
+ hash_val: "329e97b3085643ff235f11f049856242"
+ hash_type: "md5"
+ - path: "models/model.pt"
+ url: "https://drive.google.com/uc?export=download&id=1XO-ak93ZuOcGTCpgRtqgIeZq3dG5ExN6"
+ hash_val: "21c3047556fb671caf0556f1cce6ef22"
+ hash_type: "md5"
diff --git a/models/brain_image_synthesis_latent_diffusion_model/scripts/__init__.py b/models/brain_image_synthesis_latent_diffusion_model/scripts/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/models/brain_image_synthesis_latent_diffusion_model/scripts/sampler.py b/models/brain_image_synthesis_latent_diffusion_model/scripts/sampler.py
new file mode 100644
index 00000000..3058c470
--- /dev/null
+++ b/models/brain_image_synthesis_latent_diffusion_model/scripts/sampler.py
@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import torch
+import torch.nn as nn
+from monai.utils import optional_import
+from torch.cuda.amp import autocast
+
+tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
+
+
+class Sampler:
+ def __init__(self) -> None:
+ super().__init__()
+
+ @torch.no_grad()
+ def sampling_fn(
+ self,
+ input_noise: torch.Tensor,
+ autoencoder_model: nn.Module,
+ diffusion_model: nn.Module,
+ scheduler: nn.Module,
+ conditioning: torch.Tensor,
+ ) -> torch.Tensor:
+ if has_tqdm:
+ progress_bar = tqdm(scheduler.timesteps)
+ else:
+ progress_bar = iter(scheduler.timesteps)
+
+ image = input_noise
+ cond_concat = conditioning.squeeze(1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
+ cond_concat = cond_concat.expand(list(cond_concat.shape[0:2]) + list(input_noise.shape[2:]))
+ for t in progress_bar:
+ with torch.no_grad():
+ model_output = diffusion_model(
+ torch.cat((image, cond_concat), dim=1),
+ timesteps=torch.Tensor((t,)).to(input_noise.device).long(),
+ context=conditioning,
+ )
+ image, _ = scheduler.step(model_output, t, image)
+
+ with torch.no_grad():
+ with autocast():
+ sample = autoencoder_model.decode_stage_2_outputs(image)
+
+ return sample