diff --git a/chromalive/my_conf/dataset/BBBC021/BBBC021_196_nocodazole_inference.py b/chromalive/my_conf/dataset/BBBC021/BBBC021_196_nocodazole_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fd645605155ba077146456026029f9c41f1d302
--- /dev/null
+++ b/chromalive/my_conf/dataset/BBBC021/BBBC021_196_nocodazole_inference.py
@@ -0,0 +1,26 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC021.BBBC021_196_inference import dataset
+
+# Nocodazole classes + DMSO
+CLASSES_IN_ORDER = (
+    "DMSO",
+    "nocodazole_0.001",
+    "nocodazole_0.003",
+    "nocodazole_0.01",
+    "nocodazole_0.03",
+    "nocodazole_0.1",
+    "nocodazole_0.3",
+    "nocodazole_1.0",
+    "nocodazole_3.0",
+)
+assert dataset.dataset_params is not None
+ds_params = replace(dataset.dataset_params, sorting_func=lambda subdir: CLASSES_IN_ORDER.index(subdir.name))
+
+# Path and name
+dataset = replace(
+    dataset,
+    name=dataset.name + "_nocodazole",
+    dataset_params=ds_params,
+    path="/projects/static2dynamic/datasets/BBBC021/196x196/nocodazole",
+)
diff --git a/chromalive/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered.yaml b/chromalive/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7c4133274ac9c90bb7ec8fde4fe2736a3724fa30
--- /dev/null
+++ b/chromalive/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered.yaml
@@ -0,0 +1,23 @@
+name: Jurkat_brightfield_fully_ordered
+
+path: /projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed
+path_to_single_parquet: /projects/static2dynamic/datasets/Jurkat/Jurkat_brightfield__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [ 1, 66, 66 ]
+
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5 ]
+    std: [ 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+
+fully_ordered: true
diff --git a/chromalive/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered_inference.py b/chromalive/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..b847a2ea0da14b9271d0561105275ead359de4c7
--- /dev/null
+++ b/chromalive/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered_inference.py
@@ -0,0 +1,4 @@
+from GaussianProxy.conf.dataset.Jurkat.Jurkat_brightfield_fully_ordered_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed"
+dataset.path_to_single_parquet = "/projects/static2dynamic/datasets/Jurkat/Jurkat_brightfield__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet"
diff --git a/chromalive/my_conf/dataset/Jurkat/Jurkat_inference_hard_aug.py b/chromalive/my_conf/dataset/Jurkat/Jurkat_inference_hard_aug.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc826ec44e040ea3bdf9931f7434e47f5345cb9c
--- /dev/null
+++ b/chromalive/my_conf/dataset/Jurkat/Jurkat_inference_hard_aug.py
@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.Jurkat.Jurkat_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/Jurkat/rgb_images_all_cell_cycles_hard_augmented",
+)
diff --git a/chromalive/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_inference.py b/chromalive/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9e9e16f31c3c75bb780bc4832868bd0c6cebcce
--- /dev/null
+++ b/chromalive/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.NASH_fibrosis.NASH_fibrosis_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/NASH/prepared_data/fibrosis"
diff --git a/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis.yaml b/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..663b957dadc1791cc3cefb75d269b3579204ea5c
--- /dev/null
+++ b/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis.yaml
@@ -0,0 +1,24 @@
+name: NASH_steatosis
+path: /projects/static2dynamic/datasets/NASH/prepared_data/steatosis
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  # Convert to float32 (and normalize to [0, 1]) before resizing
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  # Random crop from 299x299 to 192x192, then resize to 128x128
+  - _target_: torchvision.transforms.RandomCrop
+    size: 192
+  - _target_: torchvision.transforms.Resize
+    size: 128
+  # Normalize to [-1, 1]
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  # Random 8x square augmentations
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]
diff --git a/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered_inference.py b/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9918cc6e00541668f1d52847f662fad05fcf329
--- /dev/null
+++ b/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered_inference.py
@@ -0,0 +1,4 @@
+from GaussianProxy.conf.dataset.NASH_steatosis.NASH_steatosis_fully_ordered_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/NASH/prepared_data/steatosis"
+dataset.path_to_single_parquet = "/projects/static2dynamic/datasets/NASH/prepared_data/NASH_steatosis__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet"
diff --git a/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis_inference.py b/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e1d91853a23054a41429f141a048594f0446074
--- /dev/null
+++ b/chromalive/my_conf/dataset/NASH_steatosis/NASH_steatosis_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.NASH_steatosis.NASH_steatosis_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/NASH/prepared_data/steatosis"
diff --git a/chromalive/my_conf/dataset/biotine/biotine_png_128_inference.py b/chromalive/my_conf/dataset/biotine/biotine_png_128_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f885668d8f708cce05382366a32dc71a3da7976
--- /dev/null
+++ b/chromalive/my_conf/dataset/biotine/biotine_png_128_inference.py
@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.biotine.biotine_png_128_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255",
+)
diff --git a/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_crop_inference.py b/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_crop_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..84c134f0c7aa14e0f25ae95c629e26d719e7bd22
--- /dev/null
+++ b/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_crop_inference.py
@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_full_circle_augs_2048_crop_inference import (
+    dataset,
+)
+
+dataset = replace(
+    dataset, path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented"
+)
diff --git a/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_inference.py b/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..56a796fecda6f42247e83ee7b505cf738c625423
--- /dev/null
+++ b/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_inference.py
@@ -0,0 +1,10 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_full_circle_augs_2048_precrop_inference import (
+    dataset,
+)
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented_2048_crop",
+)
diff --git a/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2560_crop_inference.py b/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2560_crop_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b4d5afef51491f9cb4907ab3c6f7a2eebf301b4
--- /dev/null
+++ b/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2560_crop_inference.py
@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_full_circle_augs_2560_crop_inference import (
+    dataset,
+)
+
+dataset = replace(
+    dataset, path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented"
+)
diff --git a/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_hard_aug_inference.py b/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_hard_aug_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f92904332804efa76648f229efa8d99331c7800
--- /dev/null
+++ b/chromalive/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_hard_aug_inference.py
@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_inference import dataset
+
+dataset = replace(
+    dataset,
+    name="diabetic_retinopathy_inference_hard_augmented",
+    path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset/train_hard_augmented",
+)
diff --git a/chromalive/my_conf/dataset/ependymal_context/ependymal_context.yaml b/chromalive/my_conf/dataset/ependymal_context/ependymal_context.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4301cfd2c648085445c33cb570d1a0941930efa0
--- /dev/null
+++ b/chromalive/my_conf/dataset/ependymal_context/ependymal_context.yaml
@@ -0,0 +1,16 @@
+name: ependymal_context
+path: /projects/static2dynamic/datasets/ependymal/prepared_dataset_context
+data_shape: [3, 256, 256]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8
diff --git a/chromalive/my_conf/dataset/ependymal_context/ependymal_context_avg_t_fully_ordered_inference.py b/chromalive/my_conf/dataset/ependymal_context/ependymal_context_avg_t_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..83ce32546f7aa4e097ca079b7ae975bbaec9c701
--- /dev/null
+++ b/chromalive/my_conf/dataset/ependymal_context/ependymal_context_avg_t_fully_ordered_inference.py
@@ -0,0 +1,31 @@
+from dataclasses import replace
+from pathlib import Path
+
+from GaussianProxy.conf.dataset.ependymal_context.ependymal_context_fully_ordered_inference import dataset
+from GaussianProxy.conf.training_conf import DatasetParams
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+
+
+# separate gt needs a workaround for sorting_func
+def sorting_func(subdir: Path):
+    if subdir.name == "all_imgs":
+        return 0
+    else:
+        raise ValueError(f"unexpected subdir: {subdir}")
+
+
+ds_params = DatasetParams(
+    file_extension="png",
+    key_transform=str,
+    sorting_func=sorting_func,
+    dataset_class=ContinuousTimeImageDataset,
+)
+
+dataset = replace(
+    dataset,
+    name="ependymal_context_avg_t_separate_gt_fully_ordered",
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_context_avg_pt_from_01_noised_facebook_dinov2-with-registers-giant/all_imgs",
+    path_to_single_parquet="/projects/static2dynamic/datasets/ependymal/prepared_dataset_context_avg_pt_from_01_noised_facebook_dinov2-with-registers-giant/ependymal_context__continuous_time_predictions__avg_t_preds.parquet",
+    dataset_params=ds_params,
+    separate_gt_starting_class_path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_context/0"
+)
diff --git a/chromalive/my_conf/dataset/ependymal_context/ependymal_context_fully_ordered_inference.py b/chromalive/my_conf/dataset/ependymal_context/ependymal_context_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..b62649854264f1653da9f1d727421730615f77b1
--- /dev/null
+++ b/chromalive/my_conf/dataset/ependymal_context/ependymal_context_fully_ordered_inference.py
@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ependymal_context.ependymal_context_fully_ordered_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_context",
+    path_to_single_parquet="/projects/static2dynamic/datasets/ependymal/ependymal_context__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+)
diff --git a/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout.yaml b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..67fc08a415914501a2d04527a3555ddc498f32f4
--- /dev/null
+++ b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout.yaml
@@ -0,0 +1,17 @@
+name: ependymal_cutout
+path: /projects/static2dynamic/datasets/ependymal/prepared_dataset_crop
+data_shape: [ 3, 256, 256 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+selected_dists: [ 1, 2, 3, 4, 5, 6 ] # 0 is the trash class!
diff --git a/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_01_noised_fc_augs_sep_gt_fully_ordered_inference.py b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_01_noised_fc_augs_sep_gt_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..b216741f454716031f9c01fa984ce0da9a1d64d6
--- /dev/null
+++ b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_01_noised_fc_augs_sep_gt_fully_ordered_inference.py
@@ -0,0 +1,18 @@
+from dataclasses import replace
+
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+from my_conf.dataset.ependymal_cutout.ependymal_cutout_01_noised_fc_augs_sep_gt_inference import dataset
+
+assert dataset.dataset_params is not None
+dataset_params = replace(
+    dataset.dataset_params,
+    dataset_class=ContinuousTimeImageDataset,
+)
+
+dataset = replace(
+    dataset,
+    fully_ordered=True,
+    path_to_single_parquet="/projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/ependymal_cutout_01_noised_full_circle_augs/ependymal_cutout_01_noised_full_circle_augs__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    selected_dists=["all_imgs"],
+    dataset_params=dataset_params,
+)
diff --git a/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_01_noised_separate_gt_inference.py b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_01_noised_separate_gt_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cb66f6d9fa66d668624fb5839d8871e9da68344
--- /dev/null
+++ b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_01_noised_separate_gt_inference.py
@@ -0,0 +1,19 @@
+from dataclasses import replace
+from pathlib import Path
+
+from GaussianProxy.conf.dataset.ependymal_cutout.ependymal_cutout_inference import dataset
+
+# note the data organization is special with separate ground truth folder
+assert dataset.dataset_params is not None
+dataset_params = replace(
+    dataset.dataset_params,
+    sorting_func=lambda subdir: str(subdir.name) if isinstance(subdir, Path) else str(subdir),
+)  # subdirs are "ground_truth" and "all_imgs" now...
+
+dataset = replace(
+    dataset,
+    name="ependymal_cutout_01_noised",
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_noised_0.1_crop/",
+    dataset_params=dataset_params,
+    separate_gt_starting_class_path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_noised_0.1_crop/ground_truths/1",
+)
diff --git a/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_03_noised_separate_gt_fully_ordered_inference.py b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_03_noised_separate_gt_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..5517fd0b6bb735487c298727d126090536e16193
--- /dev/null
+++ b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_03_noised_separate_gt_fully_ordered_inference.py
@@ -0,0 +1,18 @@
+from dataclasses import replace
+
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+from my_conf.dataset.ependymal_cutout.ependymal_cutout_03_noised_separate_gt_inference import dataset
+
+assert dataset.dataset_params is not None
+dataset_params = replace(
+    dataset.dataset_params,
+    dataset_class=ContinuousTimeImageDataset,
+)
+
+dataset = replace(
+    dataset,
+    fully_ordered=True,
+    path_to_single_parquet="/projects/static2dynamic/datasets/ependymal/ependymal_cutout_03_noised__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    dataset_params=dataset_params,
+    selected_dists=["all_imgs"],
+)
diff --git a/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_white_bg_separate_gt_inference.py b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_white_bg_separate_gt_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..807c4c6bcee124ac2b3c3ac0cb8a7251e60d71e6
--- /dev/null
+++ b/chromalive/my_conf/dataset/ependymal_cutout/ependymal_cutout_white_bg_separate_gt_inference.py
@@ -0,0 +1,16 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ependymal_cutout.ependymal_cutout_inference import dataset
+
+# note the data organization is special with separate ground truth folder
+assert dataset.dataset_params is not None
+dataset_params = replace(
+    dataset.dataset_params, sorting_func=lambda subdir: str(subdir.name)
+)  # subdirs are "ground_truth" and "all_imgs" now...
+
+dataset = replace(
+    dataset,
+    name="ependymal_cutout_white_bg",
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_white_bg_crop/",
+    dataset_params=dataset_params,
+)
diff --git a/chromalive/my_conf/dataset/human_embryo/human_embryo_fully_ordered_inference.py b/chromalive/my_conf/dataset/human_embryo/human_embryo_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c721acb02a7a6aed59823cea62ba861992f8bd0
--- /dev/null
+++ b/chromalive/my_conf/dataset/human_embryo/human_embryo_fully_ordered_inference.py
@@ -0,0 +1,15 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.human_embryo.human_embryo_inference import dataset
+from GaussianProxy.utils.data import ContinuousTimeImageDataset1D
+
+assert dataset.dataset_params is not None
+ds_params = replace(dataset.dataset_params, dataset_class=ContinuousTimeImageDataset1D)
+
+dataset = replace(
+    dataset,
+    path="/projects/imagesets3/2022_Gomez/reformated_phases/phases",
+    fully_ordered=True,
+    path_to_single_parquet="/projects/imagesets3/2022_Gomez/reformated_phases/human_embryo__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    dataset_params=ds_params,
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_fully_ordered_inference.py b/diabetic_retinopathy/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..29419d6196c43f0aa4700ca97214606d539dc509
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_fully_ordered_inference.py
@@ -0,0 +1,10 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC021.BBBC021_196_fully_ordered_inference import dataset
+
+dataset = replace(
+    dataset,
+    name=dataset.name + "_docetaxel",
+    path="/projects/static2dynamic/datasets/BBBC021/196x196/docetaxel",
+    path_to_single_parquet="/projects/static2dynamic/datasets/BBBC021/196x196/BBBC021_196_docetaxel__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_hard_aug_inference.py b/diabetic_retinopathy/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_hard_aug_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd135b459e112c4860f979c12d5bf7064e5032ba
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/BBBC021/BBBC021_196_docetaxel_hard_aug_inference.py
@@ -0,0 +1,9 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC021.BBBC021_196_hard_aug_inference import dataset
+
+dataset = replace(
+    dataset,
+    name=dataset.name + "_docetaxel",
+    path="/projects/static2dynamic/datasets/BBBC021/196x196/docetaxel_hard_augmented",
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/BBBC048/bbbc048.yaml b/diabetic_retinopathy/my_conf/dataset/BBBC048/bbbc048.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..10d8775e4a9a54bd5167de52272b15c4e3fc0d46
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/BBBC048/bbbc048.yaml
@@ -0,0 +1,21 @@
+name: BBBC048_fully_ordered
+
+path: /projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed_full_circle_augmented
+path_to_single_parquet: /projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/BBBC048/BBBC048__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [ 1, 48, 48 ]
+
+transforms:
+  _target_: torchvision.transforms.v2.Compose
+  transforms:
+  - _target_: torchvision.transforms.v2.ToDtype
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+    scale: true
+  - _target_: torchvision.transforms.v2.Normalize
+    mean: [ 0.5 ]
+    std: [ 0.5 ]
+
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+
+fully_ordered: true
diff --git a/diabetic_retinopathy/my_conf/dataset/BBBC048/bbbc048_inference.py b/diabetic_retinopathy/my_conf/dataset/BBBC048/bbbc048_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..502b4e3398627114cff7a979e77aad44259a0779
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/BBBC048/bbbc048_inference.py
@@ -0,0 +1,7 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.BBBC048.bbbc048_inference import dataset
+
+dataset = replace(
+    dataset, path="/projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed_full_circle_augmented"
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/ChromaLive6h_3ch_png_hard_aug.yaml b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/ChromaLive6h_3ch_png_hard_aug.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..426907c0883d4aac51c298f00f2e3f77fdda05e7
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/ChromaLive6h_3ch_png_hard_aug.yaml
@@ -0,0 +1,16 @@
+name: chromaLive6h_3ch_png_patches_380px_hard_aug
+path: /projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches_hard_augmented
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ] # move to [-1:1]
+    std: [ 0.5, 0.5, 0.5 ]
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+selected_dists:
diff --git a/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/ChromaLive6h_4ch_tif.yaml b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/ChromaLive6h_4ch_tif.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..694a2ba580d61c98606c8a5e60213ede4d0b5b5a
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/ChromaLive6h_4ch_tif.yaml
@@ -0,0 +1,19 @@
+name: chromaLive6h_4ch_tif_patches_380px
+path: /projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches
+data_shape: [ 4, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5, 0.5 ] # move to [-1:1]
+    std: [ 0.5, 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [ 0, 65536 ]
+expected_dtype: torch.uint16
+selected_dists: [ 'time_1', 'time_3', 'time_5', 'time_7', 'time_9', 'time_11', 'time_13' ]
diff --git a/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_fully_ordered_inference.py b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..20fc6d08aedbed7a927b05d3f3a6ad46cf67b8d9
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_fully_ordered_inference.py
@@ -0,0 +1,4 @@
+from GaussianProxy.conf.dataset.ChromaLive6h.chromalive6h_3ch_png_fully_ordered_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches"
+dataset.path_to_single_parquet = "/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/chromaLive6h_3ch_png_patches_380px__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet"
diff --git a/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_hard_aug_inference.py b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_hard_aug_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..02246ed5f528d5b04a2267c44ce65997de4c3596
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_hard_aug_inference.py
@@ -0,0 +1,6 @@
+from GaussianProxy.conf.dataset.ChromaLive6h.chromalive6h_3ch_png_inference import dataset
+
+dataset.name = "chromaLive6h_3ch_png_patches_380px_hard_aug"
+dataset.path = (
+    "/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches_hard_augmented"
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_inference.py b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7a99754fdf6a320867784d7f225c4580d8d9bf1
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ChromaLive6h/chromalive6h_3ch_png_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.ChromaLive6h.chromalive6h_3ch_png_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/20231017ChromaLive_6hr_4ch/MIP_normalized/paired_dataset/patches"
diff --git a/diabetic_retinopathy/my_conf/dataset/ChromaLiveTL24h/ChromaLiveTL24h.yaml b/diabetic_retinopathy/my_conf/dataset/ChromaLiveTL24h/ChromaLiveTL24h.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..491c42c2bc5ecc036ec55de7ede90b8c1ed88a8b
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ChromaLiveTL24h/ChromaLiveTL24h.yaml
@@ -0,0 +1,19 @@
+name: chromalive_tl_24h_380px
+path: /projects/static2dynamic/datasets/20230920ChromaLiveTL_24hr4ch/ch_4_3_1___norm_whole_ds_per_channel_per_zslice_0_99perc___patches_380
+data_shape: [3, 256, 256]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 256
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.5, 0.5, 0.5] # move to [-1:1]
+    std: [0.5, 0.5, 0.5]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8
+selected_dists: ['time_1', 'time_7', 'time_13', 'time_19', 'time_25', 'time_31', 'time_37', 'time_43', 'time_49', 'time_55', 'time_61', 'time_67', 'time_73', 'time_79', 'time_85', 'time_91', 'time_97', 'time_103', 'time_109', 'time_115', 'time_121', 'time_127', 'time_133', 'time_139', 'time_145']
diff --git a/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_brightfield_to_3D_inference.py b/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_brightfield_to_3D_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d5c7af253559ce6fbec7561ff59e4a9f9c7e4e6
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_brightfield_to_3D_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.DeepCycle.deepcycle_brightfield_to_3D_inference import dataset
+
+dataset.path = "/projects/imagesets2/DeepCycle/cells/128x128"
diff --git a/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_hoechst_brightfield_to_3D_inference.py b/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_hoechst_brightfield_to_3D_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ffcf9696a44cd5652089f937d88e2b630002f32
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_hoechst_brightfield_to_3D_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.DeepCycle.deepcycle_hoechst_brightfield_to_3D_inference import dataset
+
+dataset.path = "/projects/imagesets2/DeepCycle/cells/128x128"
diff --git a/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_inference.py b/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4f7cd69d76b72676b9513a5e44dc91ccf130028
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.DeepCycle.deepcycle_inference import dataset
+
+dataset.path = "/projects/imagesets2/DeepCycle/cells/128x128"
diff --git a/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_markers_inference.py b/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_markers_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..129730fd4014e545f15100c50b4fd60481782e17
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/DeepCycle/deepcycle_markers_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.DeepCycle.deepcycle_markers_inference import dataset
+
+dataset.path = "/projects/imagesets2/DeepCycle/cells/128x128"
diff --git a/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat.yaml b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6dae2eb460d55c87abf178bad5c7a2f4d43587cb
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat.yaml
@@ -0,0 +1,16 @@
+name: Jurkat
+path: /projects/static2dynamic/datasets/Jurkat/rgb_images_all_cell_cycles
+data_shape: [3, 66, 66]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8
diff --git a/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered.yaml b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7c4133274ac9c90bb7ec8fde4fe2736a3724fa30
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered.yaml
@@ -0,0 +1,23 @@
+name: Jurkat_brightfield_fully_ordered
+
+path: /projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed
+path_to_single_parquet: /projects/static2dynamic/datasets/Jurkat/Jurkat_brightfield__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [ 1, 66, 66 ]
+
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5 ]
+    std: [ 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+
+fully_ordered: true
diff --git a/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered_inference.py b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..b847a2ea0da14b9271d0561105275ead359de4c7
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_fully_ordered_inference.py
@@ -0,0 +1,4 @@
+from GaussianProxy.conf.dataset.Jurkat.Jurkat_brightfield_fully_ordered_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed"
+dataset.path_to_single_parquet = "/projects/static2dynamic/datasets/Jurkat/Jurkat_brightfield__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet"
diff --git a/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_inference.py b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d9cf49c06507bbebc2f57319f952ce9d5a3073b
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_brightfield_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.Jurkat.Jurkat_brightfield_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/Jurkat/brightfield_reprocessed"
diff --git a/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_fully_ordered.yaml b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_fully_ordered.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..24e6e21de17a84ad77fba0ddd54fa0f770ea3cdf
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_fully_ordered.yaml
@@ -0,0 +1,18 @@
+name: Jurkat_fully_ordered_dinov2_regs_giant_ds_preproc
+path: /projects/static2dynamic/datasets/Jurkat/rgb_images_all_cell_cycles
+path_to_single_parquet: /projects/static2dynamic/datasets/Jurkat/Jurkat__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+data_shape: [ 3, 66, 66 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+fully_ordered: true
diff --git a/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_inference.py b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f9c110d8ff54daa5c3671481b9580b407d01724
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_inference.py
@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.Jurkat.Jurkat_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/Jurkat/rgb_images_all_cell_cycles",
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_inference_hard_aug.py b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_inference_hard_aug.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc826ec44e040ea3bdf9931f7434e47f5345cb9c
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/Jurkat/Jurkat_inference_hard_aug.py
@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.Jurkat.Jurkat_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/Jurkat/rgb_images_all_cell_cycles_hard_augmented",
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis.yaml b/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a53dc5ec231d267cb6fcdc50c7166e5a792c0d32
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis.yaml
@@ -0,0 +1,24 @@
+name: NASH_fibrosis
+path: /projects/static2dynamic/datasets/NASH/prepared_data/fibrosis
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  # Convert to float32 (and normalize to [0, 1]) before resizing
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  # Random crop from 299x299 to 192x192, then resize to 128x128
+  - _target_: torchvision.transforms.RandomCrop
+    size: 192
+  - _target_: torchvision.transforms.Resize
+    size: 128
+  # Normalize to [-1, 1]
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  # Random 8x square augmentations
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]
diff --git a/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_fully_ordered_inference.py b/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..58a1b0331176b90a44a2209db56dba19005ef998
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_fully_ordered_inference.py
@@ -0,0 +1,4 @@
+from GaussianProxy.conf.dataset.NASH_fibrosis.NASH_fibrosis_fully_ordered_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/NASH/prepared_data/fibrosis"
+dataset.path_to_single_parquet = "/projects/static2dynamic/datasets/NASH/prepared_data/NASH_fibrosis__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet"
diff --git a/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_inference.py b/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9e9e16f31c3c75bb780bc4832868bd0c6cebcce
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/NASH_fibrosis/NASH_fibrosis_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.NASH_fibrosis.NASH_fibrosis_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/NASH/prepared_data/fibrosis"
diff --git a/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered.yaml b/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2ee2b473282b4c7882ddced978e0b4165481f237
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered.yaml
@@ -0,0 +1,26 @@
+name: NASH_steatosis_fully_ordered_dinov2_regs_giant_ds_preproc
+path: /projects/static2dynamic/datasets/NASH/prepared_data/steatosis
+path_to_single_parquet: /projects/static2dynamic/datasets/NASH/prepared_data/NASH_steatosis__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  # Convert to float32 (and normalize to [0, 1]) before resizing
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  # Random crop from 299x299 to 192x192, then resize to 128x128
+  - _target_: torchvision.transforms.RandomCrop
+    size: 192
+  - _target_: torchvision.transforms.Resize
+    size: 128
+  # Normalize to [-1, 1]
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  # Random 8x square augmentations
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]
+fully_ordered: true
diff --git a/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered_inference.py b/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9918cc6e00541668f1d52847f662fad05fcf329
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_fully_ordered_inference.py
@@ -0,0 +1,4 @@
+from GaussianProxy.conf.dataset.NASH_steatosis.NASH_steatosis_fully_ordered_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/NASH/prepared_data/steatosis"
+dataset.path_to_single_parquet = "/projects/static2dynamic/datasets/NASH/prepared_data/NASH_steatosis__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet"
diff --git a/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_inference.py b/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e1d91853a23054a41429f141a048594f0446074
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/NASH_steatosis/NASH_steatosis_inference.py
@@ -0,0 +1,3 @@
+from GaussianProxy.conf.dataset.NASH_steatosis.NASH_steatosis_inference import dataset
+
+dataset.path = "/projects/static2dynamic/datasets/NASH/prepared_data/steatosis"
diff --git a/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128.yaml b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..49ffb167b04d435655c47b359aa8df480319f587
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128.yaml
@@ -0,0 +1,19 @@
+name: biotine_png
+path: /projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  # ConvertImageDtype also scales to [0; 1] (from the *implicit* expected range that depends on the incoming dtype...)
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]
diff --git a/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_fully_ordered.yaml b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_fully_ordered.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3cceb83582d9dff9cd838ae1c5f4ac34f99b2af2
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_fully_ordered.yaml
@@ -0,0 +1,21 @@
+name: biotine_png_fully_ordered
+path: /projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255
+path_to_single_parquet: /projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/biotine_png__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  # ConvertImageDtype also scales to [0; 1] (from the *implicit* expected range that depends on the incoming dtype...)
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]
+fully_ordered: true
diff --git a/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_fully_ordered_inference.py b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0ba77af280d7f14f8cf140a19b146664524c38b
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_fully_ordered_inference.py
@@ -0,0 +1,18 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.biotine.biotine_png_128_inference import dataset
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+
+assert dataset.dataset_params is not None
+updated_ds_params = replace(
+    dataset.dataset_params,
+    dataset_class=ContinuousTimeImageDataset,
+)
+
+dataset = replace(
+    dataset,
+    fully_ordered=True,
+    path="/projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255",
+    path_to_single_parquet="/projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/biotine_png__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    dataset_params=updated_ds_params,
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_hard_aug.yaml b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_hard_aug.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..07044d90813fe3acadb1a0d996adc1277f60ccbe
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_hard_aug.yaml
@@ -0,0 +1,16 @@
+name: biotine_png_hard_aug
+path: /projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255_hard_augmented
+data_shape: [ 3, 128, 128 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 128
+  # ConvertImageDtype also scales to [0; 1] (from the *implicit* expected range that depends on the incoming dtype...)
+  - _target_: torchvision.transforms.ConvertImageDtype
+    dtype: ${torch_dtype:float32}
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+selected_dists:
+expected_initial_data_range: [ 0, 255 ]
diff --git a/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_hard_aug_inference.py b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_hard_aug_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fda38ec48345e97464fead2ea49563c1c484cc9
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/biotine/biotine_png_128_hard_aug_inference.py
@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.biotine.biotine_png_128_hard_aug_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/biotine/3_channels_min_99_perc_normalized_rgb_stacks_png/patches_255_hard_augmented",
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy.yaml b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b458e5fc57edb75c902a3ddb5519f9064950f555
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy.yaml
@@ -0,0 +1,18 @@
+name: diabetic_retinopathy
+path: /projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset/train
+data_shape: [3, 256, 256]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.transforms.Resize
+    size: 256 # single int => image resized to (size * aspect_ratio, size) or (size, size * aspect_ratio) with aspect_ratio >= 1 preserved
+  - _target_: torchvision.transforms.v2.CenterCrop
+    size: 256 # square centered crop
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [0.5, 0.5, 0.5]
+    std: [0.5, 0.5, 0.5]
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8
diff --git a/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_crop_fully_ordered.yaml b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_crop_fully_ordered.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a485ac70368ed67577f3e43766139422a2315b58
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_crop_fully_ordered.yaml
@@ -0,0 +1,25 @@
+name: diabetic_retinopathy_2048_crop_fully_ordered
+
+path: /projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented
+path_to_single_parquet: /projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/balanced_classes__diabetic_retinopathy_2048_crop/balanced_classes__diabetic_retinopathy_2048_crop__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [3, 256, 256]
+
+transforms:
+  _target_: torchvision.transforms.v2.Compose
+  transforms:
+    - _target_: torchvision.transforms.v2.CenterCrop
+      size: 2048
+    - _target_: torchvision.transforms.v2.Resize
+      size: 256
+    - _target_: torchvision.transforms.v2.ToDtype # this also scales to [0; 1]
+      dtype: ${torch_dtype:float32}
+      scale: true
+    - _target_: torchvision.transforms.v2.Normalize
+      mean: [0.5, 0.5, 0.5]
+      std: [0.5, 0.5, 0.5]
+
+expected_initial_data_range: [0, 255]
+expected_dtype: torch.uint8
+
+fully_ordered: true
diff --git a/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_fully_ordered.yaml b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_fully_ordered.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f4ecf72521e33c733056c5d0927ed58711dad04b
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_fully_ordered.yaml
@@ -0,0 +1,21 @@
+name: diabetic_retinopathy_2048_precrop_fully_ordered
+
+path: /projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented_2048_crop
+path_to_single_parquet: /projects/static2dynamic/Thomas/ordering_datasets/facebook_dinov2-with-registers-giant_dataset_preproc/diabetic_retinopathy_full_circle_augs_2048_precrop/diabetic_retinopathy_full_circle_augs_2048_precrop__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet
+
+data_shape: [ 3, 256, 256 ]
+
+transforms:
+  _target_: torchvision.transforms.v2.Compose
+  transforms:
+  - _target_: torchvision.transforms.v2.ToDtype
+    dtype: ${torch_dtype:float32}
+    scale: true
+  - _target_: torchvision.transforms.v2.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+
+fully_ordered: true
diff --git a/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_inference.py b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..56a796fecda6f42247e83ee7b505cf738c625423
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_full_circle_augs_2048_precrop_inference.py
@@ -0,0 +1,10 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_full_circle_augs_2048_precrop_inference import (
+    dataset,
+)
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset_full_circle_augmented_2048_crop",
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_fully_ordered_inference.py b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_fully_ordered_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..c03ee29545d35168424e0d9541358fd77f66791f
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/diabetic_retinopathy/diabetic_retinopathy_fully_ordered_inference.py
@@ -0,0 +1,15 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.diabetic_retinopathy.diabetic_retinopathy_inference import dataset
+from GaussianProxy.utils.data import ContinuousTimeImageDataset
+
+assert dataset.dataset_params is not None
+updated_ds_params = replace(dataset.dataset_params, dataset_class=ContinuousTimeImageDataset)
+
+dataset = replace(
+    dataset,
+    fully_ordered=True,
+    path="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset/train",
+    path_to_single_parquet="/projects/static2dynamic/datasets/DiabeticRetinopathy/prepared_dataset/diabetic_retinopathy__continuous_time_predictions__facebook_dinov2-with-registers-giant_dataset_preproc.parquet",
+    dataset_params=updated_ds_params,
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/ependymal_context/ependymal_context_inference.py b/diabetic_retinopathy/my_conf/dataset/ependymal_context/ependymal_context_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9514fb17a5910cb32bfd7692534a4fefe2ee78d
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ependymal_context/ependymal_context_inference.py
@@ -0,0 +1,32 @@
+from torch import float32
+from torchvision.transforms import Compose, ConvertImageDtype, Normalize
+
+from GaussianProxy.conf.training_conf import DataSet, DatasetParams
+from GaussianProxy.utils.data import ImageDataset
+
+DEFINITION = 256
+NUMBER_OF_CHANNELS = 3
+
+transforms = Compose(
+    transforms=[
+        ConvertImageDtype(float32),
+        Normalize(mean=[0.5] * NUMBER_OF_CHANNELS, std=[0.5] * NUMBER_OF_CHANNELS),
+    ]
+)
+
+ds_params = DatasetParams(
+    file_extension="png",
+    key_transform=str,
+    sorting_func=lambda subdir: int(subdir.name),
+    dataset_class=ImageDataset,
+)
+
+dataset = DataSet(
+    name="ependymal_context",
+    data_shape=(NUMBER_OF_CHANNELS, DEFINITION, DEFINITION),
+    transforms=transforms,
+    expected_initial_data_range=(0, 255),
+    dataset_params=ds_params,
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_context",
+    selected_dists=["0", "3", "5", "7", "9", "16", "30"],  # ignore .REMOVED_IMAGES/16 (imaging artifacts)
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/ependymal_cutout/ependymal_cutout.yaml b/diabetic_retinopathy/my_conf/dataset/ependymal_cutout/ependymal_cutout.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..67fc08a415914501a2d04527a3555ddc498f32f4
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ependymal_cutout/ependymal_cutout.yaml
@@ -0,0 +1,17 @@
+name: ependymal_cutout
+path: /projects/static2dynamic/datasets/ependymal/prepared_dataset_crop
+data_shape: [ 3, 256, 256 ]
+transforms:
+  _target_: torchvision.transforms.transforms.Compose
+  transforms:
+  - _target_: torchvision.transforms.ConvertImageDtype # this also scales to [0; 1]
+    dtype: ${torch_dtype:float32} # passed dtype must be accessible as a "torch" attribute
+  - _target_: torchvision.transforms.Normalize
+    mean: [ 0.5, 0.5, 0.5 ]
+    std: [ 0.5, 0.5, 0.5 ]
+  - _target_: torchvision.transforms.RandomHorizontalFlip
+  - _target_: torchvision.transforms.RandomVerticalFlip
+  - _target_: GaussianProxy.utils.data.RandomRotationSquareSymmetry
+expected_initial_data_range: [ 0, 255 ]
+expected_dtype: torch.uint8
+selected_dists: [ 1, 2, 3, 4, 5, 6 ] # 0 is the trash class!
diff --git a/diabetic_retinopathy/my_conf/dataset/ependymal_cutout/ependymal_cutout_inference.py b/diabetic_retinopathy/my_conf/dataset/ependymal_cutout/ependymal_cutout_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..3458d8b0f72c8fc1042d2fcc4feb2b64bb96ece2
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/ependymal_cutout/ependymal_cutout_inference.py
@@ -0,0 +1,8 @@
+from dataclasses import replace
+
+from GaussianProxy.conf.dataset.ependymal_cutout.ependymal_cutout_inference import dataset
+
+dataset = replace(
+    dataset,
+    path="/projects/static2dynamic/datasets/ependymal/prepared_dataset_crop",
+)
diff --git a/diabetic_retinopathy/my_conf/dataset/imagenet_n01917289_hard_aug_inference.py b/diabetic_retinopathy/my_conf/dataset/imagenet_n01917289_hard_aug_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..5092fe7e19d910949c4816bd99d0a8b5a9531d81
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/dataset/imagenet_n01917289_hard_aug_inference.py
@@ -0,0 +1,36 @@
+from torch import float32
+from torchvision.transforms import Compose, ConvertImageDtype, Normalize, Resize
+from torchvision.transforms.v2 import CenterCrop
+
+from GaussianProxy.conf.training_conf import DataSet, DatasetParams
+from GaussianProxy.utils.data import ImageDataset
+
+DEFINITION = 128
+NUMBER_OF_CHANNELS = 3
+
+transforms = Compose(
+    transforms=[
+        # single int => image resized to (size * aspect_ratio, size) or (size, size * aspect_ratio) with aspect_ratio >= 1 preserved
+        Resize(DEFINITION),
+        CenterCrop(DEFINITION),  # pyright: ignore[reportAttributeAccessIssue]
+        ConvertImageDtype(float32),
+        Normalize(mean=[0.5] * NUMBER_OF_CHANNELS, std=[0.5] * NUMBER_OF_CHANNELS),
+    ]
+)
+
+ds_params = DatasetParams(
+    file_extension="JPEG",
+    key_transform=int,
+    sorting_func=lambda subdir: int(subdir.name),
+    dataset_class=ImageDataset,
+)
+
+dataset = DataSet(
+    path="/localtmp/tboyer/augmented_imagenet_n01917289",
+    name="imagenet_n01917289_hard_aug_inference",
+    data_shape=(NUMBER_OF_CHANNELS, DEFINITION, DEFINITION),
+    transforms=transforms,
+    selected_dists=None,  # not used
+    expected_initial_data_range=(0, 255),
+    dataset_params=ds_params,
+)
diff --git a/diabetic_retinopathy/my_conf/hydra/job_logging/custom.yaml b/diabetic_retinopathy/my_conf/hydra/job_logging/custom.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a0351b01adac5db3c464d4990751266af706f6c6
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/hydra/job_logging/custom.yaml
@@ -0,0 +1,35 @@
+version: 1
+formatters:
+  simple:
+    format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
+  colorlog:
+    (): colorlog.ColoredFormatter
+    format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s] - %(message)s'
+    log_colors:
+      DEBUG: purple
+      INFO: green
+      WARNING: yellow
+      ERROR: red
+      CRITICAL: red
+handlers:
+  console:
+    class: logging.StreamHandler
+    formatter: colorlog
+    stream: ext://sys.stdout
+    level: INFO
+  file:
+    class: logging.FileHandler
+    formatter: simple
+    filename: ${hydra:run.dir}/logs.log # unify logs from launcher and script
+    level: DEBUG
+root:
+  level: DEBUG
+  handlers:
+  - console
+  - file
+logger:
+  matplotlib:
+    level: INFO
+  PIL:
+    level: INFO
+disable_existing_loggers: true
diff --git a/diabetic_retinopathy/my_conf/net/net_128_3.py b/diabetic_retinopathy/my_conf/net/net_128_3.py
new file mode 100644
index 0000000000000000000000000000000000000000..994280ed4d0a7e6e1410e51f2b058157fb17c962
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_128_3.py
@@ -0,0 +1,22 @@
+from GaussianProxy.conf.training_conf import UNet2DConditionModelConfig, TimeEncoderConfig
+
+cross_attn_dim = 64
+
+net = UNet2DConditionModelConfig(
+    sample_size=128,
+    in_channels=3,
+    out_channels=3,
+    down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+    block_out_channels=(64, 128, 256),
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=128,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/net/net_128_3_big.py b/diabetic_retinopathy/my_conf/net/net_128_3_big.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a12e57ccfc47d7fd0a9ac9cf3647764361adac7
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_128_3_big.py
@@ -0,0 +1,22 @@
+from GaussianProxy.conf.training_conf import UNet2DConditionModelConfig, TimeEncoderConfig
+
+cross_attn_dim = 64
+
+net = UNet2DConditionModelConfig(
+    sample_size=128,
+    in_channels=3,
+    out_channels=3,
+    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
+    block_out_channels=(128, 256, 512),
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=128,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/net/net_128_4.py b/diabetic_retinopathy/my_conf/net/net_128_4.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dd136ffaff1c94858ad50bb075d780b7a49bc86
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_128_4.py
@@ -0,0 +1,22 @@
+from GaussianProxy.conf.training_conf import UNet2DConditionModelConfig, TimeEncoderConfig
+
+cross_attn_dim = 64
+
+net = UNet2DConditionModelConfig(
+    sample_size=128,
+    in_channels=4,
+    out_channels=4,
+    down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+    block_out_channels=(64, 128, 256),
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=128,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/net/net_196_3_12M.py b/diabetic_retinopathy/my_conf/net/net_196_3_12M.py
new file mode 100644
index 0000000000000000000000000000000000000000..83fa0274707bdde54fd0e1141deec96f779b5393
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_196_3_12M.py
@@ -0,0 +1,22 @@
+from GaussianProxy.conf.training_conf import UNet2DConditionModelConfig, TimeEncoderConfig
+
+cross_attn_dim = 64
+
+net = UNet2DConditionModelConfig(
+    sample_size=196,
+    in_channels=3,
+    out_channels=3,
+    down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+    block_out_channels=(96, 128, 128),
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=128,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/net/net_196_3_4M.py b/diabetic_retinopathy/my_conf/net/net_196_3_4M.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f099e27892046be5f1559b9e05032a8469d815d
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_196_3_4M.py
@@ -0,0 +1,22 @@
+from GaussianProxy.conf.training_conf import UNet2DConditionModelConfig, TimeEncoderConfig
+
+cross_attn_dim = 64
+
+net = UNet2DConditionModelConfig(
+    sample_size=196,
+    in_channels=3,
+    out_channels=3,
+    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
+    block_out_channels=(32, 64, 96),
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=128,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/net/net_256_3_6M.py b/diabetic_retinopathy/my_conf/net/net_256_3_6M.py
new file mode 100644
index 0000000000000000000000000000000000000000..96a31d7fa08df8eb436f02c98b53cb154ac10585
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_256_3_6M.py
@@ -0,0 +1,22 @@
+from GaussianProxy.conf.training_conf import TimeEncoderConfig, UNet2DConditionModelConfig
+
+cross_attn_dim = 64
+
+net = UNet2DConditionModelConfig(
+    sample_size=256,
+    in_channels=3,
+    out_channels=3,
+    down_block_types=("DownBlock2D", "DownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "UpBlock2D", "UpBlock2D"),
+    block_out_channels=(32, 64, 128),
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=128,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/net/net_256_3_7M.py b/diabetic_retinopathy/my_conf/net/net_256_3_7M.py
new file mode 100644
index 0000000000000000000000000000000000000000..b47baf8af721f2e80ad3650c59cb7c6150420880
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_256_3_7M.py
@@ -0,0 +1,22 @@
+from GaussianProxy.conf.training_conf import TimeEncoderConfig, UNet2DConditionModelConfig
+
+cross_attn_dim = 64
+
+net = UNet2DConditionModelConfig(
+    sample_size=256,
+    in_channels=3,
+    out_channels=3,
+    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
+    block_out_channels=(32, 96, 128),
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=128,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/net/net_48_1.py b/diabetic_retinopathy/my_conf/net/net_48_1.py
new file mode 100644
index 0000000000000000000000000000000000000000..df3328413ffc47ff49eb7860ee153939a7fccc22
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_48_1.py
@@ -0,0 +1,23 @@
+from GaussianProxy.conf.training_conf import TimeEncoderConfig, UNet2DConditionModelConfig
+
+cross_attn_dim = 32
+
+net = UNet2DConditionModelConfig(
+    sample_size=48,
+    in_channels=1,
+    out_channels=1,
+    down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+    block_out_channels=(16, 32, 64),
+    norm_num_groups=8,
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=64,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/net/net_Jurkat.py b/diabetic_retinopathy/my_conf/net/net_Jurkat.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2caa03e0745f70db79d5cf5b15f5033b43c2300
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/net/net_Jurkat.py
@@ -0,0 +1,22 @@
+from GaussianProxy.conf.training_conf import UNet2DConditionModelConfig, TimeEncoderConfig
+
+cross_attn_dim = 64
+
+net = UNet2DConditionModelConfig(
+    sample_size=66,  # torch.compile seems to *silently* crash on this sample size (gives NaN gradients)!
+    in_channels=3,
+    out_channels=3,
+    down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+    block_out_channels=(64, 128, 256),
+    layers_per_block=2,
+    act_fn="silu",
+    cross_attention_dim=cross_attn_dim,
+)
+
+time_encoder = TimeEncoderConfig(
+    encoding_dim=128,
+    time_embed_dim=cross_attn_dim,
+    flip_sin_to_cos=True,
+    downscale_freq_shift=1,
+)
diff --git a/diabetic_retinopathy/my_conf/scheduler/DDIM_3k_vpred_tresh_SNR_trailing.json b/diabetic_retinopathy/my_conf/scheduler/DDIM_3k_vpred_tresh_SNR_trailing.json
new file mode 100644
index 0000000000000000000000000000000000000000..f7994596907c2a827c8d9941c86adb19ed8e43f2
--- /dev/null
+++ b/diabetic_retinopathy/my_conf/scheduler/DDIM_3k_vpred_tresh_SNR_trailing.json
@@ -0,0 +1,19 @@
+{
+    "_class_name": "DDIMScheduler",
+    "_diffusers_version": "0.32.2",
+    "beta_end": 0.02,
+    "beta_schedule": "linear",
+    "beta_start": 0.0001,
+    "clip_sample": false,
+    "clip_sample_range": 1.0,
+    "dynamic_thresholding_ratio": 0.995,
+    "num_train_timesteps": 3000,
+    "prediction_type": "v_prediction",
+    "rescale_betas_zero_snr": true,
+    "sample_max_value": 1.0,
+    "set_alpha_to_one": true,
+    "steps_offset": 0,
+    "thresholding": true,
+    "timestep_spacing": "trailing",
+    "trained_betas": null
+}
\ No newline at end of file
diff --git a/diabetic_retinopathy/net/config.json b/diabetic_retinopathy/net/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..37afdc42e07467daa04af5d2554527d05a765bff
--- /dev/null
+++ b/diabetic_retinopathy/net/config.json
@@ -0,0 +1,64 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.35.2",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": 8,
+  "attention_type": "default",
+  "block_out_channels": [
+    32,
+    64,
+    128
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 64,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "DownBlock2D",
+    "DownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 3,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 3,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 256,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "CrossAttnUpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false
+}