diff --git a/sub-packages/bionemo-moco/documentation.md b/sub-packages/bionemo-moco/documentation.md
index 3480921055..7f1a5def59 100644
--- a/sub-packages/bionemo-moco/documentation.md
+++ b/sub-packages/bionemo-moco/documentation.md
@@ -29,12 +29,16 @@
 * [bionemo.moco.interpolants.continuous\_time.discrete](#mocointerpolantscontinuous_timediscrete)
 * [bionemo.moco.interpolants.continuous\_time.discrete.mdlm](#mocointerpolantscontinuous_timediscretemdlm)
 * [bionemo.moco.interpolants.continuous\_time.discrete.discrete\_flow\_matching](#mocointerpolantscontinuous_timediscretediscrete_flow_matching)
-* [bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport](#mocointerpolantscontinuous_timecontinuousoptimal_transport)
+* [bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport.ot\_types](#mocointerpolantscontinuous_timecontinuousoptimal_transportot_types)
+* [bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport.ot\_sampler](#mocointerpolantscontinuous_timecontinuousoptimal_transportot_sampler)
+* [bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport.equivariant\_ot\_sampler](#mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_sampler)
+* [bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport.kabsch\_augmentation](#mocointerpolantscontinuous_timecontinuousoptimal_transportkabsch_augmentation)
 * [bionemo.moco.interpolants.continuous\_time.continuous](#mocointerpolantscontinuous_timecontinuous)
 * [bionemo.moco.interpolants.continuous\_time.continuous.vdm](#mocointerpolantscontinuous_timecontinuousvdm)
 * [bionemo.moco.interpolants.continuous\_time.continuous.continuous\_flow\_matching](#mocointerpolantscontinuous_timecontinuouscontinuous_flow_matching)
 * [bionemo.moco.interpolants.continuous\_time](#mocointerpolantscontinuous_time)
 * [bionemo.moco.interpolants](#mocointerpolants)
+* [bionemo.moco.interpolants.batch\_augmentation](#mocointerpolantsbatch_augmentation)
 * [bionemo.moco.interpolants.discrete\_time.discrete.d3pm](#mocointerpolantsdiscrete_timediscreted3pm)
 * [bionemo.moco.interpolants.discrete\_time.discrete](#mocointerpolantsdiscrete_timediscrete)
 * [bionemo.moco.interpolants.discrete\_time.continuous.ddpm](#mocointerpolantsdiscrete_timecontinuousddpm)
@@ -1895,9 +1899,9 @@ Initialize the LinearInferenceSchedule.
 #### generate\_schedule
 
 ```python
-def generate_schedule(nsteps: Optional[int] = None,
-                      device: Optional[Union[str, torch.device]] = None,
-                      inclusive_end: bool = False) -> Tensor
+def generate_schedule(
+        nsteps: Optional[int] = None,
+        device: Optional[Union[str, torch.device]] = None) -> Tensor
 ```
 
 Generate the linear time schedule as a tensor.
@@ -2461,11 +2465,31 @@ Applies this mask to the updated state xt.
 
 - `Tensor` - The updated state.
 
-<a id="mocointerpolantscontinuous_timecontinuousoptimal_transport"></a>
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_types"></a>
+
+# bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport.ot\_types
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_typesOptimalTransportType"></a>
+
+## OptimalTransportType Objects
+
+```python
+class OptimalTransportType(Enum)
+```
 
-# bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport
+An enumeration representing the type ofOptimal Transport that can be used in Continuous Flow Matching.
 
-<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportOTSampler"></a>
+- **EXACT**: Standard mini batch optimal transport defined in  https://arxiv.org/pdf/2302.00482.
+- **EQUIVARIANT**: Adding roto/translation optimization to mini batch OT see https://arxiv.org/pdf/2306.15030  https://arxiv.org/pdf/2312.07168 4.2.
+- **KABSCH**: Simple Kabsch alignment between each data and noise point, No permuation # https://arxiv.org/pdf/2410.22388 Sec 3.2
+
+These prediction types can be used to train neural networks for specific tasks, such as denoising, image synthesis, or time-series forecasting.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_sampler"></a>
+
+# bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport.ot\_sampler
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_samplerOTSampler"></a>
 
 ## OTSampler Objects
 
@@ -2478,12 +2502,12 @@ Sampler for Exact Mini-batch Optimal Transport Plan.
 OTSampler implements sampling coordinates according to an OT plan (wrt squared Euclidean cost)
 with different implementations of the plan calculation. Code is adapted from https://github.com/atong01/conditional-flow-matching/blob/main/torchcfm/optimal_transport.py
 
-<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportOTSampler__init__"></a>
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_samplerOTSampler__init__"></a>
 
 #### \_\_init\_\_
 
 ```python
-def __init__(method: str,
+def __init__(method: str = "exact",
              device: Union[str, torch.device] = "cpu",
              num_threads: int = 1) -> None
 ```
@@ -2502,7 +2526,7 @@ Initialize the OTSampler class.
 - `ValueError` - If the OT solver is not documented.
 - `NotImplementedError` - If the OT solver is not implemented.
 
-<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportOTSamplerto_device"></a>
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_samplerOTSamplerto_device"></a>
 
 #### to\_device
 
@@ -2522,7 +2546,7 @@ Moves all internal tensors to the specified device and updates the `self.device`
   This method is used to transfer the internal state of the OTSampler to a different device.
   It updates the `self.device` attribute to reflect the new device and moves all internal tensors to the specified device.
 
-<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportOTSamplersample_map"></a>
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_samplerOTSamplersample_map"></a>
 
 #### sample\_map
 
@@ -2545,7 +2569,7 @@ Draw source and target samples from pi $(x,z) \sim \pi$.
 
 - `Tuple` - tuple of 2 tensors, represents the indices of noise and data samples from pi.
 
-<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportOTSamplerget_ot_matrix"></a>
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_samplerOTSamplerget_ot_matrix"></a>
 
 #### get\_ot\_matrix
 
@@ -2568,7 +2592,7 @@ Compute the OT matrix between a source and a target minibatch.
 
 - `p` _Tensor_ - shape (bs, bs), the OT matrix between noise and data in minibatch.
 
-<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportOTSamplerapply_ot"></a>
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportot_samplerOTSamplerapply_ot"></a>
 
 #### apply\_ot
 
@@ -2578,7 +2602,7 @@ def apply_ot(
     x1: Tensor,
     mask: Optional[Tensor] = None,
     replace: Bool = False,
-    preserve: Optional[Literal["noise", "x0", "data", "x1"]] = "x0"
+    sort: Optional[Literal["noise", "x0", "data", "x1"]] = "x0"
 ) -> Tuple[Tensor, Tensor, Optional[Tensor]]
 ```
 
@@ -2593,13 +2617,273 @@ minibatch and draw source and target samples from pi $(x,z) \sim \pi$.
 - `x1` _Tensor_ - shape (bs, *dim), data from source minibatch.
 - `mask` _Optional[Tensor], optional_ - mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
 - `replace` _bool_ - sampling w/ or w/o replacement from the OT plan, default to False.
-- `preserve` _str_ - Optional Literal string to sort either x1 or x0 based on the input.
+- `sort` _str_ - Optional Literal string to sort either x1 or x0 based on the input.
 
 
 **Returns**:
 
 - `Tuple` - tuple of 2 tensors or 3 tensors if mask is used, represents the noise (plus mask) and data samples following OT plan pi.
 
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_sampler"></a>
+
+# bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport.equivariant\_ot\_sampler
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_samplerEquivariantOTSampler"></a>
+
+## EquivariantOTSampler Objects
+
+```python
+class EquivariantOTSampler()
+```
+
+Sampler for Mini-batch Optimal Transport Plan with cost calculated after Kabsch alignment.
+
+EquivariantOTSampler implements sampling coordinates according to an OT plan
+(wrt squared Euclidean cost after Kabsch alignment) with different implementations of the plan calculation.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_samplerEquivariantOTSampler__init__"></a>
+
+#### \_\_init\_\_
+
+```python
+def __init__(method: str = "exact",
+             device: Union[str, torch.device] = "cpu",
+             num_threads: int = 1) -> None
+```
+
+Initialize the OTSampler class.
+
+**Arguments**:
+
+- `method` _str_ - Choose which optimal transport solver you would like to use. Currently only support exact OT solvers (pot.emd).
+- `device` _Union[str, torch.device], optional_ - The device on which to run the interpolant, either "cpu" or a CUDA device (e.g. "cuda:0"). Defaults to "cpu".
+- `num_threads` _Union[int, str], optional_ - Number of threads to use for OT solver. If "max", uses the maximum number of threads. Default is 1.
+
+
+**Raises**:
+
+- `ValueError` - If the OT solver is not documented.
+- `NotImplementedError` - If the OT solver is not implemented.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_samplerEquivariantOTSamplerto_device"></a>
+
+#### to\_device
+
+```python
+def to_device(device: str)
+```
+
+Moves all internal tensors to the specified device and updates the `self.device` attribute.
+
+**Arguments**:
+
+- `device` _str_ - The device to move the tensors to (e.g. "cpu", "cuda:0").
+
+
+**Notes**:
+
+  This method is used to transfer the internal state of the OTSampler to a different device.
+  It updates the `self.device` attribute to reflect the new device and moves all internal tensors to the specified device.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_samplerEquivariantOTSamplersample_map"></a>
+
+#### sample\_map
+
+```python
+def sample_map(pi: Tensor,
+               batch_size: int,
+               replace: Bool = False) -> Tuple[Tensor, Tensor]
+```
+
+Draw source and target samples from pi $(x,z) \sim \pi$.
+
+**Arguments**:
+
+- `pi` _Tensor_ - shape (bs, bs), the OT matrix between noise and data in minibatch.
+- `batch_size` _int_ - The batch size of the minibatch.
+- `replace` _bool_ - sampling w/ or w/o replacement from the OT plan, default to False.
+
+
+**Returns**:
+
+- `Tuple` - tuple of 2 tensors, represents the indices of noise and data samples from pi.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_samplerEquivariantOTSamplerkabsch_align"></a>
+
+#### kabsch\_align
+
+```python
+def kabsch_align(target: Tensor, noise: Tensor) -> Tensor
+```
+
+Find the Rotation matrix (R) such that RMSD is minimized between target @ R.T and noise.
+
+**Arguments**:
+
+- `target` _Tensor_ - shape (N, *dim), data from source minibatch.
+- `noise` _Tensor_ - shape (N, *dim), noise from source minibatch.
+
+
+**Returns**:
+
+- `R` _Tensor_ - shape (*dim, *dim), the rotation matrix.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_samplerEquivariantOTSamplerget_ot_matrix"></a>
+
+#### get\_ot\_matrix
+
+```python
+def get_ot_matrix(x0: Tensor,
+                  x1: Tensor,
+                  mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]
+```
+
+Compute the OT matrix between a source and a target minibatch.
+
+**Arguments**:
+
+- `x0` _Tensor_ - shape (bs, *dim), noise from source minibatch.
+- `x1` _Tensor_ - shape (bs, *dim), data from source minibatch.
+- `mask` _Optional[Tensor], optional_ - mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
+
+
+**Returns**:
+
+- `p` _Tensor_ - shape (bs, bs), the OT matrix between noise and data in minibatch.
+- `Rs` _Tensor_ - shape (bs, bs, *dim, *dim), the rotation matrix between noise and data in minibatch.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportequivariant_ot_samplerEquivariantOTSamplerapply_ot"></a>
+
+#### apply\_ot
+
+```python
+def apply_ot(
+    x0: Tensor,
+    x1: Tensor,
+    mask: Optional[Tensor] = None,
+    replace: Bool = False,
+    sort: Optional[Literal["noise", "x0", "data", "x1"]] = "x0"
+) -> Tuple[Tensor, Tensor, Optional[Tensor]]
+```
+
+Sample indices for noise and data in minibatch according to OT plan.
+
+Compute the OT plan $\pi$ (wrt squared Euclidean cost after Kabsch alignment) between a source and a target
+minibatch and draw source and target samples from pi $(x,z) \sim \pi$.
+
+**Arguments**:
+
+- `x0` _Tensor_ - shape (bs, *dim), noise from source minibatch.
+- `x1` _Tensor_ - shape (bs, *dim), data from source minibatch.
+- `mask` _Optional[Tensor], optional_ - mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
+- `replace` _bool_ - sampling w/ or w/o replacement from the OT plan, default to False.
+- `sort` _str_ - Optional Literal string to sort either x1 or x0 based on the input.
+
+
+**Returns**:
+
+- `Tuple` - tuple of 2 tensors, represents the noise and data samples following OT plan pi.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportkabsch_augmentation"></a>
+
+# bionemo.moco.interpolants.continuous\_time.continuous.optimal\_transport.kabsch\_augmentation
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportkabsch_augmentationKabschAugmentation"></a>
+
+## KabschAugmentation Objects
+
+```python
+class KabschAugmentation()
+```
+
+Point-wise Kabsch alignment.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportkabsch_augmentationKabschAugmentation__init__"></a>
+
+#### \_\_init\_\_
+
+```python
+def __init__()
+```
+
+Initialize the KabschAugmentation instance.
+
+**Notes**:
+
+  - This implementation assumes no required initialization arguments.
+  - You can add instance variables (e.g., `self.variable_name`) as needed.
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportkabsch_augmentationKabschAugmentationkabsch_align"></a>
+
+#### kabsch\_align
+
+```python
+def kabsch_align(target: Tensor, noise: Tensor)
+```
+
+Find the Rotation matrix (R) such that RMSD is minimized between target @ R.T and noise.
+
+**Arguments**:
+
+- `target` _Tensor_ - shape (N, *dim), data from source minibatch.
+- `noise` _Tensor_ - shape (N, *dim), noise from source minibatch.
+
+
+**Returns**:
+
+- `R` _Tensor_ - shape (*dim, *dim), the rotation matrix.
+  Aliged Target (Tensor): target tensor rotated and shifted to reduced RMSD with noise
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportkabsch_augmentationKabschAugmentationbatch_kabsch_align"></a>
+
+#### batch\_kabsch\_align
+
+```python
+def batch_kabsch_align(target: Tensor, noise: Tensor)
+```
+
+Find the Rotation matrix (R) such that RMSD is minimized between target @ R.T and noise.
+
+**Arguments**:
+
+- `target` _Tensor_ - shape (N, *dim), data from source minibatch.
+- `noise` _Tensor_ - shape (N, *dim), noise from source minibatch.
+
+
+**Returns**:
+
+- `R` _Tensor_ - shape (*dim, *dim), the rotation matrix.
+  Aliged Target (Tensor): target tensor rotated and shifted to reduced RMSD with noise
+
+<a id="mocointerpolantscontinuous_timecontinuousoptimal_transportkabsch_augmentationKabschAugmentationapply_ot"></a>
+
+#### apply\_ot
+
+```python
+def apply_ot(x0: Tensor,
+             x1: Tensor,
+             mask: Optional[Tensor] = None,
+             align_noise_to_data=True) -> Tuple[Tensor, Tensor]
+```
+
+Sample indices for noise and data in minibatch according to OT plan.
+
+Compute the OT plan $\pi$ (wrt squared Euclidean cost after Kabsch alignment) between a source and a target
+minibatch and draw source and target samples from pi $(x,z) \sim \pi$.
+
+**Arguments**:
+
+- `x0` _Tensor_ - shape (bs, *dim), noise from source minibatch.
+- `x1` _Tensor_ - shape (bs, *dim), data from source minibatch.
+- `mask` _Optional[Tensor], optional_ - mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
+- `replace` _bool_ - sampling w/ or w/o replacement from the OT plan, default to False.
+- `align_noise_to_data` _bool_ - Direction of alignment default is True meaning it augments Noise to reduce error to Data.
+
+
+**Returns**:
+
+- `Tuple` - tuple of 2 tensors, represents the noise and data samples following OT plan pi.
+
 <a id="mocointerpolantscontinuous_timecontinuous"></a>
 
 # bionemo.moco.interpolants.continuous\_time.continuous
@@ -3005,22 +3289,6 @@ and https://github.com/generatebio/chroma/blob/929407c605013613941803c6113adefdc
 
 # bionemo.moco.interpolants.continuous\_time.continuous.continuous\_flow\_matching
 
-<a id="mocointerpolantscontinuous_timecontinuouscontinuous_flow_matchingOptimalTransportType"></a>
-
-## OptimalTransportType Objects
-
-```python
-class OptimalTransportType(Enum)
-```
-
-An enumeration representing the type ofOptimal Transport that can be used in Continuous Flow Matching.
-
-- **EXACT**: Standard mini batch optimal transport defined in  https://arxiv.org/pdf/2302.00482.
-- **EQUIVARIANT**: Adding roto/translation optimization to mini batch OT see https://arxiv.org/pdf/2306.15030  https://arxiv.org/pdf/2312.07168 4.2.
-- **KABSCH**: Simple Kabsch alignment between each data and noise point, No permuation # https://arxiv.org/pdf/2410.22388 Sec 3.2
-
-These prediction types can be used to train neural networks for specific tasks, such as denoising, image synthesis, or time-series forecasting.
-
 <a id="mocointerpolantscontinuous_timecontinuouscontinuous_flow_matchingContinuousFlowMatcher"></a>
 
 ## ContinuousFlowMatcher Objects
@@ -3082,6 +3350,7 @@ def __init__(time_distribution: TimeDistribution,
              prediction_type: Union[PredictionType, str] = PredictionType.DATA,
              sigma: Float = 0,
              ot_type: Optional[Union[OptimalTransportType, str]] = None,
+             ot_num_threads: int = 1,
              data_scale: Float = 1.0,
              device: Union[str, torch.device] = "cpu",
              rng_generator: Optional[torch.Generator] = None,
@@ -3097,6 +3366,7 @@ Initializes the Continuous Flow Matching interpolant.
 - `prediction_type` _PredictionType, optional_ - The type of prediction, either "flow" or another type. Defaults to PredictionType.DATA.
 - `sigma` _Float, optional_ - The standard deviation of the Gaussian noise added to the interpolated data. Defaults to 0.
 - `ot_type` _Optional[Union[OptimalTransportType, str]], optional_ - The type of optimal transport, if applicable. Defaults to None.
+- `ot_num_threads` - Number of threads to use for OT solver. If "max", uses the maximum number of threads. Default is 1.
 - `data_scale` _Float, optional_ - The scale factor for the data. Defaults to 1.0.
 - `device` _Union[str, torch.device], optional_ - The device on which to run the interpolant, either "cpu" or a CUDA device (e.g. "cuda:0"). Defaults to "cpu".
 - `rng_generator` - An optional :class:`torch.Generator` for reproducible sampling. Defaults to None.
@@ -3110,7 +3380,7 @@ Initializes the Continuous Flow Matching interpolant.
 def apply_ot(x0: Tensor,
              x1: Tensor,
              mask: Optional[Tensor] = None,
-             replace: Bool = False) -> tuple
+             **kwargs) -> tuple
 ```
 
 Sample and apply the optimal transport plan between batched (and masked) x0 and x1.
@@ -3120,7 +3390,8 @@ Sample and apply the optimal transport plan between batched (and masked) x0 and
 - `x0` _Tensor_ - shape (bs, *dim), noise from source minibatch.
 - `x1` _Tensor_ - shape (bs, *dim), data from source minibatch.
 - `mask` _Optional[Tensor], optional_ - mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
-- `replace` _bool_ - sampling w/ or w/o replacement from the OT plan, default to False.
+- `**kwargs` - Additional keyword arguments to be passed to self.ot_sampler.apply_ot or handled within this method.
+
 
 
 **Returns**:
@@ -3448,6 +3719,59 @@ From Geffner et al. Computes gt for different modes.
 
 # bionemo.moco.interpolants
 
+<a id="mocointerpolantsbatch_augmentation"></a>
+
+# bionemo.moco.interpolants.batch\_augmentation
+
+<a id="mocointerpolantsbatch_augmentationBatchAugmentation"></a>
+
+## BatchAugmentation Objects
+
+```python
+class BatchAugmentation()
+```
+
+Facilitates the creation of batch augmentation objects based on specified optimal transport types.
+
+**Arguments**:
+
+- `device` _str_ - The device to use for computations (e.g., 'cpu', 'cuda').
+- `num_threads` _int_ - The number of threads to utilize.
+
+<a id="mocointerpolantsbatch_augmentationBatchAugmentation__init__"></a>
+
+#### \_\_init\_\_
+
+```python
+def __init__(device, num_threads)
+```
+
+Initializes a BatchAugmentation instance.
+
+**Arguments**:
+
+- `device` _str_ - Device for computation.
+- `num_threads` _int_ - Number of threads to use.
+
+<a id="mocointerpolantsbatch_augmentationBatchAugmentationcreate"></a>
+
+#### create
+
+```python
+def create(method_type: OptimalTransportType)
+```
+
+Creates a batch augmentation object of the specified type.
+
+**Arguments**:
+
+- `method_type` _OptimalTransportType_ - The type of optimal transport method.
+
+
+**Returns**:
+
+  The augmentation object if the type is supported, otherwise **None**.
+
 <a id="mocointerpolantsdiscrete_timediscreted3pm"></a>
 
 # bionemo.moco.interpolants.discrete\_time.discrete.d3pm
diff --git a/sub-packages/bionemo-moco/examples/ot_sampler_tutorial.ipynb b/sub-packages/bionemo-moco/examples/ot_sampler_tutorial.ipynb
new file mode 100644
index 0000000000..d1869e45f9
--- /dev/null
+++ b/sub-packages/bionemo-moco/examples/ot_sampler_tutorial.ipynb
@@ -0,0 +1,644 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optimal Transport Samplers Tutorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import os\n",
+    "import time\n",
+    "import copy\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from bionemo.moco.interpolants import EquivariantOTSampler, OTSampler\n",
+    "\n",
+    "from sklearn.datasets import make_moons"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Task Setup\n",
+    "### Demonstrating the effectiveness of OT sampler and Kabsch-based Equivariant OT sampler\n",
+    "\n",
+    "#### 1. We will start with the OT sampler. The OT sampler is an implementation of the \"OT-CFM\" algorithm proposed by [Tong et. al](https://arxiv.org/pdf/2307.03672). For a batch of randomly sampled noise ($\\mathrm{x}_0$) and data ($\\mathrm{x}_1$), the OT sampler will sample $(x_0, x_1)$ pairs based on their Euclidean distances. We will demonstrate how to use the OT sampler with a simple 2D example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 1.1 Sample 100 points from a standard Gaussian distribution ($\\mathrm{x}_0 \\sim \\pi_0$, orange colored), and another 100 points from a double moon-shape distribution ($\\mathrm{x}_1 \\sim \\pi_1$, blue colored). The linear interpolation between pairs ($x_0^i, x_1^i$) are plotted using grey lines. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sample_moons(n, normalize = False):\n",
+    "    x1, _ = make_moons(n_samples=n, noise=0.08)\n",
+    "    x1 = torch.Tensor(x1)\n",
+    "    x1 =  x1 * 3 - 1\n",
+    "    if normalize:\n",
+    "        x1 = (x1 - x1.mean(0))/x1.std(0) * 2\n",
+    "    return x1\n",
+    "\n",
+    "def sample_gaussian(n, dim = 2):\n",
+    "    return torch.randn(n, dim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x78315adebca0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Sample x0 and x1\n",
+    "x1 = sample_moons(100, normalize=True).numpy()\n",
+    "x0 = sample_gaussian(100).numpy()\n",
+    "# Plot data points and linear interpolation\n",
+    "plt.scatter(x1[:, 0], x1[:, 1], label='$x_0$')\n",
+    "plt.scatter(x0[:, 0], x0[:, 1], label='$x_1$')\n",
+    "x0 = np.asarray(x0)\n",
+    "x1 = np.asarray(x1)\n",
+    "for i in range(len(x1)):\n",
+    "    plt.plot([x0[i, 0], x1[i, 0]], [x0[i, 1], x1[i, 1]], color='k', alpha=0.2)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 1.2 Initialize the OT sampler and sample new $(x_0, x_1)$ pairs to minimize the transport cost of the entire batch. The linear interpolation between new pairs ($x_0^i, x_1^i$) are plotted using grey lines. We can see that there are less crossover of interpolation trajectories and the transport cost has been reduced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the OTSampler\n",
+    "ot_sampler = OTSampler(method=\"exact\", num_threads=1)\n",
+    "# Sample new pairs from the OTSampler, mask is not used in this example\n",
+    "# Replace is set to False, so no duplicates are allowed\n",
+    "# Sort is set to \"x0\", so the order of output x0 is the same as input x0\n",
+    "ot_sampled_x0, ot_sampled_x1, mask = ot_sampler.apply_ot(\n",
+    "    torch.Tensor(x0), \n",
+    "    torch.Tensor(x1), \n",
+    "    mask=None, replace=False, sort=\"x0\")\n",
+    "# Convert the sampled tensors to numpy arrays\n",
+    "ot_sampled_x0 = ot_sampled_x0.numpy()\n",
+    "ot_sampled_x1 = ot_sampled_x1.numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x783152f9f4f0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot data points and linear interpolation\n",
+    "plt.scatter(ot_sampled_x1[:, 0], ot_sampled_x1[:, 1], label='$x_0$')\n",
+    "plt.scatter(ot_sampled_x0[:, 0], ot_sampled_x0[:, 1], label='$x_1$')\n",
+    "for i in range(len(x1)):\n",
+    "    plt.plot(\n",
+    "        [ot_sampled_x0[i, 0], ot_sampled_x1[i, 0]], \n",
+    "        [ot_sampled_x0[i, 1], ot_sampled_x1[i, 1]], \n",
+    "        color='k', alpha=0.2\n",
+    "    )\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 1.3 Let's see how the OT can help in conditional flow matching training. We will train two models, one with OT and the other one without, and compare the flow trajectory during sampling.\n",
+    "\n",
+    "Note the ContinuousFlowMatcher object can be initialized with any batch augmentation using the 'ot_type' parameter. For clarity we pull in our previosuly initialized OT Sampler."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bionemo.moco.interpolants import ContinuousFlowMatcher\n",
+    "from bionemo.moco.distributions.time import UniformTimeDistribution\n",
+    "from bionemo.moco.distributions.prior import GaussianPrior\n",
+    "\n",
+    "def trainCFM(use_ot=False):\n",
+    "    # Initialize model, optimizer, and flow matcher\n",
+    "    dim = 2\n",
+    "    hidden_size = 64\n",
+    "    batch_size = 256\n",
+    "    model = torch.nn.Sequential(\n",
+    "                torch.nn.Linear(dim + 1, hidden_size),\n",
+    "                torch.nn.SELU(),\n",
+    "                torch.nn.Linear(hidden_size, hidden_size),\n",
+    "                torch.nn.SELU(),\n",
+    "                torch.nn.Linear(hidden_size, hidden_size),\n",
+    "                torch.nn.SELU(),\n",
+    "                torch.nn.Linear(hidden_size, dim),\n",
+    "            )\n",
+    "    optimizer = torch.optim.Adam(model.parameters())\n",
+    "\n",
+    "    uniform_time = UniformTimeDistribution()\n",
+    "    moon_prior = GaussianPrior()\n",
+    "    sigma = 0.1\n",
+    "    cfm = ContinuousFlowMatcher(time_distribution=uniform_time, \n",
+    "                                prior_distribution=moon_prior, \n",
+    "                                sigma=sigma, \n",
+    "                                prediction_type=\"velocity\")\n",
+    "\n",
+    "    # Place both the model and the interpolant on the same device\n",
+    "    DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "    model = model.to(DEVICE)\n",
+    "    cfm = cfm.to_device(DEVICE)\n",
+    "\n",
+    "    for k in range(10000):\n",
+    "        optimizer.zero_grad()\n",
+    "        shape = (batch_size, dim)\n",
+    "        x0 = cfm.sample_prior(shape).to(DEVICE)\n",
+    "        x1 = sample_moons(batch_size, normalize=False).to(DEVICE)\n",
+    "        if use_ot:\n",
+    "            x0, x1, mask = ot_sampler.apply_ot(\n",
+    "                x0, x1, \n",
+    "                mask=None, replace=False, sort=\"x0\"\n",
+    "            )\n",
+    "        t = cfm.sample_time(batch_size)\n",
+    "        xt = cfm.interpolate(x1, t, x0)\n",
+    "        ut = cfm.calculate_target(x1, x0)\n",
+    "\n",
+    "        vt = model(torch.cat([xt, t[:, None]], dim=-1))\n",
+    "        loss = cfm.loss(vt, ut, target_type=\"velocity\").mean()\n",
+    "\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        if (k + 1) % 5000 == 0:\n",
+    "            print(f\"{k+1}: loss {loss.item():0.3f}\") \n",
+    "    return model, cfm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5000: loss 0.053\n",
+      "10000: loss 0.058\n",
+      "5000: loss 2.955\n",
+      "10000: loss 3.211\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train a model with OT\n",
+    "ot_model, ot_cfm = trainCFM(use_ot=True)\n",
+    "# Train a model without OT\n",
+    "no_ot_model, no_ot_cfm = trainCFM(use_ot=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up the sampling time schedule\n",
+    "from bionemo.moco.schedules.inference_time_schedules import LinearInferenceSchedule\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "inference_sched = LinearInferenceSchedule(nsteps = 100)\n",
+    "schedule = inference_sched.generate_schedule().to(DEVICE)\n",
+    "dts = inference_sched.discretize().to(DEVICE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sampling with the two trained models\n",
+    "inf_size = 1024\n",
+    "ot_sample = ot_cfm.sample_prior((inf_size, 2)) # Start with noise\n",
+    "no_ot_sample = copy.deepcopy(ot_sample) # Ensure the same starting point for both models\n",
+    "ot_sample, no_ot_sample = ot_sample.to(DEVICE), no_ot_sample.to(DEVICE)\n",
+    "ot_trajectory, no_ot_trajectory = [ot_sample], [no_ot_sample]\n",
+    "for dt, t in zip(dts, schedule):\n",
+    "    full_t  = torch.full((inf_size,), t).to(DEVICE)\n",
+    "    ot_vt = ot_model(torch.cat([ot_sample, full_t[:, None]], dim=-1)) # calculate the vector field based on the definition of the model\n",
+    "    ot_sample = ot_cfm.step(ot_vt, ot_sample, dt, full_t)\n",
+    "    no_ot_vt = no_ot_model(torch.cat([no_ot_sample, full_t[:, None]], dim=-1)) # calculate the vector field based on the definition of the model\n",
+    "    no_ot_sample = no_ot_cfm.step(no_ot_vt, no_ot_sample, dt, full_t)\n",
+    "    ot_trajectory.append(ot_sample) # save the trajectory for plotting purposes\n",
+    "    no_ot_trajectory.append(no_ot_sample) # save the trajectory for plotting purposes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 1.4 Visualization of flow trajectories predicted by the two models. With OT (left), the flow trajectory is straighter, thus less transport cost comapred to without OT (right)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ot_traj = torch.stack(ot_trajectory).cpu().detach().numpy()\n",
+    "no_ot_traj = torch.stack(no_ot_trajectory).cpu().detach().numpy()\n",
+    "n = 2000\n",
+    "\n",
+    "# Assuming traj is your tensor and traj.shape = (N, 2000, 2)\n",
+    "# where N is the number of time points, 2000 is the number of samples at each time point, and 2 is for the x and y coordinates.\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "\n",
+    "# Plot the first time point in black\n",
+    "ax[0].scatter(ot_traj[0, :n, 0], ot_traj[0, :n, 1], s=10, alpha=0.8, c=\"black\", label='Prior z(S)')\n",
+    "ax[1].scatter(no_ot_traj[0, :n, 0], no_ot_traj[0, :n, 1], s=10, alpha=0.8, c=\"black\", label='Prior z(S)')\n",
+    "\n",
+    "# Plot all the rest of the time points except the first and last in olive\n",
+    "for i in range(1, ot_traj.shape[0]-1):\n",
+    "    ax[0].scatter(ot_traj[i, :n, 0], ot_traj[i, :n, 1], s=0.2, alpha=0.2, c=\"olive\", zorder=1)\n",
+    "    ax[1].scatter(no_ot_traj[i, :n, 0], no_ot_traj[i, :n, 1], s=0.2, alpha=0.2, c=\"olive\", zorder=1)\n",
+    "\n",
+    "# Plot the last time point in blue\n",
+    "ax[0].scatter(ot_traj[-1, :n, 0], ot_traj[-1, :n, 1], s=4, alpha=1, c=\"blue\", label='z(0)')\n",
+    "ax[1].scatter(no_ot_traj[-1, :n, 0], no_ot_traj[-1, :n, 1], s=4, alpha=1, c=\"blue\", label='z(0)')\n",
+    "\n",
+    "# Add a second legend for \"Flow\" since we can't label in the loop directly\n",
+    "for i in range(2):\n",
+    "    ax[i].scatter([], [], s=2, alpha=1, c=\"olive\", label='Flow')\n",
+    "    ax[i].legend()\n",
+    "    # ax[i].set_aspect('equal')\n",
+    "    ax[i].set_xticks([])\n",
+    "    ax[i].set_yticks([])\n",
+    "    ax[i].set_xlim(-5, 6)\n",
+    "    ax[i].set_ylim(-4, 5)\n",
+    "    if i == 0:\n",
+    "        ax[i].set_title(\"With OT\")\n",
+    "    else:\n",
+    "        ax[i].set_title(\"Without OT\")\n",
+    "plt.subplots_adjust(wspace=0.05)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Distance between First and Last Points without OT: 4.119970321655273\n",
+      "Average Distance between First and Last Points with OT: 3.9200291633605957\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "first_points = no_ot_traj[0]\n",
+    "last_points = no_ot_traj[-1]\n",
+    "distances = ((last_points - first_points)**2).sum(-1)\n",
+    "average_distance = np.mean(distances)\n",
+    "\n",
+    "print(f\"Average Distance between First and Last Points without OT: {average_distance.item()}\")\n",
+    "\n",
+    "first_points = ot_traj[0]\n",
+    "last_points = ot_traj[-1]\n",
+    "distances = ((last_points - first_points)**2).sum(-1)\n",
+    "average_distance = np.mean(distances)\n",
+    "\n",
+    "print(f\"Average Distance between First and Last Points with OT: {average_distance.item()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sum of Squared Distances (start to mid + mid to end):\n",
+      "Without OT: 2667.9356\n",
+      "With OT: 2009.3874\n"
+     ]
+    }
+   ],
+   "source": [
+    "def sum_of_squared_distances(trajectory):\n",
+    "    \"\"\"\n",
+    "    Calculate the sum of squared distances from start to mid and mid to end of a trajectory.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    - trajectory: A numpy array of shape (N, D) where N is the number of points \n",
+    "                  in the trajectory and D is the dimensionality of the space.\n",
+    "                  \n",
+    "    Returns:\n",
+    "    - Sum of squared distances (start to mid + mid to end).\n",
+    "    \"\"\"\n",
+    "    mid_idx = len(trajectory) // 2\n",
+    "    start_point = trajectory[0]\n",
+    "    mid_point = trajectory[mid_idx]\n",
+    "    end_point = trajectory[-1]\n",
+    "    \n",
+    "    start_to_mid_distance = np.linalg.norm(start_point - mid_point)\n",
+    "    mid_to_end_distance = np.linalg.norm(mid_point - end_point)\n",
+    "    \n",
+    "    return start_to_mid_distance**2 + mid_to_end_distance**2\n",
+    "\n",
+    "# Calculate and print sum of squared distances for both trajectories\n",
+    "no_ot_sum_squared_distance = sum_of_squared_distances(no_ot_traj)\n",
+    "ot_sum_squared_distance = sum_of_squared_distances(ot_traj)\n",
+    "\n",
+    "print(\"Sum of Squared Distances (start to mid + mid to end):\")\n",
+    "print(f\"Without OT: {no_ot_sum_squared_distance:.4f}\")\n",
+    "print(f\"With OT: {ot_sum_squared_distance:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. We will then introduce the Kabsch OT sampler. The Kabsch OT sampler is an implementation of the \"Equivariant OT\" algorithm ([Klein et al.](https://arxiv.org/abs/2306.15030)). For a batch of randomly sampled noise ($\\mathrm{x}_0$) and data ($\\mathrm{x}_1$), the Kabsch OT sampler will sample $(x_0, x_1)$ pairs based on the RMSD after aligning *zero-centered* $(x_0, x_1)$ using *Kabsch algorithm*. We will demonstrate how to use the Kabsch OT sampler with a simple 2D example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define helper functions\n",
+    "def rotation_matrix(angle):\n",
+    "    theta = (angle/180.) * np.pi\n",
+    "    c, s = np.cos(theta), np.sin(theta)\n",
+    "    return np.array([[c, -s], [s, c]])\n",
+    "\n",
+    "def rotate(x, angle):\n",
+    "    R = rotation_matrix(angle)\n",
+    "    return x @ R.T\n",
+    "\n",
+    "def plot_quadrilateral(x, axis, color='C0', marker='o', label=None):\n",
+    "    assert x.shape == (4, 2)\n",
+    "    axis.scatter(\n",
+    "        x[:, 0], x[:, 1], \n",
+    "        c=color, marker=marker, linewidths=1, \n",
+    "        edgecolors='k', zorder=2, label=label\n",
+    "    )\n",
+    "    for i in range(len(x)):\n",
+    "        if i < 3:\n",
+    "            axis.plot([x[i, 0], x[i+1, 0]], [x[i, 1], x[i+1, 1]], c=color, zorder=1)\n",
+    "        else:\n",
+    "            axis.plot([x[i, 0], x[0, 0]], [x[i, 1], x[0, 1]], c=color, zorder=1)\n",
+    "    return axis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.1 Initialize $\\mathrm{k}_0$ which contains two samples. $k_0^0$ is a rhombus and $k_0^1$ is a square. Then initialize $\\mathrm{k}_1$ which is rotated $\\mathrm{k}_0$. Shuffle the order of $\\mathrm{k}_1$ so $k_1^0$ is rotated square and $k_1^1$ is rotated rhombus. When plotting, the $k_0^0$ and $k_1^0$ are shown with circle-shaped dots while $k_0^1$ and $k_1^1$ are shown with square-shaped dots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize \n",
+    "k0 = np.array([\n",
+    "    [[-2, 0], [0, 1], [2, 0], [0, -1]], # Rhombus\n",
+    "    [[-1, 2], [-1, 4], [1, 4], [1, 2]], # Square\n",
+    "])\n",
+    "angles = [60, 25]\n",
+    "\n",
+    "# Rotate and shuffle samples in k0 to create k1\n",
+    "k1 = np.array([rotate(k0[i], angles[i]) for i in [1, 0]])\n",
+    "markers = ['o', 's']\n",
+    "\n",
+    "# Translate k0 and k1\n",
+    "k0 = np.array(k0)-2\n",
+    "k1 = np.array(k1)+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot k0 and k1\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
+    "for i in range(len(k0)):\n",
+    "    plot_quadrilateral(k0[i], ax, color='C1', marker=markers[i], label='$k_0^%d$'%i)\n",
+    "    plot_quadrilateral(k1[i], ax, color='C0', marker=markers[i], label='$k_1^%d$'%i)\n",
+    "    # Calculate centroids of k0 and k1\n",
+    "    centroid_k0 = np.mean(k0[i], axis=0)\n",
+    "    centroid_k1 = np.mean(k1[i], axis=0)\n",
+    "\n",
+    "    # Plot a red line connecting the centroids\n",
+    "    ax.plot(*zip(centroid_k0, centroid_k1), color='red', linewidth=1, linestyle='--')\n",
+    "ax.legend()\n",
+    "ax.set_aspect('equal', adjustable='box')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### We see that we have arbitraility set up a mismatch. The orange rhombus with circle dots is tied to the blue rotated square with circle dots. We can use EquivariantOT to fix this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.2 Initialize the Kabsch-based  Equivariant OT sampler and sample new $(k_0, k_1)$ pairs to minimize the transport cost of the entire batch after rotational alignment. We can see that the order of newly sampled $\\mathrm{k}_1$ has changed to match $\\mathrm{k}_0$. Note that the sampled $\\mathrm{k}_1$ will be rotated but not translated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the Kabsch OT Sampler\n",
+    "kabsch_ot_sampler = EquivariantOTSampler(method=\"exact\", num_threads=1)\n",
+    "# Sample new pairs from the EquivariantOTSampler, mask is not used in this example\n",
+    "# Replace is set to False, so no duplicates are allowed\n",
+    "# Sort is set to \"x0\", so the order of output x0 is the same as input x0\n",
+    "kabsch_k0, kabsch_k1, mask = kabsch_ot_sampler.apply_ot(\n",
+    "    torch.Tensor(k0), \n",
+    "    torch.Tensor(k1), \n",
+    "    mask=None, replace=False, sort=\"x0\")\n",
+    "# Convert the sampled tensors to numpy arrays\n",
+    "kabsch_k0 = kabsch_k0.numpy()\n",
+    "kabsch_k1 = kabsch_k1.numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot newly sampled k0 and k1, note that k1 is rotated to match k0\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(5, 5))\n",
+    "for i in range(len(kabsch_k0)):\n",
+    "    plot_quadrilateral(kabsch_k0[i], ax, color='C1', marker=markers[i], label='$k_0^%d$'%i)\n",
+    "    plot_quadrilateral(kabsch_k1[i], ax, color='C0', marker=markers[i], label='$k_1^%d$'%i)\n",
+    "    # Calculate centroids of k0 and k1\n",
+    "    # Calculate centroids of k0 and k1\n",
+    "    centroid_k0 = np.mean(kabsch_k0[i], axis=0)\n",
+    "    centroid_k1 = np.mean(kabsch_k1[i], axis=0)\n",
+    "\n",
+    "    # Plot a red line connecting the centroids\n",
+    "    ax.plot(*zip(centroid_k0, centroid_k1), color='red', linewidth=1, linestyle='--')\n",
+    "ax.legend()\n",
+    "ax.set_aspect('equal', adjustable='box')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### If you wanted to align with respect to rotations and translations you could center your data or augment the EquivariantOT object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "moco_bionemo",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/__init__.py b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/__init__.py
index 0a1606c098..743489e4f4 100644
--- a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/__init__.py
+++ b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/__init__.py
@@ -15,6 +15,9 @@
 
 
 from .continuous_time.continuous.continuous_flow_matching import ContinuousFlowMatcher
+from .continuous_time.continuous.optimal_transport.equivariant_ot_sampler import EquivariantOTSampler
+from .continuous_time.continuous.optimal_transport.kabsch_augmentation import KabschAugmentation
+from .continuous_time.continuous.optimal_transport.ot_sampler import OTSampler
 from .continuous_time.continuous.vdm import VDM
 from .continuous_time.discrete.discrete_flow_matching import DiscreteFlowMatcher
 from .continuous_time.discrete.mdlm import MDLM
@@ -22,4 +25,14 @@
 from .discrete_time.discrete.d3pm import D3PM
 
 
-__all__ = ["DDPM", "D3PM", "VDM", "MDLM", "ContinuousFlowMatcher", "DiscreteFlowMatcher"]
+__all__ = [
+    "DDPM",
+    "D3PM",
+    "VDM",
+    "MDLM",
+    "ContinuousFlowMatcher",
+    "DiscreteFlowMatcher",
+    "EquivariantOTSampler",
+    "OTSampler",
+    "KabschAugmentation",
+]
diff --git a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/batch_augmentation.py b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/batch_augmentation.py
new file mode 100644
index 0000000000..dd718744e6
--- /dev/null
+++ b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/batch_augmentation.py
@@ -0,0 +1,62 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+#
+# 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.
+
+
+from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport.equivariant_ot_sampler import (
+    EquivariantOTSampler,
+)
+from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport.kabsch_augmentation import (
+    KabschAugmentation,
+)
+from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport.ot_sampler import OTSampler
+from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport.ot_types import OptimalTransportType
+
+
+class BatchAugmentation:
+    """Facilitates the creation of batch augmentation objects based on specified optimal transport types.
+
+    Args:
+        device (str): The device to use for computations (e.g., 'cpu', 'cuda').
+        num_threads (int): The number of threads to utilize.
+    """
+
+    def __init__(self, device, num_threads):
+        """Initializes a BatchAugmentation instance.
+
+        Args:
+            device (str): Device for computation.
+            num_threads (int): Number of threads to use.
+        """
+        self.device = device
+        self.num_threads = num_threads
+
+    def create(self, method_type: OptimalTransportType):
+        """Creates a batch augmentation object of the specified type.
+
+        Args:
+            method_type (OptimalTransportType): The type of optimal transport method.
+
+        Returns:
+            The augmentation object if the type is supported, otherwise **None**.
+        """
+        if method_type == OptimalTransportType.EXACT:
+            augmentation = OTSampler(method="exact", device=self.device, num_threads=self.num_threads)
+        elif method_type == OptimalTransportType.KABSCH:
+            augmentation = KabschAugmentation()
+        elif method_type == OptimalTransportType.EQUIVARIANT:
+            augmentation = EquivariantOTSampler(method="exact", device=self.device, num_threads=self.num_threads)
+        else:
+            return None
+        return augmentation
diff --git a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/continuous_flow_matching.py b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/continuous_flow_matching.py
index 2ace71ab9c..20b2ab26c6 100644
--- a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/continuous_flow_matching.py
+++ b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/continuous_flow_matching.py
@@ -14,7 +14,6 @@
 # limitations under the License.
 
 
-from enum import Enum
 from typing import Optional, Union
 
 import torch
@@ -26,22 +25,8 @@
 from bionemo.moco.distributions.prior.distribution import PriorDistribution
 from bionemo.moco.distributions.time.distribution import TimeDistribution
 from bionemo.moco.interpolants.base_interpolant import Interpolant, PredictionType, pad_like, string_to_enum
-from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport import OTSampler
-
-
-class OptimalTransportType(Enum):
-    """An enumeration representing the type ofOptimal Transport that can be used in Continuous Flow Matching.
-
-    - **EXACT**: Standard mini batch optimal transport defined in  https://arxiv.org/pdf/2302.00482.
-    - **EQUIVARIANT**: Adding roto/translation optimization to mini batch OT see https://arxiv.org/pdf/2306.15030  https://arxiv.org/pdf/2312.07168 4.2.
-    - **KABSCH**: Simple Kabsch alignment between each data and noise point, No permuation # https://arxiv.org/pdf/2410.22388 Sec 3.2
-
-    These prediction types can be used to train neural networks for specific tasks, such as denoising, image synthesis, or time-series forecasting.
-    """
-
-    EXACT = "exact"
-    EQUIVARIANT = "equivariant"
-    KABSCH = "kabsch"
+from bionemo.moco.interpolants.batch_augmentation import BatchAugmentation
+from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport.ot_types import OptimalTransportType
 
 
 class ContinuousFlowMatcher(Interpolant):
@@ -95,6 +80,7 @@ def __init__(
         prediction_type: Union[PredictionType, str] = PredictionType.DATA,
         sigma: Float = 0,
         ot_type: Optional[Union[OptimalTransportType, str]] = None,
+        ot_num_threads: int = 1,
         data_scale: Float = 1.0,
         device: Union[str, torch.device] = "cpu",
         rng_generator: Optional[torch.Generator] = None,
@@ -108,6 +94,7 @@ def __init__(
             prediction_type (PredictionType, optional): The type of prediction, either "flow" or another type. Defaults to PredictionType.DATA.
             sigma (Float, optional): The standard deviation of the Gaussian noise added to the interpolated data. Defaults to 0.
             ot_type (Optional[Union[OptimalTransportType, str]], optional): The type of optimal transport, if applicable. Defaults to None.
+            ot_num_threads:  Number of threads to use for OT solver. If "max", uses the maximum number of threads. Default is 1.
             data_scale (Float, optional): The scale factor for the data. Defaults to 1.0.
             device (Union[str, torch.device], optional): The device on which to run the interpolant, either "cpu" or a CUDA device (e.g. "cuda:0"). Defaults to "cpu".
             rng_generator: An optional :class:`torch.Generator` for reproducible sampling. Defaults to None.
@@ -123,44 +110,37 @@ def __init__(
             raise ValueError("Data Scale must be > 0")
         if ot_type is not None:
             self.ot_type = ot_type = string_to_enum(ot_type, OptimalTransportType)
-            self.ot_sampler = self._build_ot_sampler(sampler_type=ot_type)
+            self.ot_sampler = self._build_ot_sampler(method_type=ot_type, num_threads=ot_num_threads)
         self._loss_function = nn.MSELoss(reduction="none")
 
-    def _build_ot_sampler(self, sampler_type: Union[str, OptimalTransportType], num_threads: int = 1) -> OTSampler:
+    def _build_ot_sampler(self, method_type: OptimalTransportType, num_threads: int = 1):
         """Build the optimal transport sampler for the given optimal transport type.
 
         Args:
-            sampler_type (OptimalTransportType): The OT type to build the sampler for.
+            method_type (OptimalTransportType): The type of augmentation.
             num_threads (int): The number of threads to use for the OT sampler, default to 1.
 
         Returns:
-            The optimal transport sampler object or None if the optimal transport type is not specified.
+            The augmentation object.
         """
-        ot_sampler = None
-        sampler_type = string_to_enum(sampler_type, OptimalTransportType)
-        if sampler_type == OptimalTransportType.EXACT:
-            ot_sampler = OTSampler(method="exact", num_threads=num_threads)
-        elif sampler_type == OptimalTransportType.EQUIVARIANT:
-            raise NotImplementedError("Equivariant OT currently not implemented")
-        elif sampler_type == OptimalTransportType.KABSCH:
-            raise NotImplementedError("Kabsch OT currently not implemented")
-        return ot_sampler
-
-    def apply_ot(self, x0: Tensor, x1: Tensor, mask: Optional[Tensor] = None, replace: Bool = False) -> tuple:
+        return BatchAugmentation(self.device, num_threads).create(method_type)
+
+    def apply_ot(self, x0: Tensor, x1: Tensor, mask: Optional[Tensor] = None, **kwargs) -> tuple:
         """Sample and apply the optimal transport plan between batched (and masked) x0 and x1.
 
         Args:
             x0 (Tensor): shape (bs, *dim), noise from source minibatch.
             x1 (Tensor): shape (bs, *dim), data from source minibatch.
             mask (Optional[Tensor], optional): mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
-            replace (bool): sampling w/ or w/o replacement from the OT plan, default to False.
+            **kwargs: Additional keyword arguments to be passed to self.ot_sampler.apply_ot or handled within this method.
+
 
         Returns:
             Tuple: tuple of 2 tensors, represents the noise and data samples following OT plan pi.
         """
         if self.ot_sampler is None:
             raise ValueError("Optimal Transport Sampler is not defined")
-        return self.ot_sampler.apply_ot(x0, x1, mask=mask, replace=replace)
+        return self.ot_sampler.apply_ot(x0, x1, mask=mask, **kwargs)
 
     def undo_scale_data(self, data: Tensor) -> Tensor:
         """Downscale the input data by the data scale factor.
diff --git a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/equivariant_ot_sampler.py b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/equivariant_ot_sampler.py
new file mode 100644
index 0000000000..3a299c6e2b
--- /dev/null
+++ b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/equivariant_ot_sampler.py
@@ -0,0 +1,242 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+#
+# 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 warnings
+from functools import partial
+from typing import Callable, Literal, Optional, Tuple, Union
+
+import ot as pot
+import torch
+from jaxtyping import Bool
+from torch import Tensor
+
+
+class EquivariantOTSampler:
+    """Sampler for Mini-batch Optimal Transport Plan with cost calculated after Kabsch alignment.
+
+    EquivariantOTSampler implements sampling coordinates according to an OT plan
+    (wrt squared Euclidean cost after Kabsch alignment) with different implementations of the plan calculation.
+
+    """
+
+    def __init__(
+        self,
+        method: str = "exact",
+        device: Union[str, torch.device] = "cpu",
+        num_threads: int = 1,
+    ) -> None:
+        """Initialize the OTSampler class.
+
+        Args:
+            method (str): Choose which optimal transport solver you would like to use. Currently only support exact OT solvers (pot.emd).
+            device (Union[str, torch.device], optional): The device on which to run the interpolant, either "cpu" or a CUDA device (e.g. "cuda:0"). Defaults to "cpu".
+            num_threads (Union[int, str], optional): Number of threads to use for OT solver. If "max", uses the maximum number of threads. Default is 1.
+
+        Raises:
+            ValueError: If the OT solver is not documented.
+            NotImplementedError: If the OT solver is not implemented.
+        """
+        # ot_fn should take (a, b, M) as arguments where a, b are marginals and
+        # M is a cost matrix
+        if method == "exact":
+            self.ot_fn: Callable[..., torch.Tensor] = partial(pot.emd, numThreads=num_threads)  # type: ignore
+        elif method in {"sinkhorn", "unbalanced", "partial"}:
+            raise NotImplementedError("OT solver other than 'exact' is not implemented.")
+        else:
+            raise ValueError(f"Unknown method: {method}")
+        self.device = device
+
+    def to_device(self, device: str):
+        """Moves all internal tensors to the specified device and updates the `self.device` attribute.
+
+        Args:
+            device (str): The device to move the tensors to (e.g. "cpu", "cuda:0").
+
+        Note:
+            This method is used to transfer the internal state of the OTSampler to a different device.
+            It updates the `self.device` attribute to reflect the new device and moves all internal tensors to the specified device.
+        """
+        self.device = device
+        for attr_name in dir(self):
+            if attr_name.startswith("_") and isinstance(getattr(self, attr_name), torch.Tensor):
+                setattr(self, attr_name, getattr(self, attr_name).to(device))
+        return self
+
+    def sample_map(self, pi: Tensor, batch_size: int, replace: Bool = False) -> Tuple[Tensor, Tensor]:
+        r"""Draw source and target samples from pi $(x,z) \sim \pi$.
+
+        Args:
+            pi (Tensor): shape (bs, bs), the OT matrix between noise and data in minibatch.
+            batch_size (int): The batch size of the minibatch.
+            replace (bool): sampling w/ or w/o replacement from the OT plan, default to False.
+
+        Returns:
+            Tuple: tuple of 2 tensors, represents the indices of noise and data samples from pi.
+        """
+        if pi.shape[0] != batch_size or pi.shape[1] != batch_size:
+            raise ValueError("Shape mismatch: pi.shape = {}, batch_size = {}".format(pi.shape, batch_size))
+        p = pi.flatten()
+        p = p / p.sum()
+        choices = torch.multinomial(p, batch_size, replacement=replace)
+        return torch.div(choices, pi.shape[1], rounding_mode="floor"), choices % pi.shape[1]
+
+    def kabsch_align(self, target: Tensor, noise: Tensor) -> Tensor:
+        """Find the Rotation matrix (R) such that RMSD is minimized between target @ R.T and noise.
+
+        Args:
+            target (Tensor): shape (N, *dim), data from source minibatch.
+            noise (Tensor): shape (N, *dim), noise from source minibatch.
+
+        Returns:
+            R (Tensor): shape (*dim, *dim), the rotation matrix.
+        """
+        dimension = target.shape[-1]
+        noise_centered = noise - noise.mean(dim=0)
+        target_centered = target - target.mean(dim=0)
+
+        # Compute the covariance matrix
+        covariance_matix = target_centered.T @ noise_centered
+
+        # Compute the SVD of the covariance matrix
+        U, S, Vt = torch.linalg.svd(covariance_matix)
+        d = torch.sign(torch.linalg.det(Vt.T @ U.T)).item()
+        d_mat = torch.tensor([1] * (dimension - 1) + [d], device=Vt.device, dtype=Vt.dtype)
+        R = Vt.T @ torch.diag(d_mat) @ U.T
+        return R
+
+    def _calculate_cost_matrix(self, x0: Tensor, x1: Tensor, mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
+        """Compute the cost matrix between a source and a target minibatch.
+
+        The distance between noise and data is calculated after aligning them using Kabsch algorithm.
+
+        Args:
+            x0 (Tensor): shape (bs, *dim), noise from source minibatch.
+            x1 (Tensor): shape (bs, *dim), data from source minibatch.
+            mask (Optional[Tensor], optional): mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
+
+        Returns:
+            M: shape (bs, bs), the cost matrix between noise and data in minibatch.
+            Rs: shape (bs, bs, *dim, *dim), the rotation matrix between noise and data in minibatch.
+        """
+        if x0.shape[0] != x1.shape[0]:
+            raise ValueError("Shape mismatch: x0.shape = {}, x1.shape = {}".format(x0.shape, x1.shape))
+        batchsize, maxlen, dimension = x0.shape[0], x0.shape[1], x0.shape[-1]
+        M = torch.zeros(batchsize, batchsize, device=x0.device)
+        Rs = torch.zeros(batchsize, batchsize, dimension, dimension, device=x0.device)
+        for i in range(batchsize):
+            for j in range(batchsize):
+                if mask is not None:
+                    x0i_mask = mask[i].bool()
+                else:
+                    x0i_mask = torch.ones(maxlen, device=x0.device).bool()
+                x0_masked, x1_masked = x0[i][x0i_mask], x1[j][x0i_mask]
+                # Rotate the data to align with the noise
+                R = self.kabsch_align(x1_masked, x0_masked)
+                x1_aligned = x1_masked @ R.T
+                # Here the cost only considered the rotational RMSD, not the translational RMSD
+                cost = torch.dist(x0_masked - x0_masked.mean(dim=0), x1_aligned - x1_aligned.mean(dim=0), p=2)
+                M[i, j] = cost
+                Rs[i, j] = R.T
+
+        return M, Rs
+
+    def get_ot_matrix(self, x0: Tensor, x1: Tensor, mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
+        """Compute the OT matrix between a source and a target minibatch.
+
+        Args:
+            x0 (Tensor): shape (bs, *dim), noise from source minibatch.
+            x1 (Tensor): shape (bs, *dim), data from source minibatch.
+            mask (Optional[Tensor], optional): mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
+
+        Returns:
+            p (Tensor): shape (bs, bs), the OT matrix between noise and data in minibatch.
+            Rs (Tensor): shape (bs, bs, *dim, *dim), the rotation matrix between noise and data in minibatch.
+        """
+        # Compute the cost matrix
+        M, Rs = self._calculate_cost_matrix(x0, x1, mask)
+
+        # Set uniform weights for all samples in a minibatch
+        a, b = pot.unif(x0.shape[0], type_as=M), pot.unif(x1.shape[0], type_as=M)
+
+        # Compute the OT matrix using POT package
+        p = self.ot_fn(a, b, M)
+
+        # Handle Exceptions
+        if not torch.all(torch.isfinite(p)):
+            raise ValueError("OT plan map is not finite, cost mean, max: {}, {}".format(M.mean(), M.max()))
+        if torch.abs(p.sum()) < 1e-8:
+            warnings.warn("Numerical errors in OT matrix, reverting to uniform plan.")
+            p = torch.ones_like(p) / p.numel()
+
+        return p, Rs
+
+    def apply_ot(
+        self,
+        x0: Tensor,
+        x1: Tensor,
+        mask: Optional[Tensor] = None,
+        replace: Bool = False,
+        sort: Optional[Literal["noise", "x0", "data", "x1"]] = "x0",
+    ) -> Tuple[Tensor, Tensor, Optional[Tensor]]:
+        r"""Sample indices for noise and data in minibatch according to OT plan.
+
+        Compute the OT plan $\pi$ (wrt squared Euclidean cost after Kabsch alignment) between a source and a target
+        minibatch and draw source and target samples from pi $(x,z) \sim \pi$.
+
+        Args:
+            x0 (Tensor): shape (bs, *dim), noise from source minibatch.
+            x1 (Tensor): shape (bs, *dim), data from source minibatch.
+            mask (Optional[Tensor], optional): mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
+            replace (bool): sampling w/ or w/o replacement from the OT plan, default to False.
+            sort (str): Optional Literal string to sort either x1 or x0 based on the input.
+
+        Returns:
+            Tuple: tuple of 2 tensors, represents the noise and data samples following OT plan pi.
+        """
+        # Calculate the optimal transport
+        pi, Rs = self.get_ot_matrix(x0, x1, mask)
+
+        # Sample (x0, x1) mapping indices from the OT matrix
+        i, j = self.sample_map(pi, x0.shape[0], replace=replace)
+
+        if not replace and (sort == "noise" or sort == "x0"):
+            sort_idx = torch.argsort(i)
+            i = i[sort_idx]
+            j = j[sort_idx]
+
+            if not (i == torch.arange(x0.shape[0], device=i.device)).all():
+                raise ValueError("x0_idx should be a tensor from 0 to size - 1 when sort is 'noise' or 'x0")
+        elif not replace and (sort == "data" or sort == "x1"):
+            sort_idx = torch.argsort(j)
+            i = i[sort_idx]
+            j = j[sort_idx]
+            print(i, j)
+            if not (j == torch.arange(x1.shape[0], device=j.device)).all():
+                raise ValueError("x1_idx should be a tensor from 0 to size - 1 when sort is 'noise' or 'x0")
+
+        # Get the corresponding rotation matrices
+        rotations = Rs[i, j, :, :]
+        noise = x0[i]
+        # Align the data samples using the rotation matrices
+        x1_aligned = torch.bmm(x1[j], rotations)
+        data = x1_aligned
+
+        if mask is not None:
+            if mask.device != x0.device:
+                mask = mask.to(x0.device)
+            mask = mask[i]
+        # Output the permuted samples in the minibatch
+        return noise, data, mask
diff --git a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/kabsch_augmentation.py b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/kabsch_augmentation.py
new file mode 100644
index 0000000000..c1277be90c
--- /dev/null
+++ b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/kabsch_augmentation.py
@@ -0,0 +1,148 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+#
+# 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.
+
+
+from typing import Optional, Tuple
+
+import torch
+from torch import Tensor
+
+from bionemo.moco.interpolants.base_interpolant import pad_like
+
+
+class KabschAugmentation:
+    """Point-wise Kabsch alignment."""
+
+    def __init__(self):
+        """Initialize the KabschAugmentation instance.
+
+        Notes:
+            - This implementation assumes no required initialization arguments.
+            - You can add instance variables (e.g., `self.variable_name`) as needed.
+        """
+        pass  # No operations are performed when initializing with no args
+
+    def kabsch_align(self, target: Tensor, noise: Tensor):
+        """Find the Rotation matrix (R) such that RMSD is minimized between target @ R.T and noise.
+
+        Args:
+            target (Tensor): shape (N, *dim), data from source minibatch.
+            noise (Tensor): shape (N, *dim), noise from source minibatch.
+
+        Returns:
+            R (Tensor): shape (*dim, *dim), the rotation matrix.
+            Aliged Target (Tensor): target tensor rotated and shifted to reduced RMSD with noise
+        """
+        dimension = target.shape[-1]
+        noise_translation = noise.mean(dim=0)
+        noise_centered = noise - noise_translation
+        target_centered = target - target.mean(dim=0)
+
+        # Compute the covariance matrix
+        covariance_matix = target_centered.T @ noise_centered
+
+        # Compute the SVD of the covariance matrix
+        U, S, Vt = torch.linalg.svd(covariance_matix)
+        d = torch.sign(torch.linalg.det(Vt.T @ U.T)).item()
+        d_mat = torch.tensor([1] * (dimension - 1) + [d], device=Vt.device, dtype=Vt.dtype)
+        R = Vt.T @ torch.diag(d_mat) @ U.T
+
+        target_aligned = target_centered @ R.T + noise_translation
+
+        return R, target_aligned
+
+    def batch_kabsch_align(self, target: Tensor, noise: Tensor):
+        """Find the Rotation matrix (R) such that RMSD is minimized between target @ R.T and noise.
+
+        Args:
+            target (Tensor): shape (N, *dim), data from source minibatch.
+            noise (Tensor): shape (N, *dim), noise from source minibatch.
+
+        Returns:
+            R (Tensor): shape (*dim, *dim), the rotation matrix.
+            Aliged Target (Tensor): target tensor rotated and shifted to reduced RMSD with noise
+        """
+        # Corrected Batched Kabsch Alignment
+        batch_size, _, dimension = target.shape
+
+        # Center the target and noise tensors along the middle dimension (N) for each batch item
+        noise_translation = noise.mean(dim=1, keepdim=True)
+        noise_centered = noise - noise_translation
+        target_centered = target - target.mean(dim=1, keepdim=True)
+
+        # Compute the covariance matrix for each batch item
+        covariance_matrix = torch.matmul(target_centered.transpose(1, 2), noise_centered)
+
+        # Compute the SVD of the covariance matrix for each batch item
+        U, S, Vt = torch.linalg.svd(covariance_matrix)
+
+        # Adjust for proper rotation (determinant=1) for each batch item
+        d = torch.sign(torch.linalg.det(Vt @ U.transpose(-1, -2)))  # Keep as tensor for batch operations
+        d_mat = torch.diag_embed(
+            torch.cat(
+                [torch.ones(batch_size, dimension - 1, device=Vt.device, dtype=Vt.dtype), d.unsqueeze(-1)], dim=-1
+            )
+        )
+
+        R_batch = torch.matmul(torch.matmul(Vt.transpose(-1, -2), d_mat), U.transpose(-1, -2))
+
+        target_aligned = target_centered @ R_batch.transpose(-1, -2) + noise_translation
+        return R_batch, target_aligned
+
+    def apply_ot(
+        self,
+        x0: Tensor,
+        x1: Tensor,
+        mask: Optional[Tensor] = None,
+        align_noise_to_data=True,
+    ) -> Tuple[Tensor, Tensor]:
+        r"""Sample indices for noise and data in minibatch according to OT plan.
+
+        Compute the OT plan $\pi$ (wrt squared Euclidean cost after Kabsch alignment) between a source and a target
+        minibatch and draw source and target samples from pi $(x,z) \sim \pi$.
+
+        Args:
+            x0 (Tensor): shape (bs, *dim), noise from source minibatch.
+            x1 (Tensor): shape (bs, *dim), data from source minibatch.
+            mask (Optional[Tensor], optional): mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
+            replace (bool): sampling w/ or w/o replacement from the OT plan, default to False.
+            align_noise_to_data (bool): Direction of alignment default is True meaning it augments Noise to reduce error to Data.
+
+        Returns:
+            Tuple: tuple of 2 tensors, represents the noise and data samples following OT plan pi.
+        """
+        if x1.ndim > 2:
+            align_func = self.batch_kabsch_align
+        else:
+            align_func = self.kabsch_align
+        if mask is not None:
+            mask = pad_like(mask, x1)
+            x1 = x1 * mask
+            x0 = x0 * mask
+        if align_noise_to_data:
+            # Compute the rotation matrix R that aligns x0 to x1
+            R, aligned_x0 = align_func(x0, x1)
+            noise = aligned_x0
+            data = x1
+        else:
+            # Compute the rotation matrix R that aligns x1 to x0
+            R, aligned_x1 = align_func(x1, x0)
+            noise = x0
+            data = aligned_x1
+        if mask is not None:
+            noise = noise * mask
+            data = data * mask
+        # Output the permuted samples in the minibatch
+        return noise, data
diff --git a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport.py b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/ot_sampler.py
similarity index 92%
rename from sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport.py
rename to sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/ot_sampler.py
index e64aa192db..cb977828eb 100644
--- a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport.py
+++ b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/ot_sampler.py
@@ -34,7 +34,7 @@ class OTSampler:
 
     def __init__(
         self,
-        method: str,
+        method: str = "exact",
         device: Union[str, torch.device] = "cpu",
         num_threads: int = 1,
     ) -> None:
@@ -155,7 +155,7 @@ def apply_ot(
         x1: Tensor,
         mask: Optional[Tensor] = None,
         replace: Bool = False,
-        preserve: Optional[Literal["noise", "x0", "data", "x1"]] = "x0",
+        sort: Optional[Literal["noise", "x0", "data", "x1"]] = "x0",
     ) -> Tuple[Tensor, Tensor, Optional[Tensor]]:
         r"""Sample indices for noise and data in minibatch according to OT plan.
 
@@ -167,34 +167,34 @@ def apply_ot(
             x1 (Tensor): shape (bs, *dim), data from source minibatch.
             mask (Optional[Tensor], optional): mask to apply to the output, shape (batchsize, nodes), if not provided no mask is applied. Defaults to None.
             replace (bool): sampling w/ or w/o replacement from the OT plan, default to False.
-            preserve (str): Optional Literal string to sort either x1 or x0 based on the input.
+            sort (str): Optional Literal string to sort either x1 or x0 based on the input.
 
         Returns:
             Tuple: tuple of 2 tensors or 3 tensors if mask is used, represents the noise (plus mask) and data samples following OT plan pi.
         """
-        if replace and preserve is not None:
-            raise ValueError("Cannot sample with replacement and preserve")
+        if replace and sort is not None:
+            raise ValueError("Cannot sample with replacement and sort")
         # Calculate the optimal transport
         pi = self.get_ot_matrix(x0, x1, mask)
 
         # Sample (x0, x1) mapping indices from the OT matrix
         i, j = self.sample_map(pi, x0.shape[0], replace=replace)
-        if not replace and (preserve == "noise" or preserve == "x0"):
+        if not replace and (sort == "noise" or sort == "x0"):
             sort_idx = torch.argsort(i)
             i = i[sort_idx]
             j = j[sort_idx]
 
-            if not (i == torch.arange(x0.shape[0])).all():
-                raise ValueError("x0_idx should be a tensor from 0 to size - 1 when preserve is 'noise' or 'x0")
+            if not (i == torch.arange(x0.shape[0], device=i.device)).all():
+                raise ValueError("x0_idx should be a tensor from 0 to size - 1 when sort is 'noise' or 'x0")
             noise = x0
             data = x1[j]
-        elif not replace and (preserve == "data" or preserve == "x1"):
+        elif not replace and (sort == "data" or sort == "x1"):
             sort_idx = torch.argsort(j)
             i = i[sort_idx]
             j = j[sort_idx]
 
-            if not (j == torch.arange(x1.shape[0])).all():
-                raise ValueError("x1_idx should be a tensor from 0 to size - 1 when preserve is 'noise' or 'x0")
+            if not (j == torch.arange(x1.shape[0], device=j.device)).all():
+                raise ValueError("x1_idx should be a tensor from 0 to size - 1 when sort is 'noise' or 'x0")
             noise = x0[i]
             data = x1
         else:
@@ -202,5 +202,8 @@ def apply_ot(
             data = x1[j]
 
         # Output the permuted samples in the minibatch
-        mask = mask[i] if mask is not None else None
+        if mask is not None:
+            if mask.device != x0.device:
+                mask = mask.to(x0.device)
+            mask = mask[i]
         return noise, data, mask
diff --git a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/ot_types.py b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/ot_types.py
new file mode 100644
index 0000000000..bbe58fe2c1
--- /dev/null
+++ b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/continuous_time/continuous/optimal_transport/ot_types.py
@@ -0,0 +1,32 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-Apache2
+#
+# 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.
+
+
+from enum import Enum
+
+
+class OptimalTransportType(Enum):
+    """An enumeration representing the type ofOptimal Transport that can be used in Continuous Flow Matching.
+
+    - **EXACT**: Standard mini batch optimal transport defined in  https://arxiv.org/pdf/2302.00482.
+    - **EQUIVARIANT**: Adding roto/translation optimization to mini batch OT see https://arxiv.org/pdf/2306.15030  https://arxiv.org/pdf/2312.07168 4.2.
+    - **KABSCH**: Simple Kabsch alignment between each data and noise point, No permuation # https://arxiv.org/pdf/2410.22388 Sec 3.2
+
+    These prediction types can be used to train neural networks for specific tasks, such as denoising, image synthesis, or time-series forecasting.
+    """
+
+    EXACT = "exact"
+    EQUIVARIANT = "equivariant"
+    KABSCH = "kabsch"
diff --git a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/discrete_time/discrete/d3pm.py b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/discrete_time/discrete/d3pm.py
index 7e7b8fb43f..5d1f58f8e9 100644
--- a/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/discrete_time/discrete/d3pm.py
+++ b/sub-packages/bionemo-moco/src/bionemo/moco/interpolants/discrete_time/discrete/d3pm.py
@@ -29,6 +29,23 @@
 from bionemo.moco.schedules.noise.discrete_noise_schedules import DiscreteNoiseSchedule
 
 
+def _is_one_hot(data, num_classes):
+    """Check if data is one-hot encoded.
+
+    Parameters:
+    - data (Tensor): Input data to check.
+    - num_classes (int): Expected number of classes for one-hot encoding.
+
+    Returns:
+    - bool: True if data is one-hot encoded, False otherwise.
+    """
+    if len(data.shape) < 2 or data.shape[-1] != num_classes:
+        return False  # Not one-hot if last dim doesn't match num_classes or less than 2D
+
+    # Check if all vectors are one-hot
+    return (data.sum(dim=-1) == 1).all() and (data.flatten().shape[0] / num_classes) % 1 == 0
+
+
 class D3PM(Interpolant):
     """A Discrete Denoising Diffusion Probabilistic Model (D3PM) interpolant."""
 
@@ -152,7 +169,7 @@ def interpolate(self, data: Tensor, t: Tensor):
         Returns:
             Tensor: The interpolated discrete state `xt` at time `t`.
         """
-        if len(data.shape) <= 2:
+        if not _is_one_hot(data, self.num_classes):
             x1_hot = F.one_hot(data, self.num_classes)
         else:
             x1_hot = data
diff --git a/sub-packages/bionemo-moco/tests/bionemo/moco/interpolants/continuous_time/continuous/test_optimal_transport.py b/sub-packages/bionemo-moco/tests/bionemo/moco/interpolants/continuous_time/continuous/test_optimal_transport.py
index 9c594803ed..c9f17e3620 100644
--- a/sub-packages/bionemo-moco/tests/bionemo/moco/interpolants/continuous_time/continuous/test_optimal_transport.py
+++ b/sub-packages/bionemo-moco/tests/bionemo/moco/interpolants/continuous_time/continuous/test_optimal_transport.py
@@ -13,10 +13,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import numpy as np
 import pytest
 import torch
 
-from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport import OTSampler
+from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport.equivariant_ot_sampler import (
+    EquivariantOTSampler,
+)
+from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport.kabsch_augmentation import (
+    KabschAugmentation,
+)
+from bionemo.moco.interpolants.continuous_time.continuous.optimal_transport.ot_sampler import OTSampler
 
 
 @pytest.fixture
@@ -81,6 +88,12 @@ def exact_ot_sampler():
     return ot_sampler
 
 
+@pytest.fixture
+def kabsch_ot_sampler():
+    ot_sampler = EquivariantOTSampler(method="exact", num_threads=1)
+    return ot_sampler
+
+
 @pytest.mark.parametrize("device", ["cpu", "cuda"])
 @pytest.mark.parametrize("sampler", ["exact_ot_sampler"])
 @pytest.mark.parametrize("data", ["toy_data", "toy_masked_data"])
@@ -114,12 +127,13 @@ def test_exact_ot_sampler_sample_map(request, sampler, data, device):
         pytest.skip("CUDA is not available")
     ot_sampler = ot_sampler.to_device(device)
     x0, x1, mask, ground_truth_cost_matrix = request.getfixturevalue(data)
+    x0, x1 = x0.to(device), x1.to(device)
+    if mask is not None:
+        mask = mask.to(device)
     ot_matrix = ot_sampler.get_ot_matrix(x0, x1, mask=mask)
     correct_mapping = {0: 0, 1: 2, 2: 1}
 
     x0_idx, x1_idx = ot_sampler.sample_map(ot_matrix, x0.shape[0], replace=False)
-    # print(x0_idx)
-    # print(x1_idx)
     assert x0_idx.shape == (x0.shape[0],)
     assert x1_idx.shape == (x1.shape[0],)
     all_indices = set(range(x0.shape[0]))
@@ -133,9 +147,172 @@ def test_exact_ot_sampler_sample_map(request, sampler, data, device):
     x0_idx, x1_idx = ot_sampler.sample_map(ot_matrix, x0.shape[0], replace=True)
     assert x0_idx.shape == (x0.shape[0],)
     assert x1_idx.shape == (x1.shape[0],)
-    print(x0_idx)
-    print(x1_idx)
     for i in range(len(x0_idx)):
         sampled_indices.add(x0_idx[i].item())
         assert x1_idx[i].item() == correct_mapping[x0_idx[i].item()]
     # When replace is True, not all indices should be sampled
+
+    # Final test to check the apply_ot function
+    # First check preserving the order of noise
+    ot_sampled_x0, ot_sampled_x1, ot_sampled_mask = ot_sampler.apply_ot(x0, x1, mask=mask, replace=False, sort="x0")
+    for i in range(len(x0_idx)):
+        # Check if x0 output from apply_ot follows the correct order
+        assert torch.allclose(ot_sampled_x0[i], x0[i], atol=1e-7)
+        # Check if x1 output from apply_ot matches the correct mapping
+        assert torch.allclose(ot_sampled_x0[i], ot_sampled_x1[i], atol=0.1)
+        # Check if mask is preserved
+        if mask is not None:
+            assert (ot_sampled_mask[i] == mask[i]).all()
+
+    # Then check preserving the order of data
+    ot_sampled_x0, ot_sampled_x1, ot_sampled_mask = ot_sampler.apply_ot(x0, x1, mask=mask, replace=False, sort="x1")
+    reverse_mapping = {v: k for k, v in correct_mapping.items()}
+    for i in range(len(x0_idx)):
+        # Check if x1 output from apply_ot follows the correct order
+        assert torch.allclose(ot_sampled_x1[i], x1[i], atol=1e-7)
+        # Check if x1 output from apply_ot matches the correct mapping
+        assert torch.allclose(ot_sampled_x0[i], ot_sampled_x1[i], atol=0.1)
+        # Check if mask is preserved
+        if mask is not None:
+            assert (ot_sampled_mask[i] == mask[reverse_mapping[i]]).all()
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+@pytest.mark.parametrize("sampler", ["kabsch_ot_sampler"])
+def test_kabsch_ot_sampler_kabsch_align(request, sampler, device):
+    ot_sampler = request.getfixturevalue(sampler)
+    assert ot_sampler is not None
+    if device == "cuda" and not torch.cuda.is_available():
+        pytest.skip("CUDA is not available")
+    ot_sampler = ot_sampler.to_device(device)
+    x0 = torch.randn(size=(32, 3), device=device)
+    alpha = np.random.rand() * 2 * np.pi
+    R = torch.Tensor(np.array([[np.cos(alpha), -np.sin(alpha), 0], [np.sin(alpha), np.cos(alpha), 0], [0, 0, 1]])).to(
+        device
+    )
+    # Apply rotation and translation to x0
+    x0_rotated = x0 @ R.T + torch.ones_like(x0) * 5
+
+    R_kabsch = ot_sampler.kabsch_align(x0, x0_rotated)
+    assert R_kabsch.shape == (3, 3)
+    assert torch.allclose(R_kabsch, R, atol=1e-6)
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+@pytest.mark.parametrize("sampler", ["kabsch_ot_sampler"])
+def test_kabsch_ot_sample_map(request, sampler, device):
+    ot_sampler = request.getfixturevalue(sampler)
+    assert ot_sampler is not None
+    if device == "cuda" and not torch.cuda.is_available():
+        pytest.skip("CUDA is not available")
+    ot_sampler = ot_sampler.to_device(device)
+    x0 = torch.tensor(
+        [
+            [[2, 1, 2], [2, 1, -2], [-2, -1, 2], [-2, -1, -2], [0, 0, 0]],  # mask last, rectangle
+            [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 0], [0, 0, 0]],  # mask last 2, triangle
+            [[2, 0, 0], [0, 2, 0], [-2, 0, 0], [0, -2, 0], [0, 0, 2]],  # mask none, pyramid
+        ],
+        dtype=torch.float32,
+    ).to(device)
+    mask = torch.tensor([[1, 1, 1, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 1, 1]], dtype=torch.bool).to(device)
+    Rs = []
+    for i in range(x0.shape[0]):
+        alpha = np.random.rand() * 2 * np.pi
+        R = torch.Tensor(
+            np.array([[np.cos(alpha), -np.sin(alpha), 0], [np.sin(alpha), np.cos(alpha), 0], [0, 0, 1]])
+        ).to(device)
+        Rs.append(R)
+
+    # Define correct mapping
+    mapping = {0: 1, 1: 2, 2: 0}
+
+    # Create rotated x0
+    x0_rotated = torch.zeros_like(x0)
+    for i in range(len(x0)):
+        x0_rotated[mapping[i]] = x0[i] @ Rs[i].T
+
+    # Test the get_ot_matrix and sample_map functions
+    ot_matrix, Rs_output = ot_sampler.get_ot_matrix(x0, x0_rotated, mask=mask)
+    x0_idx, x0_rotated_idx = ot_sampler.sample_map(ot_matrix, x0.shape[0], replace=False)
+    assert x0_idx.shape == (x0.shape[0],)
+    assert x0_rotated_idx.shape == (x0_rotated.shape[0],)
+
+    rotations = Rs_output[x0_idx, x0_rotated_idx]
+
+    # Make sure the Rotation matrices are correct by checking if x0_rotated can be rotated back to x0
+    for i in range(len(x0_idx)):
+        assert x0_rotated_idx[i].item() == mapping[x0_idx[i].item()]
+        RR = rotations[i]
+        x0_rotate_back = x0_rotated[x0_rotated_idx[i]] @ RR
+        assert torch.allclose(x0[x0_idx[i]], x0_rotate_back, atol=1e-6)
+
+    # Final test to check the apply_ot function
+    # First check preserving the order of noise
+    realigned_x0, realigned_x0_rotated, realigned_mask = ot_sampler.apply_ot(
+        x0, x0_rotated, mask=mask, replace=False, sort="x0"
+    )
+    for i in range(len(x0_idx)):
+        # Check if x0 output from apply_ot follows the correct order
+        assert torch.allclose(realigned_x0[i], x0[i], atol=1e-6)
+        # Check if x1 output from apply_ot is rotated correctly
+        assert torch.allclose(realigned_x0[i], realigned_x0_rotated[i], atol=1e-6)
+        # Check if mask is preserved
+        assert (realigned_mask[i] == mask[i]).all()
+
+    # Then check preserving the order of data
+    realigned_x0, realigned_x0_rotated, realigned_mask = ot_sampler.apply_ot(
+        x0, x0_rotated, mask=mask, replace=False, sort="x1"
+    )
+    reverse_mapping = {v: k for k, v in mapping.items()}
+    for i in range(len(x0_idx)):
+        # Check if x0 output from apply_ot follows the correct order
+        # Since the realigned_x0_rotated is rotated back to x0, we check if it is equal to x0[reverse_mapping[i]]
+        assert torch.allclose(realigned_x0_rotated[i], x0[reverse_mapping[i]], atol=1e-6)
+        # Check if x1 output from apply_ot is rotated correctly
+        assert torch.allclose(realigned_x0[i], realigned_x0_rotated[i], atol=1e-6)
+        # Check if mask is preserved
+        assert (realigned_mask[i] == mask[reverse_mapping[i]]).all()
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+def test_kabsch_augmentation(request, device):
+    augmentor = KabschAugmentation()
+    assert augmentor is not None
+    if device == "cuda" and not torch.cuda.is_available():
+        pytest.skip("CUDA is not available")
+    x0 = torch.randn(size=(32, 3), device=device)
+    alpha = np.random.rand() * 2 * np.pi
+    R = torch.Tensor(np.array([[np.cos(alpha), -np.sin(alpha), 0], [np.sin(alpha), np.cos(alpha), 0], [0, 0, 1]])).to(
+        device
+    )
+    # Apply rotation and translation to x0
+    x0_rotated = x0 @ R.T + torch.ones_like(x0) * 5
+    R_kabsch, _ = augmentor.kabsch_align(x0, x0_rotated)
+    assert R_kabsch.shape == (3, 3)
+    assert torch.allclose(R_kabsch, R, atol=1e-6)
+    x0_aligned, x0_copy = augmentor.apply_ot(x0_rotated, x0, align_noise_to_data=True)
+    assert torch.allclose(x0, x0_copy, atol=1e-6)
+    assert torch.allclose(x0_aligned, x0, atol=5e-6)
+
+    x0_rotated_copy, x0_rotated_aligned = augmentor.apply_ot(x0_rotated, x0, align_noise_to_data=False)
+    assert torch.allclose(x0_rotated, x0_rotated_copy, atol=1e-6)
+    assert torch.allclose(x0_rotated_aligned, x0_rotated, atol=5e-6)
+
+    # Batch wise tests
+    x0 = torch.randn(size=(10, 32, 3), device=device)
+    alpha = np.random.rand() * 2 * np.pi
+    R = torch.Tensor(np.array([[np.cos(alpha), -np.sin(alpha), 0], [np.sin(alpha), np.cos(alpha), 0], [0, 0, 1]])).to(
+        device
+    )
+    # Apply rotation and translation to x0
+    x0_rotated = x0 @ R.T + torch.ones_like(x0) * 5
+    R_kabsch, _ = augmentor.batch_kabsch_align(x0, x0_rotated)
+    assert R_kabsch.shape == (10, 3, 3)
+    assert torch.allclose(R_kabsch, R, atol=1e-6)
+    x0_aligned, x0_copy = augmentor.apply_ot(x0_rotated, x0, align_noise_to_data=True)
+    assert torch.allclose(x0, x0_copy, atol=1e-6)
+    assert torch.allclose(x0_aligned, x0, atol=5e-6)  # values are close but error ranges from <1 to 2 e -6
+
+    x0_rotated_copy, x0_rotated_aligned = augmentor.apply_ot(x0_rotated, x0, align_noise_to_data=False)
+    assert torch.allclose(x0_rotated, x0_rotated_copy, atol=1e-6)
+    assert torch.allclose(x0_rotated_aligned, x0_rotated, atol=5e-6)
diff --git a/sub-packages/bionemo-moco/tests/bionemo/moco/interpolants/discrete_time/discrete/test_d3pm.py b/sub-packages/bionemo-moco/tests/bionemo/moco/interpolants/discrete_time/discrete/test_d3pm.py
index 2993c09c1a..f88983f98c 100644
--- a/sub-packages/bionemo-moco/tests/bionemo/moco/interpolants/discrete_time/discrete/test_d3pm.py
+++ b/sub-packages/bionemo-moco/tests/bionemo/moco/interpolants/discrete_time/discrete/test_d3pm.py
@@ -41,6 +41,15 @@ def test_d3pm_interpolate(d3pm, device):
     assert result.shape == (5, 10)
 
 
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+def test_d3pm_interpolate_square(d3pm, device):
+    data = torch.randint(0, 16, (5, 10, 10)).to(device)
+    t = torch.randint(0, 10, (5,)).to(device)
+    d3pm.to_device(device)
+    result = d3pm.interpolate(data, t)
+    assert result.shape == (5, 10, 10)
+
+
 @pytest.mark.parametrize("device", ["cpu", "cuda"])
 def test_d3pm_step(d3pm, device):
     # Create a random data tensor
@@ -78,3 +87,50 @@ def test_d3pm_step(d3pm, device):
     assert loss.item() == 0
     loss = d3pm.loss(logits, data, xt, time, vb_scale=0.5).mean()
     assert loss.item() < 1.0e-1
+
+
+@pytest.mark.parametrize("device", ["cpu", "cuda"])
+def test_d3pm_step_square(d3pm, device):
+    # Create a random data tensor
+    num_classes = 20
+    if device == "cuda" and not torch.cuda.is_available():
+        pytest.skip("CUDA is not available")
+    d3pm = d3pm.to_device(device)
+    torch.manual_seed(42)  # for reproducibility
+    data = torch.randint(0, num_classes, (32, 5, 6)).to(device)
+    # Create time tensor
+    T = 500
+    time = d3pm.sample_time(32, device=device) * 0 + T
+    # Create a mock model that outputs logits
+    logits = torch.zeros((32, 5, 6, num_classes), device=device)
+    # Set the logits to a large value (e.g., 1000) for the correct discrete choices
+    logits[:, :, :, :] = -1000  # initialize with a low value
+    # Set the logits to 1000 for the correct discrete choices
+    logits = logits.scatter(3, data.unsqueeze(-1), 1000)
+    # Sample noise
+    noise = d3pm.sample_prior(data.shape, device=device)
+    # Create model output and xt
+    model_out = logits  # torch.softmax(logits, dim=-1)
+    xt = data.clone()
+    xt[:, 0] = noise[:, 0]
+    # Take a step
+    next_xt = d3pm.step(model_out, time, xt)
+    # Assert shapes
+    assert next_xt.shape == data.shape
+    model_out_onehot = torch.nn.functional.one_hot(
+        model_out.argmax(-1), num_classes=num_classes
+    ).float()  # (B, N, num_classes)
+    nll = -torch.sum(torch.log(model_out_onehot.view(-1, num_classes) + 1e-8).gather(1, data.view(-1, 1)).squeeze(1))
+    assert nll < 1e-10
+    loss = d3pm.loss(
+        logits.reshape(logits.shape[0], -1, logits.shape[3]), data.reshape(data.shape[0], -1), xt, time
+    ).mean()
+    assert loss.item() == 0
+    loss = d3pm.loss(
+        logits.reshape(logits.shape[0], -1, logits.shape[3]),
+        data.reshape(data.shape[0], -1),
+        xt.reshape(xt.shape[0], -1),
+        time,
+        vb_scale=0.5,
+    ).mean()
+    assert loss.item() < 1.0e-1