diff --git a/models/vanilla_diffusion/sequence_dataloader.py b/models/vanilla_diffusion/sequence_dataloader.py
deleted file mode 100644
index 619fc6cb..00000000
--- a/models/vanilla_diffusion/sequence_dataloader.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import pandas as pd
-import numpy as np
-
-import torch
-import torchvision.transforms as T
-import torch.nn.functional as F
-import pytorch_lightning as pl 
-from torch.utils.data import Dataset, DataLoader 
-
-class SequenceDataset(Dataset):
-    def __init__(self, data_path, transform):
-        super().__init__()
-        self.data = pd.read_csv(data_path, sep="\t")
-        self.transform = transform
-
-    def __len__(self):
-        return len(self.data)
-    
-    def __getitem__(self, index):
-        # Iterating through DNA sequences from dataset and one-hot encoding all nucleotides
-        current_seq = self.data["raw_sequence"][index]
-        if 'N' not in current_seq: 
-            X_seq = np.array(self.one_hot_encode(current_seq, ['A','C','T','G'], 200))
-            X_seq = X_seq.T
-            X_seq[X_seq == 0] = -1
-
-            # One hot encoding the components that provide tissue context
-            all_cell_type = self.data["component"].to_numpy()
-            all_cell_type = F.one_hot(torch.from_numpy(all_cell_type), len(self.data.component.unique()))
-            X_cell_type = all_cell_type[index]
-            
-            if self.transform:
-                X_seq = self.transform(X_seq)
-                X_cell_type = self.transform(X_cell_type)
-
-            return X_seq, X_cell_type
-
-    # Function for one hot encoding each line of the sequence dataset
-    def one_hot_encode(self, seq, alphabet, max_seq_len):
-        """
-        One-hot encoding a sequence 
-        """
-        seq_len = len(seq)
-        seq_array = np.zeros((max_seq_len, len(alphabet)))
-        for i in range(seq_len):
-            seq_array[i, alphabet.index(seq[i])] = 1 
-        return seq_array
-     
-
-class SequenceDataModule(pl.LightningDataModule):
-    def __init__(self, train_path, val_path, test_path, transform, batch_size=32, num_workers=3):
-        super().__init__()
-        self.train_path = train_path
-        self.val_path = val_path
-        self.test_path = test_path
-        self.transform = transform
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-
-
-    def prepare_data(self):
-        pass
-
-    def setup(self):
-       #transform = T.Compose([T.ToTensor()])
-       self.train_data = SequenceDataset(self.train_path, transform = self.transform)
-       self.val_data = SequenceDataset(self.val_path, transform = self.transform)
-       self.test_data = SequenceDataset(self.test_path, transform = self.transform)
-
-    def train_dataloader(self):
-        return DataLoader(self.train_data,
-                          self.batch_size, 
-                          shuffle=True, 
-                          num_workers=self.num_workers, 
-                          pin_memory=True)
-
-    def val_dataloder(self):
-        return DataLoader(self.val_data,
-                          self.batch_size, 
-                          shuffle=True,
-                          num_workers=self.num_workers,
-                          pin_memory=True)
-
-    def test_dataloader(self):
-        return DataLoader(self.test_data,
-                          self.batch_size, 
-                          shuffle=True, 
-                          num_workers=self.num_workers, 
-                          pin_memory=True)
-