diff --git a/tests/test/test_time_t.py b/tests/test/test_time_t.py
deleted file mode 100644
index 21ad5cc..0000000
--- a/tests/test/test_time_t.py
+++ /dev/null
@@ -1,70 +0,0 @@
-"""Tests for time Tensor t."""
-
-# Author: Kilian Fatras <kilian.fatras@mila.quebec>
-
-import pytest
-import torch
-
-from torchcfm.conditional_flow_matching import (
-    ConditionalFlowMatcher,
-    ExactOptimalTransportConditionalFlowMatcher,
-    SchrodingerBridgeConditionalFlowMatcher,
-    TargetConditionalFlowMatcher,
-    VariancePreservingConditionalFlowMatcher,
-)
-
-seed = 1994
-batch_size = 128
-
-
-@pytest.mark.parametrize(
-    "FM",
-    [
-        ConditionalFlowMatcher(sigma=0.0),
-        ExactOptimalTransportConditionalFlowMatcher(sigma=0.0),
-        TargetConditionalFlowMatcher(sigma=0.0),
-        SchrodingerBridgeConditionalFlowMatcher(sigma=0.0),
-        VariancePreservingConditionalFlowMatcher(sigma=0.0),
-    ],
-)
-def test_random_Tensor_t(FM):
-    # Test sample_location_and_conditional_flow functions
-    x0 = torch.randn(batch_size, 2)
-    x1 = torch.randn(batch_size, 2)
-
-    torch.manual_seed(seed)
-    t_given = torch.rand(batch_size)
-    t_given, xt, ut = FM.sample_location_and_conditional_flow(x0, x1, t=t_given)
-
-    torch.manual_seed(seed)
-    t_random, xt, ut = FM.sample_location_and_conditional_flow(x0, x1, t=None)
-
-    assert any(t_given == t_random)
-
-
-@pytest.mark.parametrize(
-    "FM",
-    [
-        ExactOptimalTransportConditionalFlowMatcher(sigma=0.0),
-        SchrodingerBridgeConditionalFlowMatcher(sigma=0.0),
-    ],
-)
-def test_guided_random_Tensor_t(FM):
-    # Test guided_sample_location_and_conditional_flow functions
-    x0 = torch.randn(batch_size, 2)
-    y0 = torch.randint(high=10, size=(batch_size, 1))
-    x1 = torch.randn(batch_size, 2)
-    y1 = torch.randint(high=10, size=(batch_size, 1))
-
-    torch.manual_seed(seed)
-    t_given = torch.rand(batch_size)
-    t_given, xt, ut, y0, y1 = FM.guided_sample_location_and_conditional_flow(
-        x0, x1, y0=y0, y1=y1, t=t_given
-    )
-
-    torch.manual_seed(seed)
-    t_random, xt, ut, y0, y1 = FM.guided_sample_location_and_conditional_flow(
-        x0, x1, y0=y0, y1=y1, t=None
-    )
-
-    assert any(t_given == t_random)