diff --git a/test/test_time_t.py b/tests/test/test_time_t.py
similarity index 100%
rename from test/test_time_t.py
rename to tests/test/test_time_t.py
diff --git a/tests/test_time_t.py b/tests/test_time_t.py
new file mode 100644
index 0000000..21ad5cc
--- /dev/null
+++ b/tests/test_time_t.py
@@ -0,0 +1,70 @@
+"""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)