diff --git a/tests/test_fp8_mps.py b/tests/test_fp8_mps.py
index 1f4c58b7b..6ecd876ab 100644
--- a/tests/test_fp8_mps.py
+++ b/tests/test_fp8_mps.py
@@ -7,10 +7,12 @@ These tests verify that:
   2. The round-trip (quantize on CPU -> result on original device) is numerically sound.
   3. No "Placeholder storage has not been allocated on MPS device!" errors occur.
 """
-import sys
 import pytest
 import torch
 
+import comfy.float
+from comfy.quant_ops import TensorCoreFP8E4M3Layout, TensorCoreFP8E5M2Layout
+
 # Skip the entire module if MPS is not available
 pytestmark = pytest.mark.skipif(
     not torch.backends.mps.is_available(),
@@ -30,9 +32,6 @@ class TestStochasticRoundingMPS:
 
     def test_stochastic_rounding_fp8_e4m3fn_on_mps(self):
         """stochastic_rounding must not crash when input is on MPS and target dtype is float8_e4m3fn."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        import comfy.float
-
         x = _make_mps_tensor((64, 64), dtype=torch.float32)
         result = comfy.float.stochastic_rounding(x, dtype=torch.float8_e4m3fn, seed=42)
 
@@ -41,9 +40,6 @@ class TestStochasticRoundingMPS:
 
     def test_stochastic_rounding_fp8_e5m2_on_mps(self):
         """stochastic_rounding must not crash when input is on MPS and target dtype is float8_e5m2."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        import comfy.float
-
         x = _make_mps_tensor((64, 64), dtype=torch.float32)
         result = comfy.float.stochastic_rounding(x, dtype=torch.float8_e5m2, seed=42)
 
@@ -52,9 +48,6 @@ class TestStochasticRoundingMPS:
 
     def test_stochastic_rounding_fp8_result_on_cpu(self):
         """Result of FP8 rounding from MPS input should be on CPU (since MPS can't hold FP8)."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        import comfy.float
-
         x = _make_mps_tensor((32, 32), dtype=torch.float32)
         result = comfy.float.stochastic_rounding(x, dtype=torch.float8_e4m3fn, seed=42)
 
@@ -63,9 +56,6 @@ class TestStochasticRoundingMPS:
 
     def test_stochastic_rounding_non_fp8_still_works(self):
         """Non-FP8 dtypes on MPS must still work as before (no regression)."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        import comfy.float
-
         x = _make_mps_tensor((32, 32), dtype=torch.float32)
 
         r16 = comfy.float.stochastic_rounding(x, dtype=torch.float16, seed=0)
@@ -78,9 +68,6 @@ class TestStochasticRoundingMPS:
 
     def test_stochastic_rounding_fp8_numerical_sanity(self):
         """FP8 round-trip (float32 -> fp8 -> float32) should have bounded error."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        import comfy.float
-
         x = torch.randn(128, 128, device="mps", dtype=torch.float32)
         x_clamped = torch.clamp(x, min=-448, max=448)  # FP8 e4m3fn range
 
@@ -99,15 +86,9 @@ class TestStochasticRoundingMPS:
 class TestManualStochasticRoundMPS:
     """Tests for comfy.float.manual_stochastic_round_to_float8 on MPS device."""
 
-    def test_manual_round_fp8_on_mps_tensor_fails_without_fix(self):
-        """Calling manual_stochastic_round_to_float8 with MPS generator should fail or be handled."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        import comfy.float
-
+    def test_manual_round_fp8_on_mps_tensor(self):
+        """stochastic_rounding handles MPS generator internally without 'Placeholder storage' error."""
         x = _make_mps_tensor((16, 16), dtype=torch.float32)
-        # The generator device matters - this is the root cause of the second error
-        # (Placeholder storage has not been allocated on MPS device!)
-        # After fix, stochastic_rounding should handle this internally
         result = comfy.float.stochastic_rounding(x, dtype=torch.float8_e4m3fn, seed=42)
         assert result.dtype == torch.float8_e4m3fn
 
@@ -117,9 +98,6 @@ class TestNVFP4StochasticRoundMPS:
 
     def test_nvfp4_stochastic_round_on_mps(self):
         """stochastic_round_quantize_nvfp4 creates FP8 block scales internally."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        import comfy.float
-
         # NVFP4 requires 2D input with dimensions divisible by 16
         x = torch.randn(32, 32, device="mps", dtype=torch.float32)
         scale = torch.tensor(1.0, device="mps", dtype=torch.float32)
@@ -138,9 +116,6 @@ class TestQuantOpsMPS:
 
     def test_fp8_layout_quantize_on_mps(self):
         """TensorCoreFP8E4M3Layout.quantize must work with MPS tensors."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        from comfy.quant_ops import TensorCoreFP8E4M3Layout
-
         x = _make_mps_tensor((64, 64), dtype=torch.bfloat16)
         qdata, params = TensorCoreFP8E4M3Layout.quantize(
             x, scale="recalculate", stochastic_rounding=42
@@ -151,9 +126,6 @@ class TestQuantOpsMPS:
 
     def test_fp8_layout_quantize_without_stochastic_on_mps(self):
         """TensorCoreFP8E4M3Layout.quantize with stochastic_rounding=0 uses ck.quantize_per_tensor_fp8."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        from comfy.quant_ops import TensorCoreFP8E4M3Layout
-
         x = _make_mps_tensor((64, 64), dtype=torch.bfloat16)
         qdata, params = TensorCoreFP8E4M3Layout.quantize(
             x, scale="recalculate", stochastic_rounding=0
@@ -163,9 +135,6 @@ class TestQuantOpsMPS:
 
     def test_fp8_e5m2_layout_quantize_on_mps(self):
         """TensorCoreFP8E5M2Layout.quantize must work with MPS tensors."""
-        sys.path.insert(0, "/Users/tkhan/comfyui/ComfyUI")
-        from comfy.quant_ops import TensorCoreFP8E5M2Layout
-
         x = _make_mps_tensor((64, 64), dtype=torch.float32)
         qdata, params = TensorCoreFP8E5M2Layout.quantize(
             x, scale="recalculate", stochastic_rounding=42