fix(freeu): run FFT in float32 for float16 inputs to avoid ComplexHalf

src/diffusers/utils/torch_utils.py

@@ -223,8 +223,10 @@ def fourier_filter(x_in: "torch.Tensor", threshold: int, scale: int) -> "torch.T
     # Non-power of 2 images must be float32
     if (W & (W - 1)) != 0 or (H & (H - 1)) != 0:
         x = x.to(dtype=torch.float32)
-    # fftn does not support bfloat16
-    elif x.dtype == torch.bfloat16:
+    # fftn does not support bfloat16, and produces the experimental ComplexHalf
+    # dtype (torch.complex32) when given float16, which is numerically unstable
+    # and triggers a UserWarning.
+    elif x.dtype in (torch.bfloat16, torch.float16):
         x = x.to(dtype=torch.float32)
 
     # FFT

tests/others/test_utils.py

@@ -204,6 +204,49 @@ def test_deprecate_testing_utils_module(self):
         ), f"Expected deprecation message substring not found, got: {messages}"
 
 
+class FourierFilterTester(unittest.TestCase):
+    """Tests for :func:`diffusers.utils.torch_utils.fourier_filter` (FreeU helper)."""
+
+    def _run_without_complexhalf_warning(self, dtype) -> "torch.Tensor":
+        import torch
+
+        from diffusers.utils.torch_utils import fourier_filter
+
+        x = torch.randn(1, 4, 32, 32, dtype=dtype)
+        with warnings.catch_warnings(record=True) as caught:
+            warnings.simplefilter("always")
+            out = fourier_filter(x, threshold=1, scale=0.5)
+
+        messages = [str(w.message) for w in caught]
+        assert not any("ComplexHalf" in m for m in messages), (
+            f"Unexpected ComplexHalf warning emitted by fourier_filter: {messages}"
+        )
+        return out
+
+    def test_fourier_filter_float16_no_complexhalf_warning(self):
+        import torch
+
+        out = self._run_without_complexhalf_warning(torch.float16)
+        assert out.dtype == torch.float16
+
+    def test_fourier_filter_bfloat16_no_complexhalf_warning(self):
+        import torch
+
+        out = self._run_without_complexhalf_warning(torch.bfloat16)
+        assert out.dtype == torch.bfloat16
+
+    def test_fourier_filter_preserves_dtype_and_shape(self):
+        import torch
+
+        from diffusers.utils.torch_utils import fourier_filter
+
+        for dtype in (torch.float32, torch.float16, torch.bfloat16):
+            x = torch.randn(2, 3, 16, 16, dtype=dtype)
+            out = fourier_filter(x, threshold=1, scale=0.5)
+            assert out.dtype == dtype
+            assert out.shape == x.shape
+
+
 # Copied from https://github.com/huggingface/transformers/blob/main/tests/utils/test_expectations.py
 class ExpectationsTester(unittest.TestCase):
     def test_expectations(self):