Fix reshape_mask for 1D spatial dimensions.

reshape_mask sets scale_mode="linear" for dims==1 but is missing the input reshape to [N, C, W] that the 2D and 3D branches both perform. Add the missing reshape, matching the existing pattern.

comfy/utils.py

@@ -1241,6 +1241,7 @@ def reshape_mask(input_mask, output_shape):
     dims = len(output_shape) - 2
 
     if dims == 1:
+        input_mask = input_mask.reshape((-1, 1, input_mask.shape[-1]))
         scale_mode = "linear"
 
     if dims == 2:

tests-unit/comfy_test/mask_test.py

@@ -1,13 +1,16 @@
 """
-Tests for resolve_areas_and_cond_masks_multidim mask normalization and resizing.
+Tests for mask handling across arbitrary spatial dimensions.
 
-Covers 1D (audio), 2D (image), and 3D (video) spatial dims, including edge cases
-around batch size, spurious unsqueeze from upstream nodes, and size mismatches.
+Covers resolve_areas_and_cond_masks_multidim (conditioning masks) and
+reshape_mask (denoise masks) for 1D (audio), 2D (image), and 3D (video)
+spatial dims, including edge cases around batch size, spurious unsqueeze
+from upstream nodes, and size mismatches.
 """
 
 import torch
 
 from comfy.samplers import resolve_areas_and_cond_masks_multidim
+from comfy.utils import reshape_mask
 
 
 def make_cond(mask):
@@ -228,3 +231,106 @@ class TestAreaResolution:
         resolve_areas_and_cond_masks_multidim(conds, (100,), "cpu")
         area = conds[0]["area"]
         assert area == (50, 25)
+
+
+# ============================================================
+# reshape_mask — mask reshaping for F.interpolate
+# ============================================================
+
+class TestReshapeMask1D:
+    """Tests for reshape_mask with 1D output (e.g. audio with noise shape [B, C, L])."""
+
+    def test_4d_input_same_length(self):
+        """[1, 1, 1, L] input (typical from pipeline) — should reshape and expand channels."""
+        mask = torch.ones(1, 1, 1, 100)
+        result = reshape_mask(mask, torch.Size([1, 64, 100]))
+        assert result.shape == (1, 64, 100)
+
+    def test_4d_input_resize(self):
+        """[1, 1, 1, L1] input resized to different length."""
+        mask = torch.ones(1, 1, 1, 50)
+        result = reshape_mask(mask, torch.Size([1, 64, 100]))
+        assert result.shape == (1, 64, 100)
+
+    def test_3d_input(self):
+        """[1, 1, L] input — should work directly."""
+        mask = torch.ones(1, 1, 100)
+        result = reshape_mask(mask, torch.Size([1, 64, 100]))
+        assert result.shape == (1, 64, 100)
+
+    def test_2d_input(self):
+        """[B, L] input — should reshape to [B, 1, L]."""
+        mask = torch.ones(1, 50)
+        result = reshape_mask(mask, torch.Size([1, 64, 100]))
+        assert result.shape == (1, 64, 100)
+
+    def test_1d_input(self):
+        """[L] input — should reshape to [1, 1, L]."""
+        mask = torch.ones(50)
+        result = reshape_mask(mask, torch.Size([1, 64, 100]))
+        assert result.shape == (1, 64, 100)
+
+    def test_channel_repeat(self):
+        """Mask with 1 channel should repeat to match output channels."""
+        mask = torch.full((1, 1, 1, 100), 0.5)
+        result = reshape_mask(mask, torch.Size([1, 32, 100]))
+        assert result.shape == (1, 32, 100)
+        torch.testing.assert_close(result, torch.full_like(result, 0.5))
+
+    def test_batch_repeat(self):
+        """Single-batch mask should repeat to match output batch size."""
+        mask = torch.full((1, 1, 1, 100), 0.7)
+        result = reshape_mask(mask, torch.Size([4, 64, 100]))
+        assert result.shape == (4, 64, 100)
+
+    def test_values_preserved_no_resize(self):
+        """Values should be preserved when no resize is needed."""
+        values = torch.tensor([[[0.0, 0.5, 1.0]]])  # [1, 1, 3]
+        result = reshape_mask(values, torch.Size([1, 1, 3]))
+        torch.testing.assert_close(result, values)
+
+    def test_interpolation_values(self):
+        """Linear interpolation should produce sensible intermediate values."""
+        mask = torch.tensor([[[[0.0, 1.0]]]])  # [1, 1, 1, 2]
+        result = reshape_mask(mask, torch.Size([1, 1, 4]))
+        assert result.shape == (1, 1, 4)
+        # Should interpolate from 0 to 1
+        assert result[0, 0, 0].item() < result[0, 0, -1].item()
+
+
+class TestReshapeMask2D:
+    """Regression tests for reshape_mask with 2D output (image models)."""
+
+    def test_standard_resize(self):
+        """[1, 1, H, W] mask resized to different resolution."""
+        mask = torch.ones(1, 1, 32, 32)
+        result = reshape_mask(mask, torch.Size([1, 4, 64, 64]))
+        assert result.shape == (1, 4, 64, 64)
+
+    def test_same_size(self):
+        """[1, 1, H, W] mask with matching size — no resize needed."""
+        mask = torch.rand(1, 1, 64, 64)
+        result = reshape_mask(mask, torch.Size([1, 4, 64, 64]))
+        assert result.shape == (1, 4, 64, 64)
+
+    def test_3d_input(self):
+        """[B, H, W] input — should reshape to [B, 1, H, W]."""
+        mask = torch.ones(1, 32, 32)
+        result = reshape_mask(mask, torch.Size([1, 4, 64, 64]))
+        assert result.shape == (1, 4, 64, 64)
+
+
+class TestReshapeMask3D:
+    """Regression tests for reshape_mask with 3D output (video models)."""
+
+    def test_standard_resize(self):
+        """[1, 1, T, H, W] mask resized to different resolution."""
+        mask = torch.ones(1, 1, 8, 32, 32)
+        result = reshape_mask(mask, torch.Size([1, 4, 8, 64, 64]))
+        assert result.shape == (1, 4, 8, 64, 64)
+
+    def test_4d_input(self):
+        """[B, T, H, W] input — should reshape to [1, 1, T, H, W]."""
+        mask = torch.ones(1, 8, 32, 32)
+        result = reshape_mask(mask, torch.Size([1, 4, 8, 64, 64]))
+        assert result.shape == (1, 4, 8, 64, 64)