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patrickvonplatenpatrickvonplaten
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Lines changed: 13 additions & 12 deletions

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‎src/diffusers/models/resnet.py‎

Lines changed: 3 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -207,6 +207,9 @@ def __init__(
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self.updown = up or down
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# if self.updown:
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# import ipdb; ipdb.set_trace()
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if up:
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self.h_upd = Upsample(channels, use_conv=False, dims=dims)
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self.x_upd = Upsample(channels, use_conv=False, dims=dims)

‎tests/test_modeling_utils.py‎

Lines changed: 10 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -259,7 +259,7 @@ def test_output_pretrained(self):
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# fmt: off
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expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase):
@@ -607,7 +607,7 @@ def test_output_pretrained(self):
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expected_output_slice = torch.tensor([-0.0690, -0.0531, 0.0633, -0.0660, -0.0541, 0.0650, -0.0656, -0.0555, 0.0617])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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class TemporalUNetModelTests(ModelTesterMixin, unittest.TestCase):
@@ -678,7 +678,7 @@ def test_output_pretrained(self):
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expected_output_slice = torch.tensor([-0.2714, 0.1042, -0.0794, -0.2820, 0.0803, -0.0811, -0.2345, 0.0580, -0.0584])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
@@ -753,7 +753,7 @@ def test_output_pretrained_ve_small(self):
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expected_output_slice = torch.tensor([3.1909e-07, -8.5393e-08, 4.8460e-07, -4.5550e-07, -1.3205e-06, -6.3475e-07, 9.7837e-07, 2.9974e-07, 1.2345e-06])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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def test_output_pretrained_ve_large(self):
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model = NCSNpp.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy")
@@ -779,7 +779,7 @@ def test_output_pretrained_ve_large(self):
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expected_output_slice = torch.tensor([-8.3299e-07, -9.0431e-07, 4.0585e-08, 9.7563e-07, 1.0280e-06, 1.0133e-06, 1.4979e-06, -2.9716e-07, -6.1817e-07])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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def test_output_pretrained_vp(self):
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model = NCSNpp.from_pretrained("fusing/ddpm-cifar10-vp-dummy")
@@ -805,7 +805,7 @@ def test_output_pretrained_vp(self):
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expected_output_slice = torch.tensor([-3.9086e-07, -1.1001e-05, 1.8881e-06, 1.1106e-05, 1.6629e-06, 2.9820e-06, 8.4978e-06, 8.0253e-07, 1.5435e-06])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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class VQModelTests(ModelTesterMixin, unittest.TestCase):
@@ -878,10 +878,9 @@ def test_output_pretrained(self):
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output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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expected_output_slice = torch.tensor([-1.1321, 0.1056, 0.3505, -0.6461, -0.2014, 0.0419, -0.5763, -0.8462,
882-
-0.4218])
881+
expected_output_slice = torch.tensor([-1.1321, 0.1056, 0.3505, -0.6461, -0.2014, 0.0419, -0.5763, -0.8462, -0.4218])
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# fmt: on
884-
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase):
@@ -950,10 +949,9 @@ def test_output_pretrained(self):
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output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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expected_output_slice = torch.tensor([-0.0814, -0.0229, -0.1320, -0.4123, -0.0366, -0.3473, 0.0438, -0.1662,
954-
0.1750])
952+
expected_output_slice = torch.tensor([-0.0814, -0.0229, -0.1320, -0.4123, -0.0366, -0.3473, 0.0438, -0.1662, 0.1750])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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class PipelineTesterMixin(unittest.TestCase):

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