fix more cases

Author: faaany

tests/pipelines/controlnet_hunyuandit/test_controlnet_hunyuandit.py

@@ -238,9 +238,34 @@ def test_canny(self):
 
         original_image = image[-3:, -3:, -1].flatten()
 
-        expected_image = np.array(
-            [0.43652344, 0.4399414, 0.44921875, 0.45043945, 0.45703125, 0.44873047, 0.43579102, 0.44018555, 0.42578125]
-        )
+        if torch_device == "xpu":
+            expected_image = np.array(
+                [
+                    0.2944336,
+                    0.30981445,
+                    0.24389648,
+                    0.2890625,
+                    0.32006836,
+                    0.2578125,
+                    0.31469727,
+                    0.34545898,
+                    0.28515625,
+                ]
+            )
+        else:
+            expected_image = np.array(
+                [
+                    0.43652344,
+                    0.4399414,
+                    0.44921875,
+                    0.45043945,
+                    0.45703125,
+                    0.44873047,
+                    0.43579102,
+                    0.44018555,
+                    0.42578125,
+                ]
+            )
 
         assert np.abs(original_image.flatten() - expected_image).max() < 1e-2
 
@@ -277,9 +302,24 @@ def test_pose(self):
 
         original_image = image[-3:, -3:, -1].flatten()
 
-        expected_image = np.array(
-            [0.4091797, 0.4177246, 0.39526367, 0.4194336, 0.40356445, 0.3857422, 0.39208984, 0.40429688, 0.37451172]
-        )
+        if torch_device == "xpu":
+            expected_image = np.array(
+                [0.59277344, 0.6640625, 0.75146484, 0.74121094, 0.7636719, 0.7426758, 0.6879883, 0.6455078, 0.6386719]
+            )
+        else:
+            expected_image = np.array(
+                [
+                    0.4091797,
+                    0.4177246,
+                    0.39526367,
+                    0.4194336,
+                    0.40356445,
+                    0.3857422,
+                    0.39208984,
+                    0.40429688,
+                    0.37451172,
+                ]
+            )
 
         assert np.abs(original_image.flatten() - expected_image).max() < 1e-2
 
@@ -316,9 +356,34 @@ def test_depth(self):
 
         original_image = image[-3:, -3:, -1].flatten()
 
-        expected_image = np.array(
-            [0.31982422, 0.32177734, 0.30126953, 0.3190918, 0.3100586, 0.31396484, 0.3232422, 0.33544922, 0.30810547]
-        )
+        if torch_device == "xpu":
+            expected_image = np.array(
+                [
+                    0.7529297,
+                    0.69628906,
+                    0.62060547,
+                    0.76708984,
+                    0.7158203,
+                    0.65185547,
+                    0.74853516,
+                    0.69628906,
+                    0.6455078,
+                ]
+            )
+        else:
+            expected_image = np.array(
+                [
+                    0.31982422,
+                    0.32177734,
+                    0.30126953,
+                    0.3190918,
+                    0.3100586,
+                    0.31396484,
+                    0.3232422,
+                    0.33544922,
+                    0.30810547,
+                ]
+            )
 
         assert np.abs(original_image.flatten() - expected_image).max() < 1e-2
 
@@ -356,8 +421,34 @@ def test_multi_controlnet(self):
         assert image.shape == (1024, 1024, 3)
 
         original_image = image[-3:, -3:, -1].flatten()
-        expected_image = np.array(
-            [0.43652344, 0.44018555, 0.4494629, 0.44995117, 0.45654297, 0.44848633, 0.43603516, 0.4404297, 0.42626953]
-        )
+
+        if torch_device == "xpu":
+            expected_image = np.array(
+                [
+                    0.28320312,
+                    0.28125,
+                    0.31347656,
+                    0.29736328,
+                    0.29077148,
+                    0.33740234,
+                    0.33911133,
+                    0.35668945,
+                    0.38452148,
+                ]
+            )
+        else:
+            expected_image = np.array(
+                [
+                    0.43652344,
+                    0.44018555,
+                    0.4494629,
+                    0.44995117,
+                    0.45654297,
+                    0.44848633,
+                    0.43603516,
+                    0.4404297,
+                    0.42626953,
+                ]
+            )
 
         assert np.abs(original_image.flatten() - expected_image).max() < 1e-2