diff --git a/examples/controlnet/train_controlnet.py b/examples/controlnet/train_controlnet.py
index 20c4fbe189a1..b1aa63b60a76 100644
--- a/examples/controlnet/train_controlnet.py
+++ b/examples/controlnet/train_controlnet.py
@@ -577,7 +577,7 @@ def make_train_dataset(args, tokenizer, accelerator):
 
     if args.conditioning_image_column is None:
         conditioning_image_column = column_names[2]
-        logger.info(f"conditioning image column defaulting to {caption_column}")
+        logger.info(f"conditioning image column defaulting to {conditioning_image_column}")
     else:
         conditioning_image_column = args.conditioning_image_column
         if conditioning_image_column not in column_names:
diff --git a/src/diffusers/models/attention.py b/src/diffusers/models/attention.py
index f271e00f8639..5538a7b8249d 100644
--- a/src/diffusers/models/attention.py
+++ b/src/diffusers/models/attention.py
@@ -224,7 +224,14 @@ def __init__(
                 f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
             )
 
+        # Define 3 blocks. Each block has its own normalization layer.
         # 1. Self-Attn
+        if self.use_ada_layer_norm:
+            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
+        elif self.use_ada_layer_norm_zero:
+            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
+        else:
+            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
         self.attn1 = Attention(
             query_dim=dim,
             heads=num_attention_heads,
@@ -235,10 +242,16 @@ def __init__(
             upcast_attention=upcast_attention,
         )
 
-        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
-
         # 2. Cross-Attn
         if cross_attention_dim is not None or double_self_attention:
+            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+            # the second cross attention block.
+            self.norm2 = (
+                AdaLayerNorm(dim, num_embeds_ada_norm)
+                if self.use_ada_layer_norm
+                else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+            )
             self.attn2 = Attention(
                 query_dim=dim,
                 cross_attention_dim=cross_attention_dim if not double_self_attention else None,
@@ -248,30 +261,13 @@ def __init__(
                 bias=attention_bias,
                 upcast_attention=upcast_attention,
             )  # is self-attn if encoder_hidden_states is none
-        else:
-            self.attn2 = None
-
-        if self.use_ada_layer_norm:
-            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
-        elif self.use_ada_layer_norm_zero:
-            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
-        else:
-            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
-
-        if cross_attention_dim is not None or double_self_attention:
-            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
-            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
-            # the second cross attention block.
-            self.norm2 = (
-                AdaLayerNorm(dim, num_embeds_ada_norm)
-                if self.use_ada_layer_norm
-                else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
-            )
         else:
             self.norm2 = None
+            self.attn2 = None
 
         # 3. Feed-forward
         self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
 
     def forward(
         self,
@@ -283,6 +279,8 @@ def forward(
         cross_attention_kwargs=None,
         class_labels=None,
     ):
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 1. Self-Attention
         if self.use_ada_layer_norm:
             norm_hidden_states = self.norm1(hidden_states, timestep)
         elif self.use_ada_layer_norm_zero:
@@ -292,7 +290,6 @@ def forward(
         else:
             norm_hidden_states = self.norm1(hidden_states)
 
-        # 1. Self-Attention
         cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
         attn_output = self.attn1(
             norm_hidden_states,
@@ -304,6 +301,7 @@ def forward(
             attn_output = gate_msa.unsqueeze(1) * attn_output
         hidden_states = attn_output + hidden_states
 
+        # 2. Cross-Attention
         if self.attn2 is not None:
             norm_hidden_states = (
                 self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
@@ -311,7 +309,6 @@ def forward(
             # TODO (Birch-San): Here we should prepare the encoder_attention mask correctly
             # prepare attention mask here
 
-            # 2. Cross-Attention
             attn_output = self.attn2(
                 norm_hidden_states,
                 encoder_hidden_states=encoder_hidden_states,
diff --git a/tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py b/tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py
index 939632943405..144107ec1c97 100644
--- a/tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py
+++ b/tests/pipelines/altdiffusion/test_alt_diffusion_img2img.py
@@ -166,7 +166,7 @@ def test_stable_diffusion_img2img_default_case(self):
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
 
         assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.4115, 0.3870, 0.4089, 0.4807, 0.4668, 0.4144, 0.4151, 0.4721, 0.4569])
+        expected_slice = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-3
diff --git a/tests/pipelines/dit/test_dit.py b/tests/pipelines/dit/test_dit.py
index c514c3c7fa1d..947fd3cbf43d 100644
--- a/tests/pipelines/dit/test_dit.py
+++ b/tests/pipelines/dit/test_dit.py
@@ -92,7 +92,7 @@ def test_inference(self):
         image_slice = image[0, -3:, -3:, -1]
 
         self.assertEqual(image.shape, (1, 16, 16, 3))
-        expected_slice = np.array([0.4380, 0.4141, 0.5159, 0.0000, 0.4282, 0.6680, 0.5485, 0.2545, 0.6719])
+        expected_slice = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457])
         max_diff = np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(max_diff, 1e-3)
 
diff --git a/tests/pipelines/latent_diffusion/test_latent_diffusion.py b/tests/pipelines/latent_diffusion/test_latent_diffusion.py
index 3f2dbe5cec2a..2ff7feda6317 100644
--- a/tests/pipelines/latent_diffusion/test_latent_diffusion.py
+++ b/tests/pipelines/latent_diffusion/test_latent_diffusion.py
@@ -125,7 +125,7 @@ def test_inference_text2img(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 16, 16, 3)
-        expected_slice = np.array([0.59450, 0.64078, 0.55509, 0.51229, 0.69640, 0.36960, 0.59296, 0.60801, 0.49332])
+        expected_slice = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
diff --git a/tests/pipelines/paint_by_example/test_paint_by_example.py b/tests/pipelines/paint_by_example/test_paint_by_example.py
index 81d1989200ac..14b045d6c480 100644
--- a/tests/pipelines/paint_by_example/test_paint_by_example.py
+++ b/tests/pipelines/paint_by_example/test_paint_by_example.py
@@ -129,7 +129,7 @@ def test_paint_by_example_inpaint(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.4701, 0.5555, 0.3994, 0.5107, 0.5691, 0.4517, 0.5125, 0.4769, 0.4539])
+        expected_slice = np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
diff --git a/tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py b/tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py
index b312c8184390..ba42b1fe9c5f 100644
--- a/tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py
+++ b/tests/pipelines/semantic_stable_diffusion/test_semantic_diffusion.py
@@ -154,7 +154,7 @@ def test_semantic_diffusion_ddim(self):
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5644, 0.6018, 0.4799, 0.5267, 0.5585, 0.4641, 0.516, 0.4964, 0.4792])
+        expected_slice = np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@@ -200,7 +200,7 @@ def test_semantic_diffusion_pndm(self):
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5095, 0.5674, 0.4668, 0.5126, 0.5697, 0.4675, 0.5278, 0.4964, 0.4945])
+        expected_slice = np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion.py b/tests/pipelines/stable_diffusion/test_stable_diffusion.py
index 857122782d35..79796afdf597 100644
--- a/tests/pipelines/stable_diffusion/test_stable_diffusion.py
+++ b/tests/pipelines/stable_diffusion/test_stable_diffusion.py
@@ -135,7 +135,7 @@ def test_stable_diffusion_ddim(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5643, 0.6017, 0.4799, 0.5267, 0.5584, 0.4641, 0.5159, 0.4963, 0.4791])
+        expected_slice = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -282,7 +282,7 @@ def test_stable_diffusion_pndm(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5094, 0.5674, 0.4667, 0.5125, 0.5696, 0.4674, 0.5277, 0.4964, 0.4945])
+        expected_slice = np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -322,19 +322,7 @@ def test_stable_diffusion_k_lms(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [
-                0.47082293033599854,
-                0.5371589064598083,
-                0.4562119245529175,
-                0.5220914483070374,
-                0.5733777284622192,
-                0.4795039892196655,
-                0.5465868711471558,
-                0.5074326395988464,
-                0.5042197108268738,
-            ]
-        )
+        expected_slice = np.array([0.4873, 0.5443, 0.4845, 0.5004, 0.5549, 0.4850, 0.5191, 0.4941, 0.5065])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -353,19 +341,7 @@ def test_stable_diffusion_k_euler_ancestral(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [
-                0.4707113206386566,
-                0.5372191071510315,
-                0.4563021957874298,
-                0.5220003724098206,
-                0.5734264850616455,
-                0.4794946610927582,
-                0.5463782548904419,
-                0.5074145197868347,
-                0.504422664642334,
-            ]
-        )
+        expected_slice = np.array([0.4872, 0.5444, 0.4846, 0.5003, 0.5549, 0.4850, 0.5189, 0.4941, 0.5067])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -384,19 +360,7 @@ def test_stable_diffusion_k_euler(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [
-                0.47082313895225525,
-                0.5371587872505188,
-                0.4562119245529175,
-                0.5220913887023926,
-                0.5733776688575745,
-                0.47950395941734314,
-                0.546586811542511,
-                0.5074326992034912,
-                0.5042197108268738,
-            ]
-        )
+        expected_slice = np.array([0.4873, 0.5443, 0.4845, 0.5004, 0.5549, 0.4850, 0.5191, 0.4941, 0.5065])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -468,19 +432,7 @@ def test_stable_diffusion_negative_prompt(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array(
-            [
-                0.5108221173286438,
-                0.5688379406929016,
-                0.4685141146183014,
-                0.5098261833190918,
-                0.5657756328582764,
-                0.4631010890007019,
-                0.5226285457611084,
-                0.49129390716552734,
-                0.4899061322212219,
-            ]
-        )
+        expected_slice = np.array([0.5114, 0.5706, 0.4772, 0.5028, 0.5637, 0.4732, 0.5169, 0.4881, 0.4977])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py b/tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py
index 01c2e22e4816..2a07ab64a36d 100644
--- a/tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py
+++ b/tests/pipelines/stable_diffusion/test_stable_diffusion_image_variation.py
@@ -119,7 +119,7 @@ def test_stable_diffusion_img_variation_default_case(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5167, 0.5746, 0.4835, 0.4914, 0.5605, 0.4691, 0.5201, 0.4898, 0.4958])
+        expected_slice = np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -139,7 +139,7 @@ def test_stable_diffusion_img_variation_multiple_images(self):
         image_slice = image[-1, -3:, -3:, -1]
 
         assert image.shape == (2, 64, 64, 3)
-        expected_slice = np.array([0.6568, 0.5470, 0.5684, 0.5444, 0.5945, 0.6221, 0.5508, 0.5531, 0.5263])
+        expected_slice = np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py b/tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py
index e27f83fc04fe..69b92f685f25 100644
--- a/tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py
+++ b/tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py
@@ -138,7 +138,7 @@ def test_stable_diffusion_img2img_default_case(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.4492, 0.3865, 0.4222, 0.5854, 0.5139, 0.4379, 0.4193, 0.48, 0.4218])
+        expected_slice = np.array([0.4555, 0.3216, 0.4049, 0.4620, 0.4618, 0.4126, 0.4122, 0.4629, 0.4579])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -157,7 +157,7 @@ def test_stable_diffusion_img2img_negative_prompt(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.4065, 0.3783, 0.4050, 0.5266, 0.4781, 0.4252, 0.4203, 0.4692, 0.4365])
+        expected_slice = np.array([0.4593, 0.3408, 0.4232, 0.4749, 0.4476, 0.4115, 0.4357, 0.4733, 0.4663])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -176,7 +176,7 @@ def test_stable_diffusion_img2img_multiple_init_images(self):
         image_slice = image[-1, -3:, -3:, -1]
 
         assert image.shape == (2, 32, 32, 3)
-        expected_slice = np.array([0.5144, 0.4447, 0.4735, 0.6676, 0.5526, 0.5454, 0.645, 0.5149, 0.4689])
+        expected_slice = np.array([0.4241, 0.5576, 0.5711, 0.4792, 0.4311, 0.5952, 0.5827, 0.5138, 0.5109])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -196,7 +196,7 @@ def test_stable_diffusion_img2img_k_lms(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.4367, 0.4986, 0.4372, 0.6706, 0.5665, 0.444, 0.5864, 0.6019, 0.5203])
+        expected_slice = np.array([0.4398, 0.4949, 0.4337, 0.6580, 0.5555, 0.4338, 0.5769, 0.5955, 0.5175])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py b/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py
index 25b0c6ea1432..78e697fbbac3 100644
--- a/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py
+++ b/tests/pipelines/stable_diffusion/test_stable_diffusion_instruction_pix2pix.py
@@ -124,7 +124,7 @@ def test_stable_diffusion_pix2pix_default_case(self):
         image = sd_pipe(**inputs).images
         image_slice = image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.7318, 0.3723, 0.4662, 0.623, 0.5770, 0.5014, 0.4281, 0.5550, 0.4813])
+        expected_slice = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -142,7 +142,7 @@ def test_stable_diffusion_pix2pix_negative_prompt(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.7323, 0.3688, 0.4611, 0.6255, 0.5746, 0.5017, 0.433, 0.5553, 0.4827])
+        expected_slice = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -165,7 +165,7 @@ def test_stable_diffusion_pix2pix_multiple_init_images(self):
         image_slice = image[-1, -3:, -3:, -1]
 
         assert image.shape == (2, 32, 32, 3)
-        expected_slice = np.array([0.606, 0.5712, 0.5099, 0.598, 0.5805, 0.7205, 0.6793, 0.554, 0.5607])
+        expected_slice = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -187,7 +187,7 @@ def test_stable_diffusion_pix2pix_euler(self):
         print(",".join([str(x) for x in slice]))
 
         assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.726, 0.3902, 0.4868, 0.585, 0.5672, 0.511, 0.3906, 0.551, 0.4846])
+        expected_slice = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion_model_editing.py b/tests/pipelines/stable_diffusion/test_stable_diffusion_model_editing.py
index 2d9b1e54ee6e..1e11500c72b1 100644
--- a/tests/pipelines/stable_diffusion/test_stable_diffusion_model_editing.py
+++ b/tests/pipelines/stable_diffusion/test_stable_diffusion_model_editing.py
@@ -118,9 +118,7 @@ def test_stable_diffusion_model_editing_default_case(self):
         image_slice = image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array(
-            [0.5217179, 0.50658035, 0.5003239, 0.41109088, 0.3595158, 0.46607107, 0.5323504, 0.5335255, 0.49187922]
-        )
+        expected_slice = np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -139,9 +137,7 @@ def test_stable_diffusion_model_editing_negative_prompt(self):
 
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array(
-            [0.546259, 0.5108156, 0.50897664, 0.41931948, 0.3748669, 0.4669299, 0.5427151, 0.54561913, 0.49353]
-        )
+        expected_slice = np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -161,9 +157,7 @@ def test_stable_diffusion_model_editing_euler(self):
 
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array(
-            [0.47106352, 0.53579676, 0.45798016, 0.514294, 0.56856745, 0.4788605, 0.54380214, 0.5046455, 0.50404465]
-        )
+        expected_slice = np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py b/tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py
index af26e19cca73..de9e8a79fb34 100644
--- a/tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py
+++ b/tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py
@@ -119,7 +119,7 @@ def test_stable_diffusion_panorama_default_case(self):
         image_slice = image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array([0.5101, 0.5006, 0.4962, 0.3995, 0.3501, 0.4632, 0.5339, 0.525, 0.4878])
+        expected_slice = np.array([0.4794, 0.5084, 0.4992, 0.3941, 0.3555, 0.4754, 0.5248, 0.5224, 0.4839])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -138,7 +138,7 @@ def test_stable_diffusion_panorama_negative_prompt(self):
 
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array([0.5326, 0.5009, 0.5074, 0.4133, 0.371, 0.464, 0.5432, 0.5429, 0.4896])
+        expected_slice = np.array([0.5029, 0.5075, 0.5002, 0.3965, 0.3584, 0.4746, 0.5271, 0.5273, 0.4877])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -158,9 +158,7 @@ def test_stable_diffusion_panorama_euler(self):
 
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array(
-            [0.48235387, 0.5423796, 0.46016198, 0.5377287, 0.5803722, 0.4876525, 0.5515428, 0.5045897, 0.50709957]
-        )
+        expected_slice = np.array([0.4934, 0.5455, 0.4847, 0.5022, 0.5572, 0.4833, 0.5207, 0.4952, 0.5051])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py b/tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py
index 46b93a0589ce..59c45d603b91 100644
--- a/tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py
+++ b/tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py
@@ -133,7 +133,7 @@ def test_stable_diffusion_pix2pix_zero_default_case(self):
         image = sd_pipe(**inputs).images
         image_slice = image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5184, 0.503, 0.4917, 0.4022, 0.3455, 0.464, 0.5324, 0.5323, 0.4894])
+        expected_slice = np.array([0.4863, 0.5053, 0.5033, 0.4007, 0.3571, 0.4768, 0.5176, 0.5277, 0.4940])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -151,7 +151,7 @@ def test_stable_diffusion_pix2pix_zero_negative_prompt(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5464, 0.5072, 0.5012, 0.4124, 0.3624, 0.466, 0.5413, 0.5468, 0.4927])
+        expected_slice = np.array([0.5177, 0.5097, 0.5047, 0.4076, 0.3667, 0.4767, 0.5238, 0.5307, 0.4958])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -170,7 +170,7 @@ def test_stable_diffusion_pix2pix_zero_euler(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5114, 0.5051, 0.5222, 0.5279, 0.5037, 0.5156, 0.4604, 0.4966, 0.504])
+        expected_slice = np.array([0.5421, 0.5525, 0.6085, 0.5279, 0.4658, 0.5317, 0.4418, 0.4815, 0.5132])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -187,7 +187,7 @@ def test_stable_diffusion_pix2pix_zero_ddpm(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5185, 0.5027, 0.492, 0.401, 0.3445, 0.464, 0.5321, 0.5327, 0.4892])
+        expected_slice = np.array([0.4861, 0.5053, 0.5038, 0.3994, 0.3562, 0.4768, 0.5172, 0.5280, 0.4938])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
diff --git a/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py b/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py
index fa3c3d628e4f..7b607c8fdd36 100644
--- a/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py
+++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py
@@ -134,7 +134,7 @@ def test_stable_diffusion_ddim(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5649, 0.6022, 0.4804, 0.5270, 0.5585, 0.4643, 0.5159, 0.4963, 0.4793])
+        expected_slice = np.array([0.5753, 0.6113, 0.5005, 0.5036, 0.5464, 0.4725, 0.4982, 0.4865, 0.4861])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -151,7 +151,7 @@ def test_stable_diffusion_pndm(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5099, 0.5677, 0.4671, 0.5128, 0.5697, 0.4676, 0.5277, 0.4964, 0.4946])
+        expected_slice = np.array([0.5121, 0.5714, 0.4827, 0.5057, 0.5646, 0.4766, 0.5189, 0.4895, 0.4990])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -168,7 +168,7 @@ def test_stable_diffusion_k_lms(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.4717, 0.5376, 0.4568, 0.5225, 0.5734, 0.4797, 0.5467, 0.5074, 0.5043])
+        expected_slice = np.array([0.4865, 0.5439, 0.4840, 0.4995, 0.5543, 0.4846, 0.5199, 0.4942, 0.5061])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -185,7 +185,7 @@ def test_stable_diffusion_k_euler_ancestral(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.4715, 0.5376, 0.4569, 0.5224, 0.5734, 0.4797, 0.5465, 0.5074, 0.5046])
+        expected_slice = np.array([0.4864, 0.5440, 0.4842, 0.4994, 0.5543, 0.4846, 0.5196, 0.4942, 0.5063])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -202,7 +202,7 @@ def test_stable_diffusion_k_euler(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.4717, 0.5376, 0.4568, 0.5225, 0.5734, 0.4797, 0.5467, 0.5074, 0.5043])
+        expected_slice = np.array([0.4865, 0.5439, 0.4840, 0.4995, 0.5543, 0.4846, 0.5199, 0.4942, 0.5061])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
diff --git a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py
index 780abf304a46..90bb1461d351 100644
--- a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py
+++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py
@@ -132,9 +132,7 @@ def test_inference(self):
         image_slice = image[0, -3:, -3:, -1]
 
         self.assertEqual(image.shape, (1, 64, 64, 3))
-        expected_slice = np.array(
-            [0.5644937, 0.60543084, 0.48239064, 0.5206757, 0.55623394, 0.46045133, 0.5100435, 0.48919064, 0.4759359]
-        )
+        expected_slice = np.array([0.5743, 0.6081, 0.4975, 0.5021, 0.5441, 0.4699, 0.4988, 0.4841, 0.4851])
         max_diff = np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(max_diff, 1e-3)
 
diff --git a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py
index c2ad239f6888..6b0205f3faeb 100644
--- a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py
+++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py
@@ -289,7 +289,7 @@ def test_stable_diffusion_depth2img_default_case(self):
         if torch_device == "mps":
             expected_slice = np.array([0.6071, 0.5035, 0.4378, 0.5776, 0.5753, 0.4316, 0.4513, 0.5263, 0.4546])
         else:
-            expected_slice = np.array([0.6312, 0.4984, 0.4154, 0.4788, 0.5535, 0.4599, 0.4017, 0.5359, 0.4716])
+            expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -308,9 +308,9 @@ def test_stable_diffusion_depth2img_negative_prompt(self):
 
         assert image.shape == (1, 32, 32, 3)
         if torch_device == "mps":
-            expected_slice = np.array([0.5825, 0.5135, 0.4095, 0.5452, 0.6059, 0.4211, 0.3994, 0.5177, 0.4335])
-        else:
             expected_slice = np.array([0.6296, 0.5125, 0.3890, 0.4456, 0.5955, 0.4621, 0.3810, 0.5310, 0.4626])
+        else:
+            expected_slice = np.array([0.6012, 0.4507, 0.3769, 0.4121, 0.5566, 0.4585, 0.3803, 0.5045, 0.4631])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -332,7 +332,7 @@ def test_stable_diffusion_depth2img_multiple_init_images(self):
         if torch_device == "mps":
             expected_slice = np.array([0.6501, 0.5150, 0.4939, 0.6688, 0.5437, 0.5758, 0.5115, 0.4406, 0.4551])
         else:
-            expected_slice = np.array([0.6267, 0.5232, 0.6001, 0.6738, 0.5029, 0.6429, 0.5364, 0.4159, 0.4674])
+            expected_slice = np.array([0.6557, 0.6214, 0.6254, 0.5775, 0.4785, 0.5949, 0.5904, 0.4785, 0.4730])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -351,7 +351,7 @@ def test_stable_diffusion_depth2img_pil(self):
         if torch_device == "mps":
             expected_slice = np.array([0.53232, 0.47015, 0.40868, 0.45651, 0.4891, 0.4668, 0.4287, 0.48822, 0.47439])
         else:
-            expected_slice = np.array([0.6312, 0.4984, 0.4154, 0.4788, 0.5535, 0.4599, 0.4017, 0.5359, 0.4716])
+            expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -397,7 +397,7 @@ def test_stable_diffusion_depth2img_pipeline_default(self):
         image_slice = image[0, 253:256, 253:256, -1].flatten()
 
         assert image.shape == (1, 480, 640, 3)
-        expected_slice = np.array([0.9057, 0.9365, 0.9258, 0.8937, 0.8555, 0.8541, 0.8260, 0.7747, 0.7421])
+        expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])
 
         assert np.abs(expected_slice - image_slice).max() < 1e-4
 
diff --git a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py
index b8e7b858130b..747809a4fb2e 100644
--- a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py
+++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_upscale.py
@@ -154,7 +154,7 @@ def test_stable_diffusion_upscale(self):
 
         expected_height_width = low_res_image.size[0] * 4
         assert image.shape == (1, expected_height_width, expected_height_width, 3)
-        expected_slice = np.array([0.2562, 0.3606, 0.4204, 0.4469, 0.4822, 0.4647, 0.5315, 0.5748, 0.5606])
+        expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py
index 8aab5845741c..083640a87ba9 100644
--- a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py
+++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py
@@ -144,7 +144,7 @@ def test_stable_diffusion_v_pred_ddim(self):
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.6424, 0.6109, 0.494, 0.5088, 0.4984, 0.4525, 0.5059, 0.5068, 0.4474])
+        expected_slice = np.array([0.6569, 0.6525, 0.5142, 0.4968, 0.4923, 0.4601, 0.4996, 0.5041, 0.4544])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@@ -193,7 +193,7 @@ def test_stable_diffusion_v_pred_k_euler(self):
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.4616, 0.5184, 0.4887, 0.5111, 0.4839, 0.48, 0.5119, 0.5263, 0.4776])
+        expected_slice = np.array([0.5644, 0.6514, 0.5190, 0.5663, 0.5287, 0.4953, 0.5430, 0.5243, 0.4778])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/tests/pipelines/stable_diffusion_safe/test_safe_diffusion.py b/tests/pipelines/stable_diffusion_safe/test_safe_diffusion.py
index 2f393a66d166..c614fa48055e 100644
--- a/tests/pipelines/stable_diffusion_safe/test_safe_diffusion.py
+++ b/tests/pipelines/stable_diffusion_safe/test_safe_diffusion.py
@@ -154,7 +154,7 @@ def test_safe_diffusion_ddim(self):
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5644, 0.6018, 0.4799, 0.5267, 0.5585, 0.4641, 0.516, 0.4964, 0.4792])
+        expected_slice = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@@ -200,7 +200,7 @@ def test_stable_diffusion_pndm(self):
         image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
 
         assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5095, 0.5674, 0.4668, 0.5126, 0.5697, 0.4675, 0.5278, 0.4964, 0.4945])
+        expected_slice = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py b/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py
index e1123123c61c..907853394040 100644
--- a/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py
+++ b/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py
@@ -47,6 +47,7 @@ def get_dummy_components(self):
 
         feature_extractor = CLIPImageProcessor(crop_size=32, size=32)
 
+        torch.manual_seed(0)
         image_encoder = CLIPVisionModelWithProjection(
             CLIPVisionConfig(
                 hidden_size=embedder_hidden_size,
@@ -119,16 +120,16 @@ def get_dummy_components(self):
         components = {
             # image encoding components
             "feature_extractor": feature_extractor,
-            "image_encoder": image_encoder,
+            "image_encoder": image_encoder.eval(),
             # image noising components
-            "image_normalizer": image_normalizer,
+            "image_normalizer": image_normalizer.eval(),
             "image_noising_scheduler": image_noising_scheduler,
             # regular denoising components
             "tokenizer": tokenizer,
-            "text_encoder": text_encoder,
-            "unet": unet,
+            "text_encoder": text_encoder.eval(),
+            "unet": unet.eval(),
             "scheduler": scheduler,
-            "vae": vae,
+            "vae": vae.eval(),
         }
 
         return components
@@ -169,9 +170,7 @@ def test_image_embeds_none(self):
         image_slice = image[0, -3:, -3:, -1]
 
         assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array(
-            [0.34588397, 0.7747054, 0.5453714, 0.5227859, 0.57656777, 0.6532228, 0.5177634, 0.49932978, 0.56626225]
-        )
+        expected_slice = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
diff --git a/tests/pipelines/text_to_video/test_text_to_video.py b/tests/pipelines/text_to_video/test_text_to_video.py
index e4331fda02ff..438e685a443c 100644
--- a/tests/pipelines/text_to_video/test_text_to_video.py
+++ b/tests/pipelines/text_to_video/test_text_to_video.py
@@ -135,7 +135,7 @@ def test_text_to_video_default_case(self):
         image_slice = frames[0][-3:, -3:, -1]
 
         assert frames[0].shape == (64, 64, 3)
-        expected_slice = np.array([166, 184, 167, 118, 102, 123, 108, 93, 114])
+        expected_slice = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
diff --git a/tests/pipelines/vq_diffusion/test_vq_diffusion.py b/tests/pipelines/vq_diffusion/test_vq_diffusion.py
index 6769240db905..d97a7b2f6564 100644
--- a/tests/pipelines/vq_diffusion/test_vq_diffusion.py
+++ b/tests/pipelines/vq_diffusion/test_vq_diffusion.py
@@ -143,7 +143,7 @@ def test_vq_diffusion(self):
 
         assert image.shape == (1, 24, 24, 3)
 
-        expected_slice = np.array([0.6583, 0.6410, 0.5325, 0.5635, 0.5563, 0.4234, 0.6008, 0.5491, 0.4880])
+        expected_slice = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@@ -187,7 +187,7 @@ def test_vq_diffusion_classifier_free_sampling(self):
 
         assert image.shape == (1, 24, 24, 3)
 
-        expected_slice = np.array([0.6647, 0.6531, 0.5303, 0.5891, 0.5726, 0.4439, 0.6304, 0.5564, 0.4912])
+        expected_slice = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
diff --git a/tests/test_layers_utils.py b/tests/test_layers_utils.py
index d0e2102b539e..1f6e445f9d61 100644
--- a/tests/test_layers_utils.py
+++ b/tests/test_layers_utils.py
@@ -411,10 +411,7 @@ def test_spatial_transformer_cross_attention_dim(self):
 
         assert attention_scores.shape == (1, 64, 64, 64)
         output_slice = attention_scores[0, -1, -3:, -3:]
-
-        expected_slice = torch.tensor(
-            [-0.2555, -0.8877, -2.4739, -2.2251, 1.2714, 0.0807, -0.4161, -1.6408, -0.0471], device=torch_device
-        )
+        expected_slice = torch.tensor([0.0143, -0.6909, -2.1547, -1.8893, 1.4097, 0.1359, -0.2521, -1.3359, 0.2598])
         assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)
 
     def test_spatial_transformer_timestep(self):
@@ -445,14 +442,12 @@ def test_spatial_transformer_timestep(self):
         output_slice_1 = attention_scores_1[0, -1, -3:, -3:]
         output_slice_2 = attention_scores_2[0, -1, -3:, -3:]
 
-        expected_slice_1 = torch.tensor(
-            [-0.1874, -0.9704, -1.4290, -1.3357, 1.5138, 0.3036, -0.0976, -1.1667, 0.1283], device=torch_device
-        )
+        expected_slice = torch.tensor([-0.3923, -1.0923, -1.7144, -1.5570, 1.4154, 0.1738, -0.1157, -1.2998, -0.1703])
         expected_slice_2 = torch.tensor(
-            [-0.3493, -1.0924, -1.6161, -1.5016, 1.4245, 0.1367, -0.2526, -1.3109, -0.0547], device=torch_device
+            [-0.4311, -1.1376, -1.7732, -1.5997, 1.3450, 0.0964, -0.1569, -1.3590, -0.2348]
         )
 
-        assert torch.allclose(output_slice_1.flatten(), expected_slice_1, atol=1e-3)
+        assert torch.allclose(output_slice_1.flatten(), expected_slice, atol=1e-3)
         assert torch.allclose(output_slice_2.flatten(), expected_slice_2, atol=1e-3)
 
     def test_spatial_transformer_dropout(self):
diff --git a/tests/test_unet_2d_blocks.py b/tests/test_unet_2d_blocks.py
index e560240422ac..4d658f282932 100644
--- a/tests/test_unet_2d_blocks.py
+++ b/tests/test_unet_2d_blocks.py
@@ -57,7 +57,7 @@ def prepare_init_args_and_inputs_for_common(self):
         return init_dict, inputs_dict
 
     def test_output(self):
-        expected_slice = [0.2440, -0.6953, -0.2140, -0.3874, 0.1966, 1.2077, 0.0441, -0.7718, 0.2800]
+        expected_slice = [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
         super().test_output(expected_slice)
 
 
@@ -175,7 +175,7 @@ def prepare_init_args_and_inputs_for_common(self):
         return init_dict, inputs_dict
 
     def test_output(self):
-        expected_slice = [0.1879, 2.2653, 0.5987, 1.1568, -0.8454, -1.6109, -0.8919, 0.8306, 1.6758]
+        expected_slice = [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
         super().test_output(expected_slice)
 
 
@@ -237,7 +237,7 @@ def prepare_init_args_and_inputs_for_common(self):
         return init_dict, inputs_dict
 
     def test_output(self):
-        expected_slice = [-0.2796, -0.4364, -0.1067, -0.2693, 0.1894, 0.3869, -0.3470, 0.4584, 0.5091]
+        expected_slice = [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
         super().test_output(expected_slice)