diff --git a/src/diffusers/models/unet_1d.py b/src/diffusers/models/unet_1d.py
index 7d12c81395..29d1d707f5 100644
--- a/src/diffusers/models/unet_1d.py
+++ b/src/diffusers/models/unet_1d.py
@@ -57,11 +57,11 @@ class UNet1DModel(ModelMixin, ConfigMixin):
             obj:`("UpBlock1D", "UpBlock1DNoSkip", "AttnUpBlock1D")`): Tuple of upsample block types.
         block_out_channels (`Tuple[int]`, *optional*, defaults to :
             obj:`(32, 32, 64)`): Tuple of block output channels.
-        layers_per_block (`int`, *optional*, defaults to 1): added number of layers in a UNet block.
         mid_block_type (`str`, *optional*, defaults to "UNetMidBlock1D"): block type for middle of UNet.
         out_block_type (`str`, *optional*, defaults to `None`): optional output processing of UNet.
         act_fn (`str`, *optional*, defaults to None): optional activitation function in UNet blocks.
         norm_num_groups (`int`, *optional*, defaults to 8): group norm member count in UNet blocks.
+        layers_per_block (`int`, *optional*, defaults to 1): added number of layers in a UNet block.
         downsample_each_block (`int`, *optional*, defaults to False:
             experimental feature for using a UNet without upsampling.
     """
@@ -83,9 +83,9 @@ class UNet1DModel(ModelMixin, ConfigMixin):
         mid_block_type: Tuple[str] = "UNetMidBlock1D",
         out_block_type: str = None,
         block_out_channels: Tuple[int] = (32, 32, 64),
-        layers_per_block: int = 1,
         act_fn: str = None,
         norm_num_groups: int = 8,
+        layers_per_block: int = 1,
         downsample_each_block: bool = False,
     ):
         super().__init__()
diff --git a/tests/pipelines/dance_diffusion/test_dance_diffusion.py b/tests/pipelines/dance_diffusion/test_dance_diffusion.py
index c38a2e3608..1779279ba6 100644
--- a/tests/pipelines/dance_diffusion/test_dance_diffusion.py
+++ b/tests/pipelines/dance_diffusion/test_dance_diffusion.py
@@ -118,5 +118,5 @@ class PipelineIntegrationTests(unittest.TestCase):
         audio_slice = audio[0, -3:, -3:]
 
         assert audio.shape == (1, 2, pipe.unet.sample_size)
-        expected_slice = np.array([-0.1693, -0.1698, -0.1447, -0.3044, -0.3203, -0.2937])
+        expected_slice = np.array([-0.1576, -0.1526, -0.127, -0.2699, -0.2762, -0.2487])
         assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2