unet check length inputs

src/diffusers/models/unet_2d.py

@@ -94,7 +94,7 @@ def __init__(
         mid_block_scale_factor: float = 1,
         downsample_padding: int = 1,
         act_fn: str = "silu",
-        attention_head_dim: int = 8,
+        attention_head_dim: Optional[int] = 8,
         norm_num_groups: int = 32,
         norm_eps: float = 1e-5,
         resnet_time_scale_shift: str = "default",
@@ -107,6 +107,17 @@ def __init__(
         self.sample_size = sample_size
         time_embed_dim = block_out_channels[0] * 4
 
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
         # input
         self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
 

src/diffusers/models/unet_2d_condition.py

@@ -150,6 +150,27 @@ def __init__(
 
         self.sample_size = sample_size
 
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
         # input
         conv_in_padding = (conv_in_kernel - 1) // 2
         self.conv_in = nn.Conv2d(

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -236,6 +236,31 @@ def __init__(
 
         self.sample_size = sample_size
 
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                "Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`:"
+                f" {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                "Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`:"
+                f" {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                "Must provide the same number of `only_cross_attention` as `down_block_types`."
+                f" `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                "Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`:"
+                f" {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
         # input
         conv_in_padding = (conv_in_kernel - 1) // 2
         self.conv_in = LinearMultiDim(

tests/pipelines/stable_diffusion_2/test_stable_diffusion.py

@@ -56,7 +56,7 @@ def get_dummy_components(self):
             up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
             cross_attention_dim=32,
             # SD2-specific config below
-            attention_head_dim=(2, 4, 8, 8),
+            attention_head_dim=(2, 4),
             use_linear_projection=True,
         )
         scheduler = DDIMScheduler(

tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py

@@ -65,7 +65,7 @@ def get_dummy_components(self):
             down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
             up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
             cross_attention_dim=32,
-            attention_head_dim=(2, 4, 8, 8),
+            attention_head_dim=(2, 4),
             use_linear_projection=True,
         )
         scheduler = PNDMScheduler(skip_prk_steps=True)
@@ -288,7 +288,7 @@ 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([0.6374, 0.5039, 0.4199, 0.4819, 0.5563, 0.4617, 0.4028, 0.5381, 0.4711])
+            expected_slice = np.array([0.6312, 0.4984, 0.4154, 0.4788, 0.5535, 0.4599, 0.4017, 0.5359, 0.4716])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -309,7 +309,7 @@ def test_stable_diffusion_depth2img_negative_prompt(self):
         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([0.6332, 0.5167, 0.3911, 0.4446, 0.5971, 0.4619, 0.3821, 0.5323, 0.4621])
+            expected_slice = np.array([0.6296, 0.5125, 0.3890, 0.4456, 0.5955, 0.4621, 0.3810, 0.5310, 0.4626])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -331,7 +331,7 @@ 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([0.6248, 0.5206, 0.6007, 0.6749, 0.5022, 0.6442, 0.5352, 0.4140, 0.4681])
+            expected_slice = np.array([0.6267, 0.5232, 0.6001, 0.6738, 0.5029, 0.6429, 0.5364, 0.4159, 0.4674])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -386,7 +386,7 @@ 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([0.6374, 0.5039, 0.4199, 0.4819, 0.5563, 0.4617, 0.4028, 0.5381, 0.4711])
+            expected_slice = np.array([0.6312, 0.4984, 0.4154, 0.4788, 0.5535, 0.4599, 0.4017, 0.5359, 0.4716])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 

tests/pipelines/stable_diffusion_2/test_stable_diffusion_inpaint.py

@@ -47,7 +47,7 @@ def get_dummy_components(self):
             up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
             cross_attention_dim=32,
             # SD2-specific config below
-            attention_head_dim=(2, 4, 8, 8),
+            attention_head_dim=(2, 4),
             use_linear_projection=True,
         )
         scheduler = PNDMScheduler(skip_prk_steps=True)

tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py

@@ -56,7 +56,7 @@ def dummy_cond_unet(self):
             up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
             cross_attention_dim=32,
             # SD2-specific config below
-            attention_head_dim=(2, 4, 8, 8),
+            attention_head_dim=(2, 4),
             use_linear_projection=True,
         )
         return model