Fix Consistency Models UNet2DMidBlock2D Attention GroupNorm Bug (hugg…

…ingface#4863)

* Add attn_groups argument to UNet2DMidBlock2D to control theinternal Attention block's GroupNorm.

* Add docstring for attn_norm_num_groups in UNet2DModel.

* Since the test UNet config uses resnet_time_scale_shift == 'scale_shift', also set attn_norm_num_groups to 32.

* Add test for attn_norm_num_groups to UNet2DModelTests.

* Fix expected slices for slow tests.

* Also fix tolerances for slow tests.

---------

Co-authored-by: Sayak Paul <[email protected]>

scripts/convert_consistency_to_diffusers.py

@@ -27,6 +27,7 @@
         "ResnetUpsampleBlock2D",
     ],
     "resnet_time_scale_shift": "scale_shift",
+    "attn_norm_num_groups": 32,
     "upsample_type": "resnet",
     "downsample_type": "resnet",
 }
@@ -52,6 +53,7 @@
         "ResnetUpsampleBlock2D",
     ],
     "resnet_time_scale_shift": "scale_shift",
+    "attn_norm_num_groups": 32,
     "upsample_type": "resnet",
     "downsample_type": "resnet",
 }

src/diffusers/models/unet_2d.py

@@ -74,6 +74,10 @@ class UNet2DModel(ModelMixin, ConfigMixin):
         act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
         attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
         norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization.
+        attn_norm_num_groups (`int`, *optional*, defaults to `None`):
+            If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the
+            given number of groups. If left as `None`, the group norm layer will only be created if
+            `resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups.
         norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization.
         resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
             for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
@@ -107,6 +111,7 @@ def __init__(
         act_fn: str = "silu",
         attention_head_dim: Optional[int] = 8,
         norm_num_groups: int = 32,
+        attn_norm_num_groups: Optional[int] = None,
         norm_eps: float = 1e-5,
         resnet_time_scale_shift: str = "default",
         add_attention: bool = True,
@@ -192,6 +197,7 @@ def __init__(
             resnet_time_scale_shift=resnet_time_scale_shift,
             attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
             resnet_groups=norm_num_groups,
+            attn_groups=attn_norm_num_groups,
             add_attention=add_attention,
         )
 

src/diffusers/models/unet_2d_blocks.py

@@ -485,6 +485,7 @@ def __init__(
         resnet_time_scale_shift: str = "default",  # default, spatial
         resnet_act_fn: str = "swish",
         resnet_groups: int = 32,
+        attn_groups: Optional[int] = None,
         resnet_pre_norm: bool = True,
         add_attention: bool = True,
         attention_head_dim=1,
@@ -494,6 +495,9 @@ def __init__(
         resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
         self.add_attention = add_attention
 
+        if attn_groups is None:
+            attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None
+
         # there is always at least one resnet
         resnets = [
             ResnetBlock2D(
@@ -526,7 +530,7 @@ def __init__(
                         dim_head=attention_head_dim,
                         rescale_output_factor=output_scale_factor,
                         eps=resnet_eps,
-                        norm_num_groups=resnet_groups if resnet_time_scale_shift == "default" else None,
+                        norm_num_groups=attn_groups,
                         spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None,
                         residual_connection=True,
                         bias=True,

tests/models/test_models_unet_2d.py

@@ -74,6 +74,36 @@ def prepare_init_args_and_inputs_for_common(self):
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict
 
+    def test_mid_block_attn_groups(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["norm_num_groups"] = 16
+        init_dict["add_attention"] = True
+        init_dict["attn_norm_num_groups"] = 8
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        self.assertIsNotNone(
+            model.mid_block.attentions[0].group_norm, "Mid block Attention group norm should exist but does not."
+        )
+        self.assertEqual(
+            model.mid_block.attentions[0].group_norm.num_groups,
+            init_dict["attn_norm_num_groups"],
+            "Mid block Attention group norm does not have the expected number of groups.",
+        )
+
+        with torch.no_grad():
+            output = model(**inputs_dict)
+
+            if isinstance(output, dict):
+                output = output.to_tuple()[0]
+
+        self.assertIsNotNone(output)
+        expected_shape = inputs_dict["sample"].shape
+        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
+
 
 class UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
     model_class = UNet2DModel

tests/pipelines/consistency_models/test_consistency_models.py

@@ -216,9 +216,9 @@ def test_consistency_model_cd_multistep(self):
 
         image_slice = image[0, -3:, -3:, -1]
 
-        expected_slice = np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254])
+        expected_slice = np.array([0.0146, 0.0158, 0.0092, 0.0086, 0.0000, 0.0000, 0.0000, 0.0000, 0.0058])
 
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
     def test_consistency_model_cd_onestep(self):
         unet = UNet2DModel.from_pretrained("diffusers/consistency_models", subfolder="diffusers_cd_imagenet64_l2")
@@ -239,9 +239,9 @@ def test_consistency_model_cd_onestep(self):
 
         image_slice = image[0, -3:, -3:, -1]
 
-        expected_slice = np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217])
+        expected_slice = np.array([0.0059, 0.0003, 0.0000, 0.0023, 0.0052, 0.0007, 0.0165, 0.0081, 0.0095])
 
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
     @require_torch_2
     def test_consistency_model_cd_multistep_flash_attn(self):
@@ -263,7 +263,7 @@ def test_consistency_model_cd_multistep_flash_attn(self):
 
         image_slice = image[0, -3:, -3:, -1]
 
-        expected_slice = np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353])
+        expected_slice = np.array([0.1845, 0.1371, 0.1211, 0.2035, 0.1954, 0.1323, 0.1773, 0.1593, 0.1314])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
@@ -289,6 +289,6 @@ def test_consistency_model_cd_onestep_flash_attn(self):
 
         image_slice = image[0, -3:, -3:, -1]
 
-        expected_slice = np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095])
+        expected_slice = np.array([0.1623, 0.2009, 0.2387, 0.1731, 0.1168, 0.1202, 0.2031, 0.1327, 0.2447])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3