run correct version of black

invokeai/app/invocations/math.py

@@ -199,7 +199,7 @@ class IntegerMathInvocation(BaseInvocation):
         elif self.operation == "DIV":
             return IntegerOutput(value=int(self.a / self.b))
         elif self.operation == "EXP":
-            return IntegerOutput(value=self.a ** self.b)
+            return IntegerOutput(value=self.a**self.b)
         elif self.operation == "MOD":
             return IntegerOutput(value=self.a % self.b)
         elif self.operation == "ABS":
@@ -273,7 +273,7 @@ class FloatMathInvocation(BaseInvocation):
         elif self.operation == "DIV":
             return FloatOutput(value=self.a / self.b)
         elif self.operation == "EXP":
-            return FloatOutput(value=self.a ** self.b)
+            return FloatOutput(value=self.a**self.b)
         elif self.operation == "SQRT":
             return FloatOutput(value=np.sqrt(self.a))
         elif self.operation == "ABS":

invokeai/backend/ip_adapter/resampler.py

@@ -33,7 +33,7 @@ def reshape_tensor(x, heads):
 class PerceiverAttention(nn.Module):
     def __init__(self, *, dim, dim_head=64, heads=8):
         super().__init__()
-        self.scale = dim_head ** -0.5
+        self.scale = dim_head**-0.5
         self.dim_head = dim_head
         self.heads = heads
         inner_dim = dim_head * heads
@@ -91,7 +91,7 @@ class Resampler(nn.Module):
     ):
         super().__init__()
 
-        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5)
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
 
         self.proj_in = nn.Linear(embedding_dim, dim)
 

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -261,7 +261,7 @@ class InvokeAICrossAttentionMixin:
         if q.shape[1] <= 4096:  # (512x512) max q.shape[1]: 4096
             return self.einsum_lowest_level(q, k, v, None, None, None)
         else:
-            slice_size = math.floor(2 ** 30 / (q.shape[0] * q.shape[1]))
+            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
             return self.einsum_op_slice_dim1(q, k, v, slice_size)
 
     def einsum_op_mps_v2(self, q, k, v):

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -175,7 +175,10 @@ class InvokeAIDiffuserComponent:
                                 dim=0,
                             ),
                         }
-                    (encoder_hidden_states, encoder_attention_mask,) = self._concat_conditionings_for_batch(
+                    (
+                        encoder_hidden_states,
+                        encoder_attention_mask,
+                    ) = self._concat_conditionings_for_batch(
                         conditioning_data.unconditioned_embeddings.embeds,
                         conditioning_data.text_embeddings.embeds,
                     )
@@ -237,7 +240,10 @@ class InvokeAIDiffuserComponent:
         wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
 
         if wants_cross_attention_control:
-            (unconditioned_next_x, conditioned_next_x,) = self._apply_cross_attention_controlled_conditioning(
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_cross_attention_controlled_conditioning(
                 sample,
                 timestep,
                 conditioning_data,
@@ -245,7 +251,10 @@ class InvokeAIDiffuserComponent:
                 **kwargs,
             )
         elif self.sequential_guidance:
-            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning_sequentially(
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_standard_conditioning_sequentially(
                 sample,
                 timestep,
                 conditioning_data,
@@ -253,7 +262,10 @@ class InvokeAIDiffuserComponent:
             )
 
         else:
-            (unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning(
+            (
+                unconditioned_next_x,
+                conditioned_next_x,
+            ) = self._apply_standard_conditioning(
                 sample,
                 timestep,
                 conditioning_data,

invokeai/backend/training/textual_inversion_training.py

@@ -470,7 +470,10 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            (h, w,) = (
+            (
+                h,
+                w,
+            ) = (
                 img.shape[0],
                 img.shape[1],
             )

invokeai/backend/util/mps_fixes.py

@@ -203,7 +203,7 @@ class ChunkedSlicedAttnProcessor:
         if attn.upcast_attention:
             out_item_size = 4
 
-        chunk_size = 2 ** 29
+        chunk_size = 2**29
 
         out_size = query.shape[1] * key.shape[1] * out_item_size
         chunks_count = min(query.shape[1], math.ceil((out_size - 1) / chunk_size))

invokeai/backend/util/util.py

@@ -207,7 +207,7 @@ def parallel_data_prefetch(
         return gather_res
 
 
-def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t ** 5 - 15 * t ** 4 + 10 * t ** 3):
+def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3):
     delta = (res[0] / shape[0], res[1] / shape[1])
     d = (shape[0] // res[0], shape[1] // res[1])