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run correct version of black

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This commit is contained in:
Lincoln Stein 2023-09-25 19:27:22 -04:00 committed by Kent Keirsey
parent d59e534cad
commit 3fd27b1aa9
7 changed files with 27 additions and 12 deletions
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4
invokeai/app/invocations/math.py
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@ -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":

4
invokeai/backend/ip_adapter/resampler.py
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@ -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)

2
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
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@ -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):

20
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -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,

5
invokeai/backend/training/textual_inversion_training.py
View File

@ -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],
)

2
invokeai/backend/util/mps_fixes.py
View File

@ -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))

2
invokeai/backend/util/util.py
View File

@ -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])