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chore: Black lint fix

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This commit is contained in:
blessedcoolant 2023-09-01 09:19:10 +12:00
parent 54cda8ea42
commit b94ec14853
8 changed files with 57 additions and 54 deletions
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20
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -215,7 +215,10 @@ class InvokeAIDiffuserComponent:
dim=0, 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.unconditioned_embeddings.embeds,
conditioning_data.text_embeddings.embeds, conditioning_data.text_embeddings.embeds,
) )
@ -277,7 +280,10 @@ class InvokeAIDiffuserComponent:
wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0 wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
if wants_cross_attention_control: 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, sample,
timestep, timestep,
conditioning_data, conditioning_data,
@ -285,7 +291,10 @@ class InvokeAIDiffuserComponent:
**kwargs, **kwargs,
) )
elif self.sequential_guidance: 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, sample,
timestep, timestep,
conditioning_data, conditioning_data,
@ -293,7 +302,10 @@ class InvokeAIDiffuserComponent:
) )
else: else:
(unconditioned_next_x, conditioned_next_x,) = self._apply_standard_conditioning( (
unconditioned_next_x,
conditioned_next_x,
) = self._apply_standard_conditioning(
sample, sample,
timestep, timestep,
conditioning_data, conditioning_data,

24
invokeai/backend/stable_diffusion/image_degradation/utils_image.py
View File

@ -562,18 +562,14 @@ def rgb2ycbcr(img, only_y=True):
if only_y: if only_y:
rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0 rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
else: else:
rlt = ( rlt = np.matmul(
np.matmul(
img, img,
[ [
[65.481, -37.797, 112.0], [65.481, -37.797, 112.0],
[128.553, -74.203, -93.786], [128.553, -74.203, -93.786],
[24.966, 112.0, -18.214], [24.966, 112.0, -18.214],
], ],
) ) / 255.0 + [16, 128, 128]
/ 255.0
+ [16, 128, 128]
)
if in_img_type == np.uint8: if in_img_type == np.uint8:
rlt = rlt.round() rlt = rlt.round()
else: else:
@ -592,18 +588,14 @@ def ycbcr2rgb(img):
if in_img_type != np.uint8: if in_img_type != np.uint8:
img *= 255.0 img *= 255.0
# convert # convert
rlt = ( rlt = np.matmul(
np.matmul(
img, img,
[ [
[0.00456621, 0.00456621, 0.00456621], [0.00456621, 0.00456621, 0.00456621],
[0, -0.00153632, 0.00791071], [0, -0.00153632, 0.00791071],
[0.00625893, -0.00318811, 0], [0.00625893, -0.00318811, 0],
], ],
) ) * 255.0 + [-222.921, 135.576, -276.836]
* 255.0
+ [-222.921, 135.576, -276.836]
)
if in_img_type == np.uint8: if in_img_type == np.uint8:
rlt = rlt.round() rlt = rlt.round()
else: else:
@ -626,18 +618,14 @@ def bgr2ycbcr(img, only_y=True):
if only_y: if only_y:
rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0 rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
else: else:
rlt = ( rlt = np.matmul(
np.matmul(
img, img,
[ [
[24.966, 112.0, -18.214], [24.966, 112.0, -18.214],
[128.553, -74.203, -93.786], [128.553, -74.203, -93.786],
[65.481, -37.797, 112.0], [65.481, -37.797, 112.0],
], ],
) ) / 255.0 + [16, 128, 128]
/ 255.0
+ [16, 128, 128]
)
if in_img_type == np.uint8: if in_img_type == np.uint8:
rlt = rlt.round() rlt = rlt.round()
else: else:

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

@ -475,7 +475,10 @@ class TextualInversionDataset(Dataset):
if self.center_crop: if self.center_crop:
crop = min(img.shape[0], img.shape[1]) crop = min(img.shape[0], img.shape[1])
(h, w,) = ( (
h,
w,
) = (
img.shape[0], img.shape[0],
img.shape[1], img.shape[1],
) )