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chore: Black lint fix

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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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2
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
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@ -265,7 +265,7 @@ class InvokeAICrossAttentionMixin:
if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096 if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
return self.einsum_lowest_level(q, k, v, None, None, None) return self.einsum_lowest_level(q, k, v, None, None, None)
else: 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) return self.einsum_op_slice_dim1(q, k, v, slice_size)
def einsum_op_mps_v2(self, q, k, v): def einsum_op_mps_v2(self, q, k, v):

20
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
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@ -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,

4
invokeai/backend/stable_diffusion/image_degradation/bsrgan.py
View File

@ -395,7 +395,7 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
D = np.diag(np.random.rand(3)) D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3)) U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U) conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0) img = np.clip(img, 0.0, 1.0)
return img return img
@ -413,7 +413,7 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
D = np.diag(np.random.rand(3)) D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3)) U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U) conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0) img = np.clip(img, 0.0, 1.0)
return img return img

4
invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py
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@ -399,7 +399,7 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
D = np.diag(np.random.rand(3)) D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3)) U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U) conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0) img = np.clip(img, 0.0, 1.0)
return img return img
@ -417,7 +417,7 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
D = np.diag(np.random.rand(3)) D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3)) U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U) conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32) img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0) img = np.clip(img, 0.0, 1.0)
return img return img

72
invokeai/backend/stable_diffusion/image_degradation/utils_image.py
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@ -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:
@ -728,11 +716,11 @@ def ssim(img1, img2):
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
mu1_sq = mu1 ** 2 mu1_sq = mu1**2
mu2_sq = mu2 ** 2 mu2_sq = mu2**2
mu1_mu2 = mu1 * mu2 mu1_mu2 = mu1 * mu2
sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
@ -749,8 +737,8 @@ def ssim(img1, img2):
# matlab 'imresize' function, now only support 'bicubic' # matlab 'imresize' function, now only support 'bicubic'
def cubic(x): def cubic(x):
absx = torch.abs(x) absx = torch.abs(x)
absx2 = absx ** 2 absx2 = absx**2
absx3 = absx ** 3 absx3 = absx**3
return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + ( return (1.5 * absx3 - 2.5 * absx2 + 1) * ((absx <= 1).type_as(absx)) + (
-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2 -0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2
) * (((absx > 1) * (absx <= 2)).type_as(absx)) ) * (((absx > 1) * (absx <= 2)).type_as(absx))

5
invokeai/backend/training/textual_inversion_training.py
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@ -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],
) )

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

2
invokeai/backend/util/util.py
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@ -207,7 +207,7 @@ def parallel_data_prefetch(
return gather_res 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]) delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1]) d = (shape[0] // res[0], shape[1] // res[1])