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fix baseinvocation call to __attribute__ to work with py3.9

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This commit is contained in:
Lincoln Stein 2023-08-31 23:11:54 -04:00
parent a74e2108bb
commit 2cb57ef301
9 changed files with 66 additions and 55 deletions
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10
invokeai/app/invocations/baseinvocation.py
View File

@ -615,7 +615,15 @@ def invocation_output(
config=cls.__config__,
)
cls.__fields__.update({"type": output_type_field})
cls.__annotations__.update({"type": output_type_annotation})
# to support 3.9, 3.10 and 3.11, as described in https://docs.python.org/3/howto/annotations.html
annotations = (
cls.__dict__.get("__annotations__", None)
if isinstance(cls, type)
else getattr(cls, "__annotations__", None)
)
if annotations:
annotations.update({"type": output_type_annotation})
return cls

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

@ -215,7 +215,10 @@ 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,
)
@ -277,7 +280,10 @@ 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,
@ -285,7 +291,10 @@ 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,
@ -293,7 +302,10 @@ 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,

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

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

@ -475,7 +475,10 @@ 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],
)