fix baseinvocation call to __attribute__ to work with py3.9

invokeai/app/invocations/baseinvocation.py

@@ -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
 

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -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,

invokeai/backend/stable_diffusion/image_degradation/utils_image.py

@@ -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 = (
+        rlt = np.matmul(
-            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 = (
+    rlt = np.matmul(
-        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 = (
+        rlt = np.matmul(
-            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:

invokeai/backend/training/textual_inversion_training.py

@@ -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],
             )