add note about discriminated union and Body() issue; blackified

invokeai/app/invocations/latent.py

@@ -755,7 +755,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     denoising_end=self.denoising_end,
                 )
 
-                (result_latents, result_attention_map_saver,) = pipeline.latents_from_embeddings(
+                (
+                    result_latents,
+                    result_attention_map_saver,
+                ) = pipeline.latents_from_embeddings(
                     latents=latents,
                     timesteps=timesteps,
                     init_timestep=init_timestep,

invokeai/backend/model_manager/config.py

@@ -268,7 +268,14 @@ AnyModelConfig = Union[
     T2IConfig,
 ]
 
-# Preferred alternative is a discriminated Union, but it breaks FastAPI when applied to a route.
+AnyModelConfigValidator = TypeAdapter(AnyModelConfig)
+
+# IMPLEMENTATION NOTE:
+# The preferred alternative to the above is a discriminated Union as shown
+# below. However, it breaks FastAPI when used as the input Body parameter in a route.
+# This is a known issue. Please see:
+#   https://github.com/tiangolo/fastapi/discussions/9761 and
+#   https://github.com/tiangolo/fastapi/discussions/9287
 # AnyModelConfig = Annotated[
 #     Union[
 #         _MainModelConfig,
@@ -284,8 +291,6 @@ AnyModelConfig = Union[
 #     Field(discriminator="type"),
 # ]
 
-AnyModelConfigValidator = TypeAdapter(AnyModelConfig)
-
 
 class ModelConfigFactory(object):
     """Class for parsing config dicts into StableDiffusion Config obects."""

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -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,
@@ -245,14 +251,20 @@ 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,
                 **kwargs,
             )
         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/training/textual_inversion_training.py

@@ -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],
             )