chore: linter fixes

invokeai/app/invocations/latent.py

@@ -15,10 +15,12 @@ from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models.adapter import T2IAdapter
-from diffusers.models.attention_processor import (AttnProcessor2_0,
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
     LoRAAttnProcessor2_0,
     LoRAXFormersAttnProcessor,
-                                                  XFormersAttnProcessor)
+    XFormersAttnProcessor,
+)
 from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.schedulers import DPMSolverSDEScheduler
 from diffusers.schedulers import SchedulerMixin as Scheduler
@@ -27,17 +29,22 @@ from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPVisionModelWithProjection
 
-from invokeai.app.invocations.constants import (LATENT_SCALE_FACTOR,
+from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
-                                                SCHEDULER_NAME_VALUES)
+from invokeai.app.invocations.fields import (
-from invokeai.app.invocations.fields import (ConditioningField,
+    ConditioningField,
     DenoiseMaskField,
-                                             FieldDescriptions, ImageField,
+    FieldDescriptions,
-                                             Input, InputField, LatentsField,
+    ImageField,
-                                             OutputField, UIType, WithBoard,
+    Input,
-                                             WithMetadata)
+    InputField,
+    LatentsField,
+    OutputField,
+    UIType,
+    WithBoard,
+    WithMetadata,
+)
 from invokeai.app.invocations.ip_adapter import IPAdapterField
-from invokeai.app.invocations.primitives import (DenoiseMaskOutput,
+from invokeai.app.invocations.primitives import DenoiseMaskOutput, ImageOutput, LatentsOutput
-                                                 ImageOutput, LatentsOutput)
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
@@ -45,21 +52,28 @@ from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, LoadedModel
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.stable_diffusion import (PipelineIntermediateState,
+from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
-                                               set_seamless)
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
-    BasicConditioningInfo, IPAdapterConditioningInfo, IPAdapterData, Range,
+    BasicConditioningInfo,
-    SDXLConditioningInfo, TextConditioningData, TextConditioningRegions)
+    IPAdapterConditioningInfo,
+    IPAdapterData,
+    Range,
+    SDXLConditioningInfo,
+    TextConditioningData,
+    TextConditioningRegions,
+)
 from invokeai.backend.util.mask import to_standard_float_mask
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 from ...backend.stable_diffusion.diffusers_pipeline import (
-    ControlNetData, StableDiffusionGeneratorPipeline, T2IAdapterData,
+    ControlNetData,
-    image_resized_to_grid_as_tensor)
+    StableDiffusionGeneratorPipeline,
+    T2IAdapterData,
+    image_resized_to_grid_as_tensor,
+)
 from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
 from ...backend.util.devices import choose_precision, choose_torch_device
-from .baseinvocation import (BaseInvocation, BaseInvocationOutput, invocation,
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-                             invocation_output)
 from .controlnet_image_processors import ControlField
 from .model import ModelIdentifierField, UNetField, VAEField
 

invokeai/app/invocations/mask.py

@@ -24,9 +24,9 @@ class RectangleMaskInvocation(BaseInvocation, WithMetadata):
 
     def invoke(self, context: InvocationContext) -> MaskOutput:
         mask = torch.zeros((1, self.height, self.width), dtype=torch.bool)
-        mask[
+        mask[:, self.y_top : self.y_top + self.rectangle_height, self.x_left : self.x_left + self.rectangle_width] = (
-            :, self.y_top : self.y_top + self.rectangle_height, self.x_left : self.x_left + self.rectangle_width
+            True
-        ] = True
+        )
 
         mask_tensor_name = context.tensors.save(mask)
         return MaskOutput(

invokeai/app/services/shared/sqlite_migrator/sqlite_migrator_common.py

@@ -17,8 +17,7 @@ class MigrateCallback(Protocol):
     See :class:`Migration` for an example.
     """
 
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
+    def __call__(self, cursor: sqlite3.Cursor) -> None: ...
-        ...
 
 
 class MigrationError(RuntimeError):

invokeai/backend/stable_diffusion/diffusion/custom_atttention.py

@@ -156,7 +156,6 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
                     # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
 
                     if self._ip_adapter_attention_weights["skip"]:
-
                         ip_key = ipa_weights.to_k_ip(ip_hidden_states)
                         ip_value = ipa_weights.to_v_ip(ip_hidden_states)
 

invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py

@@ -33,10 +33,8 @@ class UNetAttentionPatcher:
                 # "attn1" processors do not use IP-Adapters.
                 attn_procs[name] = CustomAttnProcessor2_0()
             else:
-
                 ip_adapter_attention_weights: IPAdapterAttentionWeights = {"ip_adapter_weights": [], "skip": False}
                 for ip_adapter in self._ip_adapters:
-
                     ip_adapter_weight = ip_adapter["ip_adapter"].attn_weights.get_attention_processor_weights(idx)
                     skip = False
                     for block in ip_adapter["target_blocks"]: