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chore: Black linting
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@ -8,21 +8,27 @@ import numpy as np
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import torch
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import torchvision.transforms as T
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.models.attention_processor import (AttnProcessor2_0,
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LoRAAttnProcessor2_0,
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LoRAXFormersAttnProcessor,
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XFormersAttnProcessor)
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from diffusers.models.attention_processor import (
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AttnProcessor2_0,
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LoRAAttnProcessor2_0,
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LoRAXFormersAttnProcessor,
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XFormersAttnProcessor,
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)
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from diffusers.schedulers import DPMSolverSDEScheduler
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from diffusers.schedulers import SchedulerMixin as Scheduler
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from pydantic import validator
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from torchvision.transforms.functional import resize as tv_resize
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from invokeai.app.invocations.metadata import CoreMetadata
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from invokeai.app.invocations.primitives import (DenoiseMaskField,
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DenoiseMaskOutput, ImageField,
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ImageOutput, LatentsField,
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LatentsOutput,
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build_latents_output)
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from invokeai.app.invocations.primitives import (
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DenoiseMaskField,
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DenoiseMaskOutput,
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ImageField,
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ImageOutput,
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LatentsField,
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LatentsOutput,
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build_latents_output,
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)
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from invokeai.app.util.controlnet_utils import prepare_control_image
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from invokeai.app.util.step_callback import stable_diffusion_step_callback
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from invokeai.backend.model_management.models import ModelType, SilenceWarnings
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@ -31,16 +37,16 @@ from ...backend.model_management.lora import ModelPatcher
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from ...backend.model_management.models import BaseModelType
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from ...backend.stable_diffusion import PipelineIntermediateState
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from ...backend.stable_diffusion.diffusers_pipeline import (
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ConditioningData, ControlNetData, StableDiffusionGeneratorPipeline,
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image_resized_to_grid_as_tensor)
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from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
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PostprocessingSettings
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ConditioningData,
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ControlNetData,
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StableDiffusionGeneratorPipeline,
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image_resized_to_grid_as_tensor,
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)
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from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
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from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
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from ...backend.util.devices import choose_precision, choose_torch_device
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from ..models.image import ImageCategory, ResourceOrigin
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from .baseinvocation import (BaseInvocation, FieldDescriptions, Input,
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InputField, InvocationContext, UIType, tags,
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title)
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from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, UIType, tags, title
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from .compel import ConditioningField
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from .controlnet_image_processors import ControlField
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from .model import ModelInfo, UNetField, VaeField
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@ -60,11 +66,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
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type: Literal["create_denoise_mask"] = "create_denoise_mask"
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# Inputs
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vae: VaeField = InputField(
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description=FieldDescriptions.vae,
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input=Input.Connection,
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ui_order=0
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)
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vae: VaeField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
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image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
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mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
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tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
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