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fix: Reorder DenoiseMask socket fields

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blessedcoolant 2023-08-27 06:14:35 +12:00
parent 521da555d6
commit 249048aae7
1 changed files with 21 additions and 26 deletions
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47
invokeai/app/invocations/latent.py
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@ -8,27 +8,21 @@ import numpy as np
import torch import torch
import torchvision.transforms as T import torchvision.transforms as T
from diffusers.image_processor import VaeImageProcessor from diffusers.image_processor import VaeImageProcessor
from diffusers.models.attention_processor import ( from diffusers.models.attention_processor import (AttnProcessor2_0,
AttnProcessor2_0, LoRAAttnProcessor2_0,
LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor,
LoRAXFormersAttnProcessor, XFormersAttnProcessor)
XFormersAttnProcessor,
)
from diffusers.schedulers import DPMSolverSDEScheduler from diffusers.schedulers import DPMSolverSDEScheduler
from diffusers.schedulers import SchedulerMixin as Scheduler from diffusers.schedulers import SchedulerMixin as Scheduler
from pydantic import validator from pydantic import validator
from torchvision.transforms.functional import resize as tv_resize from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.metadata import CoreMetadata from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import ( from invokeai.app.invocations.primitives import (DenoiseMaskField,
DenoiseMaskField, DenoiseMaskOutput, ImageField,
DenoiseMaskOutput, ImageOutput, LatentsField,
ImageField, LatentsOutput,
ImageOutput, build_latents_output)
LatentsField,
LatentsOutput,
build_latents_output,
)
from invokeai.app.util.controlnet_utils import prepare_control_image from invokeai.app.util.controlnet_utils import prepare_control_image
from invokeai.app.util.step_callback import stable_diffusion_step_callback from invokeai.app.util.step_callback import stable_diffusion_step_callback
from invokeai.backend.model_management.models import ModelType, SilenceWarnings from invokeai.backend.model_management.models import ModelType, SilenceWarnings
@ -37,16 +31,16 @@ from ...backend.model_management.lora import ModelPatcher
from ...backend.model_management.models import BaseModelType from ...backend.model_management.models import BaseModelType
from ...backend.stable_diffusion import PipelineIntermediateState from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.stable_diffusion.diffusers_pipeline import ( from ...backend.stable_diffusion.diffusers_pipeline import (
ConditioningData, ConditioningData, ControlNetData, StableDiffusionGeneratorPipeline,
ControlNetData, image_resized_to_grid_as_tensor)
StableDiffusionGeneratorPipeline, from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
image_resized_to_grid_as_tensor, PostprocessingSettings
)
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import choose_precision, choose_torch_device from ...backend.util.devices import choose_precision, choose_torch_device
from ..models.image import ImageCategory, ResourceOrigin from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, UIType, tags, title from .baseinvocation import (BaseInvocation, FieldDescriptions, Input,
InputField, InvocationContext, UIType, tags,
title)
from .compel import ConditioningField from .compel import ConditioningField
from .controlnet_image_processors import ControlField from .controlnet_image_processors import ControlField
from .model import ModelInfo, UNetField, VaeField from .model import ModelInfo, UNetField, VaeField
@ -66,14 +60,15 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
type: Literal["create_denoise_mask"] = "create_denoise_mask" type: Literal["create_denoise_mask"] = "create_denoise_mask"
# Inputs # Inputs
image: Optional[ImageField] = InputField(default=None, description="Image which will be masked")
mask: ImageField = InputField(description="The mask to use when pasting")
vae: VaeField = InputField( vae: VaeField = InputField(
description=FieldDescriptions.vae, description=FieldDescriptions.vae,
input=Input.Connection, input=Input.Connection,
ui_order=0
) )
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled) image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32) mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32, ui_order=4)
def prep_mask_tensor(self, mask_image): def prep_mask_tensor(self, mask_image):
if mask_image.mode != "L": if mask_image.mode != "L":