mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
29b04b7e83
Bump all nodes in prep for v4.0.0.
98 lines
4.3 KiB
Python
98 lines
4.3 KiB
Python
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.model_manager import SubModelType
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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invocation,
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invocation_output,
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)
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from .model import CLIPField, ModelIdentifierField, UNetField, VAEField
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@invocation_output("sdxl_model_loader_output")
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class SDXLModelLoaderOutput(BaseInvocationOutput):
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"""SDXL base model loader output"""
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unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
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clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 1")
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clip2: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
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vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
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@invocation_output("sdxl_refiner_model_loader_output")
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class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
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"""SDXL refiner model loader output"""
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unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
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clip2: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
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vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
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@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.2")
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class SDXLModelLoaderInvocation(BaseInvocation):
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"""Loads an sdxl base model, outputting its submodels."""
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model: ModelIdentifierField = InputField(
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description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
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)
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# TODO: precision?
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def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
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model_key = self.model.key
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# TODO: not found exceptions
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if not context.models.exists(model_key):
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raise Exception(f"Unknown model: {model_key}")
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unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
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scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
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tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
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text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
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tokenizer2 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
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text_encoder2 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
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vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
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return SDXLModelLoaderOutput(
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unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
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clip=CLIPField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
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clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
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vae=VAEField(vae=vae),
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)
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@invocation(
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"sdxl_refiner_model_loader",
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title="SDXL Refiner Model",
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tags=["model", "sdxl", "refiner"],
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category="model",
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version="1.0.2",
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)
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class SDXLRefinerModelLoaderInvocation(BaseInvocation):
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"""Loads an sdxl refiner model, outputting its submodels."""
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model: ModelIdentifierField = InputField(
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description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel
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)
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# TODO: precision?
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def invoke(self, context: InvocationContext) -> SDXLRefinerModelLoaderOutput:
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model_key = self.model.key
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# TODO: not found exceptions
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if not context.models.exists(model_key):
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raise Exception(f"Unknown model: {model_key}")
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unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
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scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
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tokenizer2 = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
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text_encoder2 = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
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vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
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return SDXLRefinerModelLoaderOutput(
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unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
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clip2=CLIPField(tokenizer=tokenizer2, text_encoder=text_encoder2, loras=[], skipped_layers=0),
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vae=VAEField(vae=vae),
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)
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