from ...backend.model_management import ModelType, SubModelType from .baseinvocation import ( BaseInvocation, BaseInvocationOutput, FieldDescriptions, Input, InputField, InvocationContext, OutputField, UIType, invocation, invocation_output, ) from .model import ClipField, MainModelField, ModelInfo, UNetField, VaeField @invocation_output("sdxl_model_loader_output") class SDXLModelLoaderOutput(BaseInvocationOutput): """SDXL base model loader output""" unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet") clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 1") clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2") vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE") @invocation_output("sdxl_refiner_model_loader_output") class SDXLRefinerModelLoaderOutput(BaseInvocationOutput): """SDXL refiner model loader output""" unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet") clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2") vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE") @invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.0") class SDXLModelLoaderInvocation(BaseInvocation): """Loads an sdxl base model, outputting its submodels.""" model: MainModelField = InputField( description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel ) # TODO: precision? def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput: base_model = self.model.base_model model_name = self.model.model_name model_type = ModelType.Main # TODO: not found exceptions if not context.services.model_manager.model_exists( model_name=model_name, base_model=base_model, model_type=model_type, ): raise Exception(f"Unknown {base_model} {model_type} model: {model_name}") return SDXLModelLoaderOutput( unet=UNetField( unet=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.UNet, ), scheduler=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.Scheduler, ), loras=[], ), clip=ClipField( tokenizer=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.Tokenizer, ), text_encoder=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.TextEncoder, ), loras=[], skipped_layers=0, ), clip2=ClipField( tokenizer=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.Tokenizer2, ), text_encoder=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.TextEncoder2, ), loras=[], skipped_layers=0, ), vae=VaeField( vae=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.Vae, ), ), ) @invocation( "sdxl_refiner_model_loader", title="SDXL Refiner Model", tags=["model", "sdxl", "refiner"], category="model", version="1.0.0", ) class SDXLRefinerModelLoaderInvocation(BaseInvocation): """Loads an sdxl refiner model, outputting its submodels.""" model: MainModelField = InputField( description=FieldDescriptions.sdxl_refiner_model, input=Input.Direct, ui_type=UIType.SDXLRefinerModel, ) # TODO: precision? def invoke(self, context: InvocationContext) -> SDXLRefinerModelLoaderOutput: base_model = self.model.base_model model_name = self.model.model_name model_type = ModelType.Main # TODO: not found exceptions if not context.services.model_manager.model_exists( model_name=model_name, base_model=base_model, model_type=model_type, ): raise Exception(f"Unknown {base_model} {model_type} model: {model_name}") return SDXLRefinerModelLoaderOutput( unet=UNetField( unet=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.UNet, ), scheduler=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.Scheduler, ), loras=[], ), clip2=ClipField( tokenizer=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.Tokenizer2, ), text_encoder=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.TextEncoder2, ), loras=[], skipped_layers=0, ), vae=VaeField( vae=ModelInfo( model_name=model_name, base_model=base_model, model_type=model_type, submodel=SubModelType.Vae, ), ), )