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https://github.com/invoke-ai/InvokeAI
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Run ruff
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@ -1,6 +1,5 @@
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import copy
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from time import sleep
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from typing import Dict, List, Literal, Optional
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from typing import List, Literal, Optional
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import yaml
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from pydantic import BaseModel, Field
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@ -13,14 +12,12 @@ from invokeai.app.invocations.baseinvocation import (
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invocation_output,
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)
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from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
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from invokeai.app.services.model_records import ModelRecordChanges
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.app.shared.models import FreeUConfig
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from invokeai.backend.model_manager.config import (
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AnyModelConfig,
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BaseModelType,
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CheckpointConfigBase,
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ModelFormat,
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ModelType,
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SubModelType,
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)
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@ -13,7 +13,6 @@ from invokeai.app.services.config.config_default import InvokeAIAppConfig
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from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
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from invokeai.app.services.images.images_common import ImageDTO
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from invokeai.app.services.invocation_services import InvocationServices
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from invokeai.app.services.model_records import ModelRecordChanges
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from invokeai.app.services.model_records.model_records_base import UnknownModelException
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from invokeai.app.util.step_callback import stable_diffusion_step_callback
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from invokeai.backend.model_manager.config import (
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@ -87,7 +87,9 @@ class ClipCheckpointModel(ModelLoader):
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case SubModelType.TextEncoder:
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return CLIPTextModel.from_pretrained(config.path)
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raise ValueError(f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
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raise ValueError(
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f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
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)
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder8b)
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@ -108,7 +110,9 @@ class T5Encoder8bCheckpointModel(ModelLoader):
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case SubModelType.TextEncoder2:
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return FastQuantizedTransformersModel.from_pretrained(Path(config.path) / "text_encoder_2")
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raise ValueError(f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
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raise ValueError(
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f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
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)
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder)
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@ -131,7 +135,9 @@ class T5EncoderCheckpointModel(ModelLoader):
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Path(config.path) / "text_encoder_2"
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) # TODO: Fix hf subfolder install
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raise ValueError(f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
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raise ValueError(
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f"Only Tokenizer and TextEncoder submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
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)
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@ModelLoaderRegistry.register(base=BaseModelType.Flux, type=ModelType.Main, format=ModelFormat.Checkpoint)
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@ -154,7 +160,9 @@ class FluxCheckpointModel(ModelLoader):
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case SubModelType.Transformer:
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return self._load_from_singlefile(config, flux_conf)
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raise ValueError(f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
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raise ValueError(
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f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
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)
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def _load_from_singlefile(
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self,
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@ -194,7 +202,9 @@ class FluxBnbQuantizednf4bCheckpointModel(ModelLoader):
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case SubModelType.Transformer:
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return self._load_from_singlefile(config, flux_conf)
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raise ValueError(f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}")
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raise ValueError(
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f"Only Transformer submodels are currently supported. Received: {submodel_type.value if submodel_type else 'None'}"
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)
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def _load_from_singlefile(
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self,
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