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87 lines
3.3 KiB
Python
87 lines
3.3 KiB
Python
from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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Classification,
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invocation,
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invocation_output,
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)
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from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
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from invokeai.app.invocations.model import (
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ModelIdentifierField,
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Qwen3EncoderField,
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TransformerField,
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VAEField,
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)
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelType, SubModelType
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@invocation_output("anima_model_loader_output")
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class AnimaModelLoaderOutput(BaseInvocationOutput):
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"""Anima model loader output."""
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transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
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qwen3_encoder: Qwen3EncoderField = OutputField(description=FieldDescriptions.qwen3_encoder, title="Qwen3 Encoder")
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vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
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@invocation(
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"anima_model_loader",
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title="Main Model - Anima",
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tags=["model", "anima"],
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category="model",
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version="1.4.0",
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classification=Classification.Prototype,
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)
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class AnimaModelLoaderInvocation(BaseInvocation):
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"""Loads an Anima model, outputting its submodels.
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Anima uses:
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- Transformer: Cosmos Predict2 DiT + LLM Adapter (from single-file checkpoint)
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- Qwen3 Encoder: Qwen3 0.6B (standalone single-file)
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- VAE: AutoencoderKLQwenImage / Wan 2.1 VAE (standalone single-file or FLUX VAE)
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The T5-XXL tokenizer needed for LLM Adapter token IDs is bundled in the package,
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so no T5-XXL encoder model needs to be installed.
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"""
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model: ModelIdentifierField = InputField(
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description="Anima main model (transformer + LLM adapter).",
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input=Input.Direct,
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ui_model_base=BaseModelType.Anima,
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ui_model_type=ModelType.Main,
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title="Transformer",
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)
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vae_model: ModelIdentifierField = InputField(
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description="Standalone VAE model. Anima uses a Wan 2.1 / QwenImage VAE (16-channel). "
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"A FLUX VAE can also be used as a compatible fallback.",
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input=Input.Direct,
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ui_model_type=ModelType.VAE,
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title="VAE",
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)
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qwen3_encoder_model: ModelIdentifierField = InputField(
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description="Standalone Qwen3 0.6B Encoder model.",
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input=Input.Direct,
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ui_model_type=ModelType.Qwen3Encoder,
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title="Qwen3 Encoder",
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)
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def invoke(self, context: InvocationContext) -> AnimaModelLoaderOutput:
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# Transformer always comes from the main model
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transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
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# VAE
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vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
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# Qwen3 Encoder
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qwen3_tokenizer = self.qwen3_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
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qwen3_encoder = self.qwen3_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
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return AnimaModelLoaderOutput(
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transformer=TransformerField(transformer=transformer, loras=[]),
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qwen3_encoder=Qwen3EncoderField(tokenizer=qwen3_tokenizer, text_encoder=qwen3_encoder),
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vae=VAEField(vae=vae),
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)
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