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90 lines
3.8 KiB
Python
90 lines
3.8 KiB
Python
from typing import Literal
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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, UIType
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from invokeai.app.invocations.model import CLIPField, ModelIdentifierField, T5EncoderField, TransformerField, VAEField
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.flux.util import max_seq_lengths
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from invokeai.backend.model_manager.config import (
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CheckpointConfigBase,
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SubModelType,
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)
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@invocation_output("flux_model_loader_output")
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class FluxModelLoaderOutput(BaseInvocationOutput):
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"""Flux base model loader output"""
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transformer: TransformerField = OutputField(description=FieldDescriptions.transformer, title="Transformer")
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clip: CLIPField = OutputField(description=FieldDescriptions.clip, title="CLIP")
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t5_encoder: T5EncoderField = OutputField(description=FieldDescriptions.t5_encoder, title="T5 Encoder")
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vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
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max_seq_len: Literal[256, 512] = OutputField(
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description="The max sequence length to used for the T5 encoder. (256 for schnell transformer, 512 for dev transformer)",
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title="Max Seq Length",
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)
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@invocation(
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"flux_model_loader",
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title="Flux Main Model",
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tags=["model", "flux"],
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category="model",
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version="1.0.4",
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classification=Classification.Prototype,
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)
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class FluxModelLoaderInvocation(BaseInvocation):
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"""Loads a flux base model, outputting its submodels."""
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model: ModelIdentifierField = InputField(
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description=FieldDescriptions.flux_model,
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ui_type=UIType.FluxMainModel,
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input=Input.Direct,
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)
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t5_encoder_model: ModelIdentifierField = InputField(
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description=FieldDescriptions.t5_encoder, ui_type=UIType.T5EncoderModel, input=Input.Direct, title="T5 Encoder"
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)
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clip_embed_model: ModelIdentifierField = InputField(
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description=FieldDescriptions.clip_embed_model,
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ui_type=UIType.CLIPEmbedModel,
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input=Input.Direct,
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title="CLIP Embed",
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)
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vae_model: ModelIdentifierField = InputField(
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description=FieldDescriptions.vae_model, ui_type=UIType.FluxVAEModel, title="VAE"
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)
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def invoke(self, context: InvocationContext) -> FluxModelLoaderOutput:
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for key in [self.model.key, self.t5_encoder_model.key, self.clip_embed_model.key, self.vae_model.key]:
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if not context.models.exists(key):
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raise ValueError(f"Unknown model: {key}")
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transformer = self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
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vae = self.vae_model.model_copy(update={"submodel_type": SubModelType.VAE})
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tokenizer = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
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clip_encoder = self.clip_embed_model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
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tokenizer2 = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.Tokenizer2})
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t5_encoder = self.t5_encoder_model.model_copy(update={"submodel_type": SubModelType.TextEncoder2})
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transformer_config = context.models.get_config(transformer)
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assert isinstance(transformer_config, CheckpointConfigBase)
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return FluxModelLoaderOutput(
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transformer=TransformerField(transformer=transformer, loras=[]),
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clip=CLIPField(tokenizer=tokenizer, text_encoder=clip_encoder, loras=[], skipped_layers=0),
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t5_encoder=T5EncoderField(tokenizer=tokenizer2, text_encoder=t5_encoder),
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vae=VAEField(vae=vae),
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max_seq_len=max_seq_lengths[transformer_config.config_path],
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)
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