mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
510 lines
17 KiB
Python
510 lines
17 KiB
Python
import copy
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from typing import List, Optional
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from pydantic import BaseModel, ConfigDict, Field
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from ...backend.model_management import BaseModelType, ModelType, SubModelType
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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FieldDescriptions,
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Input,
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InputField,
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InvocationContext,
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OutputField,
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UIType,
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invocation,
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invocation_output,
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)
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# TODO: Permanent fix for this
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# from invokeai.app.invocations.shared import FreeUConfig
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class FreeUConfig(BaseModel):
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"""
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Configuration for the FreeU hyperparameters.
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- https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu
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- https://github.com/ChenyangSi/FreeU
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"""
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s1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s1)
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s2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s2)
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b1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b1)
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b2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b2)
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class ModelInfo(BaseModel):
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model_name: str = Field(description="Info to load submodel")
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base_model: BaseModelType = Field(description="Base model")
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model_type: ModelType = Field(description="Info to load submodel")
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submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
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model_config = ConfigDict(protected_namespaces=())
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class LoraInfo(ModelInfo):
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weight: float = Field(description="Lora's weight which to use when apply to model")
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class UNetField(BaseModel):
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unet: ModelInfo = Field(description="Info to load unet submodel")
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scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
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loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
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seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
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freeu_config: Optional[FreeUConfig] = Field(default=None, description="FreeU configuration")
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class ClipField(BaseModel):
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tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel")
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text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel")
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skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
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loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
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class VaeField(BaseModel):
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# TODO: better naming?
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vae: ModelInfo = Field(description="Info to load vae submodel")
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seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
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@invocation_output("unet_output")
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class UNetOutput(BaseInvocationOutput):
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"""Base class for invocations that output a UNet field"""
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unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
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@invocation_output("vae_output")
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class VAEOutput(BaseInvocationOutput):
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"""Base class for invocations that output a VAE field"""
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vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
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@invocation_output("clip_output")
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class CLIPOutput(BaseInvocationOutput):
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"""Base class for invocations that output a CLIP field"""
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clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
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@invocation_output("model_loader_output")
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class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
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"""Model loader output"""
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pass
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class MainModelField(BaseModel):
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"""Main model field"""
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model_name: str = Field(description="Name of the model")
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base_model: BaseModelType = Field(description="Base model")
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model_type: ModelType = Field(description="Model Type")
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model_config = ConfigDict(protected_namespaces=())
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class LoRAModelField(BaseModel):
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"""LoRA model field"""
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model_name: str = Field(description="Name of the LoRA model")
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base_model: BaseModelType = Field(description="Base model")
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model_config = ConfigDict(protected_namespaces=())
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@invocation(
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"main_model_loader",
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title="Main Model",
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tags=["model"],
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category="model",
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version="1.0.0",
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)
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class MainModelLoaderInvocation(BaseInvocation):
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"""Loads a main model, outputting its submodels."""
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model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
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# TODO: precision?
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def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
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base_model = self.model.base_model
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model_name = self.model.model_name
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model_type = ModelType.Main
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# TODO: not found exceptions
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if not context.services.model_manager.model_exists(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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):
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raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
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"""
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if not context.services.model_manager.model_exists(
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model_name=self.model_name,
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model_type=SDModelType.Diffusers,
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submodel=SDModelType.Tokenizer,
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):
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raise Exception(
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f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
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)
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if not context.services.model_manager.model_exists(
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model_name=self.model_name,
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model_type=SDModelType.Diffusers,
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submodel=SDModelType.TextEncoder,
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):
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raise Exception(
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f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
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)
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if not context.services.model_manager.model_exists(
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model_name=self.model_name,
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model_type=SDModelType.Diffusers,
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submodel=SDModelType.UNet,
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):
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raise Exception(
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f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
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)
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"""
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return ModelLoaderOutput(
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unet=UNetField(
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unet=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.UNet,
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),
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scheduler=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.Scheduler,
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),
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loras=[],
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),
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clip=ClipField(
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tokenizer=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.Tokenizer,
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),
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text_encoder=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.TextEncoder,
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),
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loras=[],
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skipped_layers=0,
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),
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vae=VaeField(
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vae=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.Vae,
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),
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),
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)
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@invocation_output("lora_loader_output")
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class LoraLoaderOutput(BaseInvocationOutput):
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"""Model loader output"""
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unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
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clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
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@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.0")
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class LoraLoaderInvocation(BaseInvocation):
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"""Apply selected lora to unet and text_encoder."""
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lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
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weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
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unet: Optional[UNetField] = InputField(
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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clip: Optional[ClipField] = InputField(
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP",
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)
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def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
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if self.lora is None:
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raise Exception("No LoRA provided")
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base_model = self.lora.base_model
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lora_name = self.lora.model_name
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if not context.services.model_manager.model_exists(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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):
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raise Exception(f"Unkown lora name: {lora_name}!")
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if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
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raise Exception(f'Lora "{lora_name}" already applied to unet')
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if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
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raise Exception(f'Lora "{lora_name}" already applied to clip')
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output = LoraLoaderOutput()
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if self.unet is not None:
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output.unet = copy.deepcopy(self.unet)
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output.unet.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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if self.clip is not None:
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output.clip = copy.deepcopy(self.clip)
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output.clip.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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return output
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@invocation_output("sdxl_lora_loader_output")
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class SDXLLoraLoaderOutput(BaseInvocationOutput):
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"""SDXL LoRA Loader Output"""
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unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
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clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
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clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
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@invocation(
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"sdxl_lora_loader",
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title="SDXL LoRA",
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tags=["lora", "model"],
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category="model",
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version="1.0.0",
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)
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class SDXLLoraLoaderInvocation(BaseInvocation):
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"""Apply selected lora to unet and text_encoder."""
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lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
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weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
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unet: Optional[UNetField] = InputField(
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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clip: Optional[ClipField] = InputField(
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP 1",
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)
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clip2: Optional[ClipField] = InputField(
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP 2",
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)
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def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
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if self.lora is None:
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raise Exception("No LoRA provided")
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base_model = self.lora.base_model
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lora_name = self.lora.model_name
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if not context.services.model_manager.model_exists(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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):
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raise Exception(f"Unknown lora name: {lora_name}!")
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if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
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raise Exception(f'Lora "{lora_name}" already applied to unet')
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if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
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raise Exception(f'Lora "{lora_name}" already applied to clip')
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if self.clip2 is not None and any(lora.model_name == lora_name for lora in self.clip2.loras):
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raise Exception(f'Lora "{lora_name}" already applied to clip2')
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output = SDXLLoraLoaderOutput()
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if self.unet is not None:
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output.unet = copy.deepcopy(self.unet)
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output.unet.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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if self.clip is not None:
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output.clip = copy.deepcopy(self.clip)
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output.clip.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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if self.clip2 is not None:
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output.clip2 = copy.deepcopy(self.clip2)
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output.clip2.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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return output
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class VAEModelField(BaseModel):
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"""Vae model field"""
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model_name: str = Field(description="Name of the model")
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base_model: BaseModelType = Field(description="Base model")
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model_config = ConfigDict(protected_namespaces=())
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@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
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class VaeLoaderInvocation(BaseInvocation):
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"""Loads a VAE model, outputting a VaeLoaderOutput"""
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vae_model: VAEModelField = InputField(
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description=FieldDescriptions.vae_model,
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input=Input.Direct,
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ui_type=UIType.VaeModel,
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title="VAE",
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)
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def invoke(self, context: InvocationContext) -> VAEOutput:
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base_model = self.vae_model.base_model
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model_name = self.vae_model.model_name
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model_type = ModelType.Vae
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if not context.services.model_manager.model_exists(
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base_model=base_model,
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model_name=model_name,
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model_type=model_type,
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):
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raise Exception(f"Unkown vae name: {model_name}!")
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return VAEOutput(
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vae=VaeField(
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vae=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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)
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)
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)
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@invocation_output("seamless_output")
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class SeamlessModeOutput(BaseInvocationOutput):
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"""Modified Seamless Model output"""
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unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
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vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
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@invocation(
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"seamless",
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title="Seamless",
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tags=["seamless", "model"],
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category="model",
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version="1.0.0",
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)
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class SeamlessModeInvocation(BaseInvocation):
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"""Applies the seamless transformation to the Model UNet and VAE."""
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unet: Optional[UNetField] = InputField(
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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vae: Optional[VaeField] = InputField(
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default=None,
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description=FieldDescriptions.vae_model,
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input=Input.Connection,
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title="VAE",
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)
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seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
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seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
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def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
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# Conditionally append 'x' and 'y' based on seamless_x and seamless_y
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unet = copy.deepcopy(self.unet)
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vae = copy.deepcopy(self.vae)
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seamless_axes_list = []
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if self.seamless_x:
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seamless_axes_list.append("x")
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if self.seamless_y:
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seamless_axes_list.append("y")
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if unet is not None:
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unet.seamless_axes = seamless_axes_list
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if vae is not None:
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vae.seamless_axes = seamless_axes_list
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return SeamlessModeOutput(unet=unet, vae=vae)
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@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.0")
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class FreeUInvocation(BaseInvocation):
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"""
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Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2):
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SD1.5: 1.2/1.4/0.9/0.2,
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SD2: 1.1/1.2/0.9/0.2,
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SDXL: 1.1/1.2/0.6/0.4,
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"""
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unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet")
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b1: float = InputField(default=1.2, ge=-1, le=3, description=FieldDescriptions.freeu_b1)
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b2: float = InputField(default=1.4, ge=-1, le=3, description=FieldDescriptions.freeu_b2)
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s1: float = InputField(default=0.9, ge=-1, le=3, description=FieldDescriptions.freeu_s1)
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s2: float = InputField(default=0.2, ge=-1, le=3, description=FieldDescriptions.freeu_s2)
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def invoke(self, context: InvocationContext) -> UNetOutput:
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self.unet.freeu_config = FreeUConfig(s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2)
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return UNetOutput(unet=self.unet)
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