mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
528ac5dd25
- All models are identified by a key and optionally a submodel type via new model `ModelField`. Previously, a few model types had their own class, but not all of them. This inconsistency just added complexity without any benefit. - Update all invocation to use the new format. - In the node API, models are loaded by key or an instance of `ModelField` as a convenience. - Add an enriched model schema for metadata. It includes key, hash, name, base and type.
349 lines
12 KiB
Python
349 lines
12 KiB
Python
import copy
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from typing import List, Optional
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from pydantic import BaseModel, Field
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from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
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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 SubModelType
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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invocation,
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invocation_output,
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)
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class ModelField(BaseModel):
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key: str = Field(description="Key of the model")
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submodel_type: Optional[SubModelType] = Field(description="Submodel type", default=None)
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class LoRAField(BaseModel):
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lora: ModelField = Field(description="Info to load lora model")
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weight: float = Field(description="Weight to apply to lora model")
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class UNetField(BaseModel):
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unet: ModelField = Field(description="Info to load unet submodel")
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scheduler: ModelField = Field(description="Info to load scheduler submodel")
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loras: List[LoRAField] = 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: ModelField = Field(description="Info to load tokenizer submodel")
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text_encoder: ModelField = 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[LoRAField] = 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: ModelField = 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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@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="1.0.1",
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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: ModelField = 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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# TODO: not found exceptions
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if not context.models.exists(self.model.key):
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raise Exception(f"Unknown model {self.model.key}")
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unet = self.model.model_copy(update={"submodel_type": SubModelType.UNet})
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scheduler = self.model.model_copy(update={"submodel_type": SubModelType.Scheduler})
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tokenizer = self.model.model_copy(update={"submodel_type": SubModelType.Tokenizer})
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text_encoder = self.model.model_copy(update={"submodel_type": SubModelType.TextEncoder})
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vae = self.model.model_copy(update={"submodel_type": SubModelType.VAE})
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return ModelLoaderOutput(
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unet=UNetField(unet=unet, scheduler=scheduler, loras=[]),
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clip=ClipField(tokenizer=tokenizer, text_encoder=text_encoder, loras=[], skipped_layers=0),
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vae=VaeField(vae=vae),
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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="1.0.1")
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class LoraLoaderInvocation(BaseInvocation):
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"""Apply selected lora to unet and text_encoder."""
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lora: ModelField = 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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lora_key = self.lora.key
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if not context.models.exists(lora_key):
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raise Exception(f"Unkown lora: {lora_key}!")
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if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
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raise Exception(f'Lora "{lora_key}" already applied to unet')
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if self.clip is not None and any(lora.lora.key == lora_key for lora in self.clip.loras):
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raise Exception(f'Lora "{lora_key}" 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 = self.unet.model_copy(deep=True)
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output.unet.loras.append(
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LoRAField(
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lora=self.lora,
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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 = self.clip.model_copy(deep=True)
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output.clip.loras.append(
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LoRAField(
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lora=self.lora,
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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="1.0.1",
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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: ModelField = 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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lora_key = self.lora.key
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if not context.models.exists(lora_key):
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raise Exception(f"Unknown lora: {lora_key}!")
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if self.unet is not None and any(lora.lora.key == lora_key for lora in self.unet.loras):
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raise Exception(f'Lora "{lora_key}" already applied to unet')
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if self.clip is not None and any(lora.lora.key == lora_key for lora in self.clip.loras):
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raise Exception(f'Lora "{lora_key}" already applied to clip')
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if self.clip2 is not None and any(lora.lora.key == lora_key for lora in self.clip2.loras):
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raise Exception(f'Lora "{lora_key}" 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 = self.unet.model_copy(deep=True)
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output.unet.loras.append(
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LoRAField(
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lora=self.lora,
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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 = self.clip.model_copy(deep=True)
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output.clip.loras.append(
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LoRAField(
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lora=self.lora,
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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 = self.clip2.model_copy(deep=True)
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output.clip2.loras.append(
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LoRAField(
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lora=self.lora,
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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("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
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class VaeLoaderInvocation(BaseInvocation):
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"""Loads a VAE model, outputting a VaeLoaderOutput"""
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vae_model: ModelField = InputField(
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description=FieldDescriptions.vae_model,
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input=Input.Direct,
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title="VAE",
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)
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def invoke(self, context: InvocationContext) -> VAEOutput:
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key = self.vae_model.key
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if not context.models.exists(key):
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raise Exception(f"Unkown vae: {key}!")
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return VAEOutput(vae=VaeField(vae=self.vae_model))
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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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