mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
328 lines
11 KiB
Python
328 lines
11 KiB
Python
import copy
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from typing import List, Literal, Optional, Union
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from pydantic import BaseModel, Field
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from ...backend.model_management import BaseModelType, ModelType, SubModelType
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from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
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InvocationConfig, InvocationContext)
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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(
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default=None, description="Info to load submodel"
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)
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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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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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class ModelLoaderOutput(BaseInvocationOutput):
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"""Model loader output"""
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# fmt: off
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type: Literal["model_loader_output"] = "model_loader_output"
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unet: UNetField = Field(default=None, description="UNet submodel")
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clip: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
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vae: VaeField = Field(default=None, description="Vae submodel")
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# fmt: on
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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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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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class MainModelLoaderInvocation(BaseInvocation):
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"""Loads a main model, outputting its submodels."""
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type: Literal["main_model_loader"] = "main_model_loader"
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model: MainModelField = Field(description="The model to load")
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# TODO: precision?
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# Schema customisation
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Model Loader",
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"tags": ["model", "loader"],
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"type_hints": {"model": "model"},
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},
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}
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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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clip2=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.Tokenizer2,
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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.TextEncoder2,
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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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class LoraLoaderOutput(BaseInvocationOutput):
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"""Model loader output"""
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# fmt: off
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type: Literal["lora_loader_output"] = "lora_loader_output"
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unet: Optional[UNetField] = Field(default=None, description="UNet submodel")
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clip: Optional[ClipField] = Field(default=None, description="Tokenizer and text_encoder submodels")
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# fmt: on
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class LoraLoaderInvocation(BaseInvocation):
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"""Apply selected lora to unet and text_encoder."""
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type: Literal["lora_loader"] = "lora_loader"
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lora: Union[LoRAModelField, None] = Field(
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default=None, description="Lora model name"
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)
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weight: float = Field(default=0.75, description="With what weight to apply lora")
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unet: Optional[UNetField] = Field(description="UNet model for applying lora")
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clip: Optional[ClipField] = Field(description="Clip model for applying lora")
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "Lora Loader",
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"tags": ["lora", "loader"],
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"type_hints": {"lora": "lora_model"},
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},
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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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# TODO: ui rewrite
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base_model = BaseModelType.StableDiffusion1
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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(
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lora.model_name == lora_name for lora in self.unet.loras
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):
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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(
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lora.model_name == lora_name for lora in self.clip.loras
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):
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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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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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class VaeLoaderOutput(BaseInvocationOutput):
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"""Model loader output"""
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# fmt: off
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type: Literal["vae_loader_output"] = "vae_loader_output"
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vae: VaeField = Field(default=None, description="Vae model")
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# fmt: on
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class VaeLoaderInvocation(BaseInvocation):
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"""Loads a VAE model, outputting a VaeLoaderOutput"""
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type: Literal["vae_loader"] = "vae_loader"
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vae_model: VAEModelField = Field(description="The VAE to load")
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# Schema customisation
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class Config(InvocationConfig):
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schema_extra = {
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"ui": {
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"title": "VAE Loader",
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"tags": ["vae", "loader"],
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"type_hints": {"vae_model": "vae_model"},
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},
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}
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def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
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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 VaeLoaderOutput(
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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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