mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
312 lines
11 KiB
Python
312 lines
11 KiB
Python
from typing import Literal, Optional, Union, List
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from pydantic import BaseModel, Field
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import copy
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from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
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from ...backend.util.devices import choose_torch_device, torch_dtype
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from ...backend.model_management import BaseModelType, ModelType, SubModelType
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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(description="Info to load submodel")
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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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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 SD1ModelLoaderInvocation(BaseInvocation):
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"""Loading submodels of selected model."""
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type: Literal["sd1_model_loader"] = "sd1_model_loader"
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model_name: str = Field(default="", description="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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"tags": ["model", "loader"],
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"type_hints": {
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"model_name": "model" # TODO: rename to model_name?
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}
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},
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}
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def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
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base_model = BaseModelType.StableDiffusion1 # TODO:
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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=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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):
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raise Exception(f"Unkown model name: {self.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=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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submodel=SubModelType.UNet,
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),
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scheduler=ModelInfo(
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model_name=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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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=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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submodel=SubModelType.Tokenizer,
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),
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text_encoder=ModelInfo(
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model_name=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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submodel=SubModelType.TextEncoder,
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),
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loras=[],
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),
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vae=VaeField(
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vae=ModelInfo(
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model_name=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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submodel=SubModelType.Vae,
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),
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)
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)
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# TODO: optimize(less code copy)
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class SD2ModelLoaderInvocation(BaseInvocation):
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"""Loading submodels of selected model."""
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type: Literal["sd2_model_loader"] = "sd2_model_loader"
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model_name: str = Field(default="", description="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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"tags": ["model", "loader"],
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"type_hints": {
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"model_name": "model" # TODO: rename to model_name?
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}
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},
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}
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def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
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base_model = BaseModelType.StableDiffusion2 # TODO:
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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=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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):
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raise Exception(f"Unkown model name: {self.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=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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submodel=SubModelType.UNet,
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),
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scheduler=ModelInfo(
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model_name=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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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=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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submodel=SubModelType.Tokenizer,
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),
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text_encoder=ModelInfo(
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model_name=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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submodel=SubModelType.TextEncoder,
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),
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loras=[],
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),
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vae=VaeField(
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vae=ModelInfo(
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model_name=self.model_name,
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base_model=base_model,
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model_type=ModelType.Pipeline,
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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_name: str = Field(description="Lora model name")
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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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def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
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if not context.services.model_manager.model_exists(
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model_name=self.lora_name,
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model_type=SDModelType.Lora,
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):
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raise Exception(f"Unkown lora name: {self.lora_name}!")
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if self.unet is not None and any(lora.model_name == self.lora_name for lora in self.unet.loras):
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raise Exception(f"Lora \"{self.lora_name}\" already applied to unet")
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if self.clip is not None and any(lora.model_name == self.lora_name for lora in self.clip.loras):
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raise Exception(f"Lora \"{self.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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model_name=self.lora_name,
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model_type=SDModelType.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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model_name=self.lora_name,
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model_type=SDModelType.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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