mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
323 lines
9.8 KiB
Python
323 lines
9.8 KiB
Python
# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
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"""
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Configuration definitions for image generation models.
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Typical usage:
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from invokeai.backend.model_manager import ModelConfigFactory
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raw = dict(path='models/sd-1/main/foo.ckpt',
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name='foo',
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base='sd-1',
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type='main',
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config='configs/stable-diffusion/v1-inference.yaml',
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variant='normal',
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format='checkpoint'
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)
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config = ModelConfigFactory.make_config(raw)
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print(config.name)
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Validation errors will raise an InvalidModelConfigException error.
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"""
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from enum import Enum
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from typing import Literal, Optional, Type, Union
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from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
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from typing_extensions import Annotated
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class InvalidModelConfigException(Exception):
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"""Exception for when config parser doesn't recognized this combination of model type and format."""
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class BaseModelType(str, Enum):
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"""Base model type."""
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Any = "any"
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StableDiffusion1 = "sd-1"
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StableDiffusion2 = "sd-2"
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StableDiffusionXL = "sdxl"
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StableDiffusionXLRefiner = "sdxl-refiner"
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# Kandinsky2_1 = "kandinsky-2.1"
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class ModelType(str, Enum):
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"""Model type."""
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ONNX = "onnx"
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Main = "main"
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Vae = "vae"
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Lora = "lora"
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ControlNet = "controlnet" # used by model_probe
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TextualInversion = "embedding"
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IPAdapter = "ip_adapter"
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CLIPVision = "clip_vision"
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T2IAdapter = "t2i_adapter"
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class SubModelType(str, Enum):
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"""Submodel type."""
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UNet = "unet"
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TextEncoder = "text_encoder"
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TextEncoder2 = "text_encoder_2"
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Tokenizer = "tokenizer"
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Tokenizer2 = "tokenizer_2"
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Vae = "vae"
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VaeDecoder = "vae_decoder"
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VaeEncoder = "vae_encoder"
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Scheduler = "scheduler"
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SafetyChecker = "safety_checker"
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class ModelVariantType(str, Enum):
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"""Variant type."""
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Normal = "normal"
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Inpaint = "inpaint"
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Depth = "depth"
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class ModelFormat(str, Enum):
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"""Storage format of model."""
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Diffusers = "diffusers"
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Checkpoint = "checkpoint"
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Lycoris = "lycoris"
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Onnx = "onnx"
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Olive = "olive"
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EmbeddingFile = "embedding_file"
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EmbeddingFolder = "embedding_folder"
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InvokeAI = "invokeai"
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class SchedulerPredictionType(str, Enum):
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"""Scheduler prediction type."""
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Epsilon = "epsilon"
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VPrediction = "v_prediction"
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Sample = "sample"
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class ModelConfigBase(BaseModel):
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"""Base class for model configuration information."""
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path: str
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name: str
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base: BaseModelType
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type: ModelType
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format: ModelFormat
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key: str = Field(description="unique key for model", default="<NOKEY>")
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original_hash: Optional[str] = Field(
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description="original fasthash of model contents", default=None
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) # this is assigned at install time and will not change
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current_hash: Optional[str] = Field(
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description="current fasthash of model contents", default=None
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) # if model is converted or otherwise modified, this will hold updated hash
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description: Optional[str] = Field(default=None)
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source: Optional[str] = Field(description="Model download source (URL or repo_id)", default=None)
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model_config = ConfigDict(
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use_enum_values=False,
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validate_assignment=True,
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)
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def update(self, attributes: dict):
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"""Update the object with fields in dict."""
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for key, value in attributes.items():
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setattr(self, key, value) # may raise a validation error
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class _CheckpointConfig(ModelConfigBase):
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"""Model config for checkpoint-style models."""
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format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
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config: str = Field(description="path to the checkpoint model config file")
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class _DiffusersConfig(ModelConfigBase):
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"""Model config for diffusers-style models."""
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format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
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class LoRAConfig(ModelConfigBase):
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"""Model config for LoRA/Lycoris models."""
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type: Literal[ModelType.Lora] = ModelType.Lora
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format: Literal[ModelFormat.Lycoris, ModelFormat.Diffusers]
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class VaeCheckpointConfig(ModelConfigBase):
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"""Model config for standalone VAE models."""
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type: Literal[ModelType.Vae] = ModelType.Vae
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format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
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class VaeDiffusersConfig(ModelConfigBase):
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"""Model config for standalone VAE models (diffusers version)."""
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type: Literal[ModelType.Vae] = ModelType.Vae
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format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
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class ControlNetDiffusersConfig(_DiffusersConfig):
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"""Model config for ControlNet models (diffusers version)."""
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type: Literal[ModelType.ControlNet] = ModelType.ControlNet
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format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
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class ControlNetCheckpointConfig(_CheckpointConfig):
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"""Model config for ControlNet models (diffusers version)."""
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type: Literal[ModelType.ControlNet] = ModelType.ControlNet
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format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
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class TextualInversionConfig(ModelConfigBase):
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"""Model config for textual inversion embeddings."""
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type: Literal[ModelType.TextualInversion] = ModelType.TextualInversion
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format: Literal[ModelFormat.EmbeddingFile, ModelFormat.EmbeddingFolder]
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class _MainConfig(ModelConfigBase):
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"""Model config for main models."""
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vae: Optional[str] = Field(default=None)
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variant: ModelVariantType = ModelVariantType.Normal
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ztsnr_training: bool = False
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class MainCheckpointConfig(_CheckpointConfig, _MainConfig):
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"""Model config for main checkpoint models."""
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type: Literal[ModelType.Main] = ModelType.Main
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# Note that we do not need prediction_type or upcast_attention here
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# because they are provided in the checkpoint's own config file.
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class MainDiffusersConfig(_DiffusersConfig, _MainConfig):
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"""Model config for main diffusers models."""
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type: Literal[ModelType.Main] = ModelType.Main
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prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
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upcast_attention: bool = False
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class ONNXSD1Config(_MainConfig):
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"""Model config for ONNX format models based on sd-1."""
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type: Literal[ModelType.ONNX] = ModelType.ONNX
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format: Literal[ModelFormat.Onnx, ModelFormat.Olive]
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base: Literal[BaseModelType.StableDiffusion1] = BaseModelType.StableDiffusion1
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prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
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upcast_attention: bool = False
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class ONNXSD2Config(_MainConfig):
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"""Model config for ONNX format models based on sd-2."""
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type: Literal[ModelType.ONNX] = ModelType.ONNX
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format: Literal[ModelFormat.Onnx, ModelFormat.Olive]
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# No yaml config file for ONNX, so these are part of config
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base: Literal[BaseModelType.StableDiffusion2] = BaseModelType.StableDiffusion2
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prediction_type: SchedulerPredictionType = SchedulerPredictionType.VPrediction
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upcast_attention: bool = True
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class IPAdapterConfig(ModelConfigBase):
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"""Model config for IP Adaptor format models."""
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type: Literal[ModelType.IPAdapter] = ModelType.IPAdapter
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format: Literal[ModelFormat.InvokeAI]
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class CLIPVisionDiffusersConfig(ModelConfigBase):
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"""Model config for ClipVision."""
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type: Literal[ModelType.CLIPVision] = ModelType.CLIPVision
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format: Literal[ModelFormat.Diffusers]
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class T2IConfig(ModelConfigBase):
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"""Model config for T2I."""
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type: Literal[ModelType.T2IAdapter] = ModelType.T2IAdapter
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format: Literal[ModelFormat.Diffusers]
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_ONNXConfig = Annotated[Union[ONNXSD1Config, ONNXSD2Config], Field(discriminator="base")]
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_ControlNetConfig = Annotated[
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Union[ControlNetDiffusersConfig, ControlNetCheckpointConfig], Field(discriminator="format")
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]
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_VaeConfig = Annotated[Union[VaeDiffusersConfig, VaeCheckpointConfig], Field(discriminator="format")]
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_MainModelConfig = Annotated[Union[MainDiffusersConfig, MainCheckpointConfig], Field(discriminator="format")]
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AnyModelConfig = Union[
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_MainModelConfig,
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_ONNXConfig,
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_VaeConfig,
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_ControlNetConfig,
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LoRAConfig,
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TextualInversionConfig,
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IPAdapterConfig,
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CLIPVisionDiffusersConfig,
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T2IConfig,
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]
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AnyModelConfigValidator = TypeAdapter(AnyModelConfig)
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# IMPLEMENTATION NOTE:
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# The preferred alternative to the above is a discriminated Union as shown
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# below. However, it breaks FastAPI when used as the input Body parameter in a route.
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# This is a known issue. Please see:
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# https://github.com/tiangolo/fastapi/discussions/9761 and
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# https://github.com/tiangolo/fastapi/discussions/9287
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# AnyModelConfig = Annotated[
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# Union[
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# _MainModelConfig,
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# _ONNXConfig,
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# _VaeConfig,
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# _ControlNetConfig,
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# LoRAConfig,
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# TextualInversionConfig,
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# IPAdapterConfig,
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# CLIPVisionDiffusersConfig,
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# T2IConfig,
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# ],
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# Field(discriminator="type"),
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# ]
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class ModelConfigFactory(object):
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"""Class for parsing config dicts into StableDiffusion Config obects."""
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@classmethod
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def make_config(
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cls,
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model_data: Union[dict, ModelConfigBase],
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key: Optional[str] = None,
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dest_class: Optional[Type] = None,
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) -> AnyModelConfig:
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"""
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Return the appropriate config object from raw dict values.
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:param model_data: A raw dict corresponding the obect fields to be
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parsed into a ModelConfigBase obect (or descendent), or a ModelConfigBase
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object, which will be passed through unchanged.
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:param dest_class: The config class to be returned. If not provided, will
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be selected automatically.
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"""
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if isinstance(model_data, ModelConfigBase):
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model = model_data
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elif dest_class:
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model = dest_class.validate_python(model_data)
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else:
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model = AnyModelConfigValidator.validate_python(model_data)
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if key:
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model.key = key
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return model
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