InvokeAI/invokeai/backend/model_manager/config.py

# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
"""
Configuration definitions for image generation models.

Typical usage:

  from invokeai.backend.model_manager import ModelConfigFactory
  raw = dict(path='models/sd-1/main/foo.ckpt',
             name='foo',
             base='sd-1',
             type='main',
             config='configs/stable-diffusion/v1-inference.yaml',
             variant='normal',
             format='checkpoint'
            )
  config = ModelConfigFactory.make_config(raw)
  print(config.name)

Validation errors will raise an InvalidModelConfigException error.

"""
import time
import torch
from enum import Enum
from typing import Literal, Optional, Type, Union

from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
from diffusers import ModelMixin
from typing_extensions import Annotated, Any, Dict
from .onnx_runtime import IAIOnnxRuntimeModel

class InvalidModelConfigException(Exception):
    """Exception for when config parser doesn't recognized this combination of model type and format."""


class BaseModelType(str, Enum):
    """Base model type."""

    Any = "any"
    StableDiffusion1 = "sd-1"
    StableDiffusion2 = "sd-2"
    StableDiffusionXL = "sdxl"
    StableDiffusionXLRefiner = "sdxl-refiner"
    # Kandinsky2_1 = "kandinsky-2.1"


class ModelType(str, Enum):
    """Model type."""

    ONNX = "onnx"
    Main = "main"
    Vae = "vae"
    Lora = "lora"
    ControlNet = "controlnet"  # used by model_probe
    TextualInversion = "embedding"
    IPAdapter = "ip_adapter"
    CLIPVision = "clip_vision"
    T2IAdapter = "t2i_adapter"


class SubModelType(str, Enum):
    """Submodel type."""

    UNet = "unet"
    TextEncoder = "text_encoder"
    TextEncoder2 = "text_encoder_2"
    Tokenizer = "tokenizer"
    Tokenizer2 = "tokenizer_2"
    Vae = "vae"
    VaeDecoder = "vae_decoder"
    VaeEncoder = "vae_encoder"
    Scheduler = "scheduler"
    SafetyChecker = "safety_checker"


class ModelVariantType(str, Enum):
    """Variant type."""

    Normal = "normal"
    Inpaint = "inpaint"
    Depth = "depth"


class ModelFormat(str, Enum):
    """Storage format of model."""

    Diffusers = "diffusers"
    Checkpoint = "checkpoint"
    Lycoris = "lycoris"
    Onnx = "onnx"
    Olive = "olive"
    EmbeddingFile = "embedding_file"
    EmbeddingFolder = "embedding_folder"
    InvokeAI = "invokeai"


class SchedulerPredictionType(str, Enum):
    """Scheduler prediction type."""

    Epsilon = "epsilon"
    VPrediction = "v_prediction"
    Sample = "sample"


class ModelRepoVariant(str, Enum):
    """Various hugging face variants on the diffusers format."""

    DEFAULT = "default"  # model files without "fp16" or other qualifier
    FP16 = "fp16"
    FP32 = "fp32"
    ONNX = "onnx"
    OPENVINO = "openvino"
    FLAX = "flax"


class ModelConfigBase(BaseModel):
    """Base class for model configuration information."""

    path: str
    name: str
    base: BaseModelType
    type: ModelType
    format: ModelFormat
    key: str = Field(description="unique key for model", default="<NOKEY>")
    original_hash: Optional[str] = Field(
        description="original fasthash of model contents", default=None
    )  # this is assigned at install time and will not change
    current_hash: Optional[str] = Field(
        description="current fasthash of model contents", default=None
    )  # if model is converted or otherwise modified, this will hold updated hash
    description: Optional[str] = Field(default=None)
    source: Optional[str] = Field(description="Model download source (URL or repo_id)", default=None)
    last_modified: Optional[float] = Field(description="Timestamp for modification time", default_factory=time.time)

    model_config = ConfigDict(
        use_enum_values=False,
        validate_assignment=True,
    )

    def update(self, attributes: Dict[str, Any]) -> None:
        """Update the object with fields in dict."""
        for key, value in attributes.items():
            setattr(self, key, value)  # may raise a validation error


class _CheckpointConfig(ModelConfigBase):
    """Model config for checkpoint-style models."""

    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint
    config: str = Field(description="path to the checkpoint model config file")


class _DiffusersConfig(ModelConfigBase):
    """Model config for diffusers-style models."""

    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers
    repo_variant: Optional[ModelRepoVariant] = ModelRepoVariant.DEFAULT


class LoRAConfig(ModelConfigBase):
    """Model config for LoRA/Lycoris models."""

    type: Literal[ModelType.Lora] = ModelType.Lora
    format: Literal[ModelFormat.Lycoris, ModelFormat.Diffusers]


class VaeCheckpointConfig(ModelConfigBase):
    """Model config for standalone VAE models."""

    type: Literal[ModelType.Vae] = ModelType.Vae
    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint


class VaeDiffusersConfig(ModelConfigBase):
    """Model config for standalone VAE models (diffusers version)."""

    type: Literal[ModelType.Vae] = ModelType.Vae
    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers


class ControlNetDiffusersConfig(_DiffusersConfig):
    """Model config for ControlNet models (diffusers version)."""

    type: Literal[ModelType.ControlNet] = ModelType.ControlNet
    format: Literal[ModelFormat.Diffusers] = ModelFormat.Diffusers


class ControlNetCheckpointConfig(_CheckpointConfig):
    """Model config for ControlNet models (diffusers version)."""

    type: Literal[ModelType.ControlNet] = ModelType.ControlNet
    format: Literal[ModelFormat.Checkpoint] = ModelFormat.Checkpoint


class TextualInversionConfig(ModelConfigBase):
    """Model config for textual inversion embeddings."""

    type: Literal[ModelType.TextualInversion] = ModelType.TextualInversion
    format: Literal[ModelFormat.EmbeddingFile, ModelFormat.EmbeddingFolder]


class _MainConfig(ModelConfigBase):
    """Model config for main models."""

    vae: Optional[str] = Field(default=None)
    variant: ModelVariantType = ModelVariantType.Normal
    ztsnr_training: bool = False


class MainCheckpointConfig(_CheckpointConfig, _MainConfig):
    """Model config for main checkpoint models."""

    type: Literal[ModelType.Main] = ModelType.Main


class MainDiffusersConfig(_DiffusersConfig, _MainConfig):
    """Model config for main diffusers models."""

    type: Literal[ModelType.Main] = ModelType.Main
    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
    upcast_attention: bool = False


class ONNXSD1Config(_MainConfig):
    """Model config for ONNX format models based on sd-1."""

    type: Literal[ModelType.ONNX] = ModelType.ONNX
    format: Literal[ModelFormat.Onnx, ModelFormat.Olive]
    base: Literal[BaseModelType.StableDiffusion1] = BaseModelType.StableDiffusion1
    prediction_type: SchedulerPredictionType = SchedulerPredictionType.Epsilon
    upcast_attention: bool = False


class ONNXSD2Config(_MainConfig):
    """Model config for ONNX format models based on sd-2."""

    type: Literal[ModelType.ONNX] = ModelType.ONNX
    format: Literal[ModelFormat.Onnx, ModelFormat.Olive]
    # No yaml config file for ONNX, so these are part of config
    base: Literal[BaseModelType.StableDiffusion2] = BaseModelType.StableDiffusion2
    prediction_type: SchedulerPredictionType = SchedulerPredictionType.VPrediction
    upcast_attention: bool = True


class IPAdapterConfig(ModelConfigBase):
    """Model config for IP Adaptor format models."""

    type: Literal[ModelType.IPAdapter] = ModelType.IPAdapter
    format: Literal[ModelFormat.InvokeAI]


class CLIPVisionDiffusersConfig(ModelConfigBase):
    """Model config for ClipVision."""

    type: Literal[ModelType.CLIPVision] = ModelType.CLIPVision
    format: Literal[ModelFormat.Diffusers]


class T2IConfig(ModelConfigBase):
    """Model config for T2I."""

    type: Literal[ModelType.T2IAdapter] = ModelType.T2IAdapter
    format: Literal[ModelFormat.Diffusers]


_ONNXConfig = Annotated[Union[ONNXSD1Config, ONNXSD2Config], Field(discriminator="base")]
_ControlNetConfig = Annotated[
    Union[ControlNetDiffusersConfig, ControlNetCheckpointConfig],
    Field(discriminator="format"),
]
_VaeConfig = Annotated[Union[VaeDiffusersConfig, VaeCheckpointConfig], Field(discriminator="format")]
_MainModelConfig = Annotated[Union[MainDiffusersConfig, MainCheckpointConfig], Field(discriminator="format")]

AnyModelConfig = Union[
    _MainModelConfig,
    _ONNXConfig,
    _VaeConfig,
    _ControlNetConfig,
    LoRAConfig,
    TextualInversionConfig,
    IPAdapterConfig,
    CLIPVisionDiffusersConfig,
    T2IConfig,
]

AnyModelConfigValidator = TypeAdapter(AnyModelConfig)
AnyModel = Union[ModelMixin, torch.nn.Module, IAIOnnxRuntimeModel]

# IMPLEMENTATION NOTE:
# The preferred alternative to the above is a discriminated Union as shown
# below. However, it breaks FastAPI when used as the input Body parameter in a route.
# This is a known issue. Please see:
#   https://github.com/tiangolo/fastapi/discussions/9761 and
#   https://github.com/tiangolo/fastapi/discussions/9287
# AnyModelConfig = Annotated[
#     Union[
#         _MainModelConfig,
#         _ONNXConfig,
#         _VaeConfig,
#         _ControlNetConfig,
#         LoRAConfig,
#         TextualInversionConfig,
#         IPAdapterConfig,
#         CLIPVisionDiffusersConfig,
#         T2IConfig,
#     ],
#     Field(discriminator="type"),
# ]


class ModelConfigFactory(object):
    """Class for parsing config dicts into StableDiffusion Config obects."""

    @classmethod
    def make_config(
        cls,
        model_data: Union[dict, AnyModelConfig],
        key: Optional[str] = None,
        dest_class: Optional[Type] = None,
        timestamp: Optional[float] = None
    ) -> AnyModelConfig:
        """
        Return the appropriate config object from raw dict values.

        :param model_data: A raw dict corresponding the obect fields to be
        parsed into a ModelConfigBase obect (or descendent), or a ModelConfigBase
        object, which will be passed through unchanged.
        :param dest_class: The config class to be returned. If not provided, will
        be selected automatically.
        """
        if isinstance(model_data, ModelConfigBase):
            model = model_data
        elif dest_class:
            model = dest_class.validate_python(model_data)
        else:
            model = AnyModelConfigValidator.validate_python(model_data)
        if key:
            model.key = key
        if timestamp:
            model.last_modified = timestamp
        return model