mirror of
https://github.com/invoke-ai/InvokeAI
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125 lines
4.3 KiB
Python
125 lines
4.3 KiB
Python
import json
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import os
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from enum import Enum
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from typing import Literal, Optional
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from omegaconf import OmegaConf
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from pydantic import Field
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from ..config import MainDiffusersConfig, MainCheckpointConfig
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from .base import (
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BaseModelType,
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DiffusersModel,
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InvalidModelException,
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ModelConfigBase,
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ModelType,
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ModelVariantType,
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classproperty,
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read_checkpoint_meta,
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)
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class StableDiffusionXLModelFormat(str, Enum):
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Checkpoint = "checkpoint"
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Diffusers = "diffusers"
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class StableDiffusionXLModel(DiffusersModel):
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# TODO: check that configs overwriten properly
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class DiffusersConfig(MainDiffusersConfig):
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model_format: Literal[StableDiffusionXLModelFormat.Diffusers]
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class CheckpointConfig(ModelConfigBase):
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model_format: Literal[StableDiffusionXLModelFormat.Checkpoint]
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def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
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assert base_model in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner}
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assert model_type == ModelType.Main
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super().__init__(
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model_path=model_path,
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base_model=BaseModelType.StableDiffusionXL,
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model_type=ModelType.Main,
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)
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@classmethod
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def probe_config(cls, path: str, **kwargs):
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model_format = cls.detect_format(path)
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ckpt_config_path = kwargs.get("config", None)
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if model_format == StableDiffusionXLModelFormat.Checkpoint:
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if ckpt_config_path:
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ckpt_config = OmegaConf.load(ckpt_config_path)
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in_channels = ckpt_config["model"]["params"]["unet_config"]["params"]["in_channels"]
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else:
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checkpoint = read_checkpoint_meta(path)
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checkpoint = checkpoint.get("state_dict", checkpoint)
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in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
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elif model_format == StableDiffusionXLModelFormat.Diffusers:
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unet_config_path = os.path.join(path, "unet", "config.json")
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if os.path.exists(unet_config_path):
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with open(unet_config_path, "r") as f:
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unet_config = json.loads(f.read())
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in_channels = unet_config["in_channels"]
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else:
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raise InvalidModelException(f"{path} is not a recognized Stable Diffusion diffusers model")
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else:
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raise NotImplementedError(f"Unknown stable diffusion 2.* format: {model_format}")
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if in_channels == 9:
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variant = ModelVariantType.Inpaint
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elif in_channels == 5:
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variant = ModelVariantType.Depth
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elif in_channels == 4:
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variant = ModelVariantType.Normal
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else:
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raise Exception("Unkown stable diffusion 2.* model format")
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if ckpt_config_path is None:
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# avoid circular import
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from .stable_diffusion import _select_ckpt_config
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ckpt_config_path = _select_ckpt_config(kwargs.get("model_base", BaseModelType.StableDiffusionXL), variant)
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return cls.create_config(
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path=path,
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model_format=model_format,
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config=ckpt_config_path,
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variant=variant,
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)
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@classproperty
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def save_to_config(cls) -> bool:
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return True
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@classmethod
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def detect_format(cls, model_path: str):
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if os.path.isdir(model_path):
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return StableDiffusionXLModelFormat.Diffusers
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else:
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return StableDiffusionXLModelFormat.Checkpoint
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@classmethod
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def convert_if_required(
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cls,
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model_path: str,
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output_path: str,
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config: ModelConfigBase,
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base_model: BaseModelType,
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) -> str:
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# The convert script adapted from the diffusers package uses
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# strings for the base model type. To avoid making too many
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# source code changes, we simply translate here
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if isinstance(config, cls.CheckpointConfig):
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from invokeai.backend.model_management.models.stable_diffusion import _convert_ckpt_and_cache
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return _convert_ckpt_and_cache(
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version=base_model,
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model_config=config,
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output_path=output_path,
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use_safetensors=False, # corrupts sdxl models for some reason
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)
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else:
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return model_path
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