mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
import os
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import typing
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from enum import Enum
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from typing import Literal, Optional
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import torch
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from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus, build_ip_adapter
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from invokeai.backend.model_management.models.base import (
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BaseModelType,
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InvalidModelException,
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ModelBase,
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ModelConfigBase,
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ModelType,
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SubModelType,
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classproperty,
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)
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class IPAdapterModelFormat(str, Enum):
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# The custom IP-Adapter model format defined by InvokeAI.
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InvokeAI = "invokeai"
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class IPAdapterModel(ModelBase):
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class InvokeAIConfig(ModelConfigBase):
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model_format: Literal[IPAdapterModelFormat.InvokeAI]
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def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
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assert model_type == ModelType.IPAdapter
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super().__init__(model_path, base_model, model_type)
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self.model_size = os.path.getsize(self.model_path)
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@classmethod
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def detect_format(cls, path: str) -> str:
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if not os.path.exists(path):
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raise ModuleNotFoundError(f"No IP-Adapter model at path '{path}'.")
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if os.path.isdir(path):
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model_file = os.path.join(path, "ip_adapter.bin")
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image_encoder_config_file = os.path.join(path, "image_encoder.txt")
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if os.path.exists(model_file) and os.path.exists(image_encoder_config_file):
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return IPAdapterModelFormat.InvokeAI
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raise InvalidModelException(f"Unexpected IP-Adapter model format: {path}")
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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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def get_size(self, child_type: Optional[SubModelType] = None) -> int:
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if child_type is not None:
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raise ValueError("There are no child models in an IP-Adapter model.")
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return self.model_size
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def get_model(
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self,
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torch_dtype: Optional[torch.dtype],
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child_type: Optional[SubModelType] = None,
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) -> typing.Union[IPAdapter, IPAdapterPlus]:
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if child_type is not None:
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raise ValueError("There are no child models in an IP-Adapter model.")
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return build_ip_adapter(
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ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), device="cpu", dtype=torch_dtype
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)
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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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format = cls.detect_format(model_path)
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if format == IPAdapterModelFormat.InvokeAI:
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return model_path
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else:
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raise ValueError(f"Unsupported format: '{format}'.")
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def get_ip_adapter_image_encoder_model_id(model_path: str):
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"""Read the ID of the image encoder associated with the IP-Adapter at `model_path`."""
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image_encoder_config_file = os.path.join(model_path, "image_encoder.txt")
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with open(image_encoder_config_file, "r") as f:
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image_encoder_model = f.readline().strip()
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return image_encoder_model
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