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83 lines
3.6 KiB
Python
83 lines
3.6 KiB
Python
from typing import Any, Literal, Self
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from pydantic import Field
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from invokeai.backend.model_manager.configs.base import Config_Base
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from invokeai.backend.model_manager.configs.identification_utils import (
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NotAMatchError,
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raise_for_class_name,
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raise_for_override_fields,
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raise_if_not_dir,
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state_dict_has_any_keys_ending_with,
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)
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from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
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from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType
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class T5Encoder_T5Encoder_Config(Config_Base):
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"""Configuration for T5 Encoder models in a bespoke, diffusers-like format. The model weights are expected to be in
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a folder called text_encoder_2 inside the model directory, with a config file named model.safetensors.index.json."""
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base: Literal[BaseModelType.Any] = Field(default=BaseModelType.Any)
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type: Literal[ModelType.T5Encoder] = Field(default=ModelType.T5Encoder)
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format: Literal[ModelFormat.T5Encoder] = Field(default=ModelFormat.T5Encoder)
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cpu_only: bool | None = Field(default=None, description="Whether this model should run on CPU only")
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@classmethod
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def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self:
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raise_if_not_dir(mod)
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raise_for_override_fields(cls, override_fields)
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expected_config_path = mod.path / "text_encoder_2" / "config.json"
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expected_class_name = "T5EncoderModel"
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raise_for_class_name(expected_config_path, expected_class_name)
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cls.raise_if_doesnt_have_unquantized_config_file(mod)
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return cls(**override_fields)
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@classmethod
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def raise_if_doesnt_have_unquantized_config_file(cls, mod: ModelOnDisk) -> None:
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has_unquantized_config = (mod.path / "text_encoder_2" / "model.safetensors.index.json").exists()
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if not has_unquantized_config:
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raise NotAMatchError("missing text_encoder_2/model.safetensors.index.json")
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class T5Encoder_BnBLLMint8_Config(Config_Base):
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"""Configuration for T5 Encoder models quantized by bitsandbytes' LLM.int8."""
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base: Literal[BaseModelType.Any] = Field(default=BaseModelType.Any)
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type: Literal[ModelType.T5Encoder] = Field(default=ModelType.T5Encoder)
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format: Literal[ModelFormat.BnbQuantizedLlmInt8b] = Field(default=ModelFormat.BnbQuantizedLlmInt8b)
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cpu_only: bool | None = Field(default=None, description="Whether this model should run on CPU only")
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@classmethod
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def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self:
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raise_if_not_dir(mod)
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raise_for_override_fields(cls, override_fields)
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expected_config_path = mod.path / "text_encoder_2" / "config.json"
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expected_class_name = "T5EncoderModel"
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raise_for_class_name(expected_config_path, expected_class_name)
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cls.raise_if_filename_doesnt_look_like_bnb_quantized(mod)
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cls.raise_if_state_dict_doesnt_look_like_bnb_quantized(mod)
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return cls(**override_fields)
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@classmethod
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def raise_if_filename_doesnt_look_like_bnb_quantized(cls, mod: ModelOnDisk) -> None:
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filename_looks_like_bnb = any(x for x in mod.weight_files() if "llm_int8" in x.as_posix())
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if not filename_looks_like_bnb:
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raise NotAMatchError("filename does not look like bnb quantized llm_int8")
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@classmethod
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def raise_if_state_dict_doesnt_look_like_bnb_quantized(cls, mod: ModelOnDisk) -> None:
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has_scb_key_suffix = state_dict_has_any_keys_ending_with(mod.load_state_dict(), "SCB")
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if not has_scb_key_suffix:
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raise NotAMatchError("state dict does not look like bnb quantized llm_int8")
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