chore: import upstream snapshot with attribution

invokeai/backend/model_manager/configs/t5_encoder.py

@@ -0,0 +1,82 @@
+from typing import Any, Literal, Self
+
+from pydantic import Field
+
+from invokeai.backend.model_manager.configs.base import Config_Base
+from invokeai.backend.model_manager.configs.identification_utils import (
+    NotAMatchError,
+    raise_for_class_name,
+    raise_for_override_fields,
+    raise_if_not_dir,
+    state_dict_has_any_keys_ending_with,
+)
+from invokeai.backend.model_manager.model_on_disk import ModelOnDisk
+from invokeai.backend.model_manager.taxonomy import BaseModelType, ModelFormat, ModelType
+
+
+class T5Encoder_T5Encoder_Config(Config_Base):
+    """Configuration for T5 Encoder models in a bespoke, diffusers-like format. The model weights are expected to be in
+    a folder called text_encoder_2 inside the model directory, with a config file named model.safetensors.index.json."""
+
+    base: Literal[BaseModelType.Any] = Field(default=BaseModelType.Any)
+    type: Literal[ModelType.T5Encoder] = Field(default=ModelType.T5Encoder)
+    format: Literal[ModelFormat.T5Encoder] = Field(default=ModelFormat.T5Encoder)
+    cpu_only: bool | None = Field(default=None, description="Whether this model should run on CPU only")
+
+    @classmethod
+    def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self:
+        raise_if_not_dir(mod)
+
+        raise_for_override_fields(cls, override_fields)
+
+        expected_config_path = mod.path / "text_encoder_2" / "config.json"
+        expected_class_name = "T5EncoderModel"
+        raise_for_class_name(expected_config_path, expected_class_name)
+
+        cls.raise_if_doesnt_have_unquantized_config_file(mod)
+
+        return cls(**override_fields)
+
+    @classmethod
+    def raise_if_doesnt_have_unquantized_config_file(cls, mod: ModelOnDisk) -> None:
+        has_unquantized_config = (mod.path / "text_encoder_2" / "model.safetensors.index.json").exists()
+
+        if not has_unquantized_config:
+            raise NotAMatchError("missing text_encoder_2/model.safetensors.index.json")
+
+
+class T5Encoder_BnBLLMint8_Config(Config_Base):
+    """Configuration for T5 Encoder models quantized by bitsandbytes' LLM.int8."""
+
+    base: Literal[BaseModelType.Any] = Field(default=BaseModelType.Any)
+    type: Literal[ModelType.T5Encoder] = Field(default=ModelType.T5Encoder)
+    format: Literal[ModelFormat.BnbQuantizedLlmInt8b] = Field(default=ModelFormat.BnbQuantizedLlmInt8b)
+    cpu_only: bool | None = Field(default=None, description="Whether this model should run on CPU only")
+
+    @classmethod
+    def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self:
+        raise_if_not_dir(mod)
+
+        raise_for_override_fields(cls, override_fields)
+
+        expected_config_path = mod.path / "text_encoder_2" / "config.json"
+        expected_class_name = "T5EncoderModel"
+        raise_for_class_name(expected_config_path, expected_class_name)
+
+        cls.raise_if_filename_doesnt_look_like_bnb_quantized(mod)
+
+        cls.raise_if_state_dict_doesnt_look_like_bnb_quantized(mod)
+
+        return cls(**override_fields)
+
+    @classmethod
+    def raise_if_filename_doesnt_look_like_bnb_quantized(cls, mod: ModelOnDisk) -> None:
+        filename_looks_like_bnb = any(x for x in mod.weight_files() if "llm_int8" in x.as_posix())
+        if not filename_looks_like_bnb:
+            raise NotAMatchError("filename does not look like bnb quantized llm_int8")
+
+    @classmethod
+    def raise_if_state_dict_doesnt_look_like_bnb_quantized(cls, mod: ModelOnDisk) -> None:
+        has_scb_key_suffix = state_dict_has_any_keys_ending_with(mod.load_state_dict(), "SCB")
+        if not has_scb_key_suffix:
+            raise NotAMatchError("state dict does not look like bnb quantized llm_int8")