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chore: import upstream snapshot with attribution
2026-07-13 13:22:06 +08:00

36 lines
1.4 KiB
Python

from pathlib import Path
from typing import Optional
from transformers import CLIPVisionModelWithProjection
from invokeai.backend.model_manager.configs.base import Diffusers_Config_Base
from invokeai.backend.model_manager.configs.factory import AnyModelConfig
from invokeai.backend.model_manager.load.load_default import ModelLoader
from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
from invokeai.backend.model_manager.taxonomy import AnyModel, BaseModelType, ModelFormat, ModelType, SubModelType
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)
class ClipVisionLoader(ModelLoader):
"""Class to load CLIPVision models."""
def _load_model(
self,
config: AnyModelConfig,
submodel_type: Optional[SubModelType] = None,
) -> AnyModel:
if not isinstance(config, Diffusers_Config_Base):
raise ValueError("Only DiffusersConfigBase models are currently supported here.")
if submodel_type is not None:
raise Exception("There are no submodels in CLIP Vision models.")
model_path = Path(config.path)
model = CLIPVisionModelWithProjection.from_pretrained(
model_path, torch_dtype=self._torch_dtype, local_files_only=True
)
assert isinstance(model, CLIPVisionModelWithProjection)
return model