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3e0fb45dd7
* use model_class.load_singlefile() instead of converting; works, but performance is poor * adjust the convert api - not right just yet * working, needs sql migrator update * rename migration_11 before conflict merge with main * Update invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> * Update invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py Co-authored-by: Ryan Dick <ryanjdick3@gmail.com> * implement lightweight version-by-version config migration * simplified config schema migration code * associate sdxl config with sdxl VAEs * remove use of original_config_file in load_single_file() --------- Co-authored-by: Lincoln Stein <lstein@gmail.com> Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
# Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
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"""Class for LoRA model loading in InvokeAI."""
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from logging import Logger
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from pathlib import Path
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from typing import Optional
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from invokeai.app.services.config import InvokeAIAppConfig
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from invokeai.backend.lora import LoRAModelRaw
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from invokeai.backend.model_manager import (
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AnyModel,
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AnyModelConfig,
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BaseModelType,
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ModelFormat,
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ModelType,
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SubModelType,
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)
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from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
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from .. import ModelLoader, ModelLoaderRegistry
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.Diffusers)
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.LoRA, format=ModelFormat.LyCORIS)
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class LoRALoader(ModelLoader):
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"""Class to load LoRA models."""
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# We cheat a little bit to get access to the model base
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def __init__(
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self,
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app_config: InvokeAIAppConfig,
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logger: Logger,
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ram_cache: ModelCacheBase[AnyModel],
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):
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"""Initialize the loader."""
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super().__init__(app_config, logger, ram_cache)
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self._model_base: Optional[BaseModelType] = None
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def _load_model(
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self,
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config: AnyModelConfig,
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submodel_type: Optional[SubModelType] = None,
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) -> AnyModel:
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if submodel_type is not None:
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raise ValueError("There are no submodels in a LoRA model.")
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model_path = Path(config.path)
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assert self._model_base is not None
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model = LoRAModelRaw.from_checkpoint(
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file_path=model_path,
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dtype=self._torch_dtype,
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base_model=self._model_base,
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)
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return model
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# override
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def _get_model_path(self, config: AnyModelConfig) -> Path:
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# cheating a little - we remember this variable for using in the subsequent call to _load_model()
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self._model_base = config.base
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model_base_path = self._app_config.models_path
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model_path = model_base_path / config.path
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if config.format == ModelFormat.Diffusers:
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for ext in ["safetensors", "bin"]: # return path to the safetensors file inside the folder
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path = model_base_path / config.path / f"pytorch_lora_weights.{ext}"
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if path.exists():
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model_path = path
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break
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return model_path.resolve()
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