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71a1740740
- No longer install core conversion models. Use the HuggingFace cache to load them if and when needed. - Call directly into the diffusers library to perform conversions with only shallow wrappers around them to massage arguments, etc. - At root configuration time, do not create all the possible model subdirectories, but let them be created and populated at model install time. - Remove checks for missing core conversion files, since they are no longer installed.
96 lines
3.6 KiB
Python
96 lines
3.6 KiB
Python
# Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
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"""Class for StableDiffusion model loading in InvokeAI."""
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from pathlib import Path
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from typing import Optional
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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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ModelRepoVariant,
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ModelType,
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SchedulerPredictionType,
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SubModelType,
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)
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from invokeai.backend.model_manager.config import CheckpointConfigBase, MainCheckpointConfig
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from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
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from .. import ModelLoaderRegistry
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from .generic_diffusers import GenericDiffusersLoader
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Diffusers)
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@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Checkpoint)
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class StableDiffusionDiffusersModel(GenericDiffusersLoader):
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"""Class to load main models."""
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model_base_to_model_type = {
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BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",
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BaseModelType.StableDiffusion2: "FrozenOpenCLIPEmbedder",
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BaseModelType.StableDiffusionXL: "SDXL",
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BaseModelType.StableDiffusionXLRefiner: "SDXL-Refiner",
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}
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def _load_model(
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self,
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model_path: Path,
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model_variant: Optional[ModelRepoVariant] = None,
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submodel_type: Optional[SubModelType] = None,
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) -> AnyModel:
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if not submodel_type is not None:
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raise Exception("A submodel type must be provided when loading main pipelines.")
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load_class = self.get_hf_load_class(model_path, submodel_type)
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variant = model_variant.value if model_variant else None
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model_path = model_path / submodel_type.value
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result: AnyModel = load_class.from_pretrained(
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model_path,
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torch_dtype=self._torch_dtype,
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variant=variant,
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) # type: ignore
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return result
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def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
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if not isinstance(config, CheckpointConfigBase):
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return False
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elif (
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dest_path.exists()
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and (dest_path / "model_index.json").stat().st_mtime >= (config.converted_at or 0.0)
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and (dest_path / "model_index.json").stat().st_mtime >= model_path.stat().st_mtime
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):
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return False
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else:
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return True
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def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
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assert isinstance(config, MainCheckpointConfig)
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base = config.base
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config_file = config.config_path
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prediction_type = config.prediction_type.value
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upcast_attention = config.upcast_attention
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image_size = (
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1024
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if base == BaseModelType.StableDiffusionXL
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else 768
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if config.prediction_type == SchedulerPredictionType.VPrediction and base == BaseModelType.StableDiffusion2
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else 512
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)
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self._logger.info(f"Converting {model_path} to diffusers format")
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convert_ckpt_to_diffusers(
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model_path,
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output_path,
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model_type=self.model_base_to_model_type[base],
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original_config_file=self._app_config.root_path / config_file,
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extract_ema=True,
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from_safetensors=model_path.suffix == ".safetensors",
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precision=self._torch_dtype,
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prediction_type=prediction_type,
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image_size=image_size,
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upcast_attention=upcast_attention,
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load_safety_checker=False,
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)
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return output_path
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