mirror of
https://github.com/invoke-ai/InvokeAI.git
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a23dedd2ee
- Replace legacy model manager service with the v2 manager. - Update invocations to use new load interface. - Fixed many but not all type checking errors in the invocations. Most were unrelated to model manager - Updated routes. All the new routes live under the route tag `model_manager_v2`. To avoid confusion with the old routes, they have the URL prefix `/api/v2/models`. The old routes have been de-registered. - Added a pytest for the loader. - Updated documentation in contributing/MODEL_MANAGER.md
63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
# Copyright (c) 2024, Lincoln D. Stein and the InvokeAI Development Team
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"""Class for ControlNet model loading in InvokeAI."""
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from pathlib import Path
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import safetensors
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import torch
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from invokeai.backend.model_manager import (
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AnyModelConfig,
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BaseModelType,
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ModelFormat,
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ModelType,
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)
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from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_controlnet_to_diffusers
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from invokeai.backend.model_manager.load.load_base import AnyModelLoader
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from .generic_diffusers import GenericDiffusersLoader
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@AnyModelLoader.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Diffusers)
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@AnyModelLoader.register(base=BaseModelType.Any, type=ModelType.ControlNet, format=ModelFormat.Checkpoint)
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class ControlnetLoader(GenericDiffusersLoader):
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"""Class to load ControlNet models."""
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def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
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if config.format != ModelFormat.Checkpoint:
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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 / "config.json").stat().st_mtime >= (config.last_modified or 0.0)
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and (dest_path / "config.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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if config.base not in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
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raise Exception(f"Vae conversion not supported for model type: {config.base}")
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else:
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assert hasattr(config, "config")
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config_file = config.config
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if model_path.suffix == ".safetensors":
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checkpoint = safetensors.torch.load_file(model_path, device="cpu")
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else:
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checkpoint = torch.load(model_path, map_location="cpu")
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# sometimes weights are hidden under "state_dict", and sometimes not
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if "state_dict" in checkpoint:
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checkpoint = checkpoint["state_dict"]
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convert_controlnet_to_diffusers(
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model_path,
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output_path,
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original_config_file=self._app_config.root_path / config_file,
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image_size=512,
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scan_needed=True,
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from_safetensors=model_path.suffix == ".safetensors",
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)
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return output_path
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