mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
import os
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import sys
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import torch
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from argparse import Namespace
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from invokeai.backend import Args
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from omegaconf import OmegaConf
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from pathlib import Path
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import invokeai.version
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from ...backend import ModelManager
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from ...backend.util import choose_precision, choose_torch_device
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from ...backend import Globals
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# TODO: Replace with an abstract class base ModelManagerBase
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def get_model_manager(config: Args) -> ModelManager:
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if not config.conf:
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config_file = os.path.join(Globals.root, "configs", "models.yaml")
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if not os.path.exists(config_file):
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report_model_error(
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config, FileNotFoundError(f"The file {config_file} could not be found.")
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)
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print(f">> {invokeai.version.__app_name__}, version {invokeai.version.__version__}")
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print(f'>> InvokeAI runtime directory is "{Globals.root}"')
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# these two lines prevent a horrible warning message from appearing
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# when the frozen CLIP tokenizer is imported
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import transformers # type: ignore
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transformers.logging.set_verbosity_error()
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import diffusers
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diffusers.logging.set_verbosity_error()
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# normalize the config directory relative to root
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if not os.path.isabs(config.conf):
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config.conf = os.path.normpath(os.path.join(Globals.root, config.conf))
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if config.embeddings:
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if not os.path.isabs(config.embedding_path):
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embedding_path = os.path.normpath(
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os.path.join(Globals.root, config.embedding_path)
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)
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else:
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embedding_path = config.embedding_path
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else:
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embedding_path = None
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# migrate legacy models
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ModelManager.migrate_models()
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# creating the model manager
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try:
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device = torch.device(choose_torch_device())
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precision = 'float16' if config.precision=='float16' \
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else 'float32' if config.precision=='float32' \
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else choose_precision(device)
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model_manager = ModelManager(
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OmegaConf.load(config.conf),
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precision=precision,
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device_type=device,
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max_loaded_models=config.max_loaded_models,
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embedding_path = Path(embedding_path),
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)
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except (FileNotFoundError, TypeError, AssertionError) as e:
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report_model_error(config, e)
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except (IOError, KeyError) as e:
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print(f"{e}. Aborting.")
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sys.exit(-1)
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# try to autoconvert new models
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# autoimport new .ckpt files
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if path := config.autoconvert:
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model_manager.autoconvert_weights(
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conf_path=config.conf,
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weights_directory=path,
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)
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return model_manager
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def report_model_error(opt: Namespace, e: Exception):
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print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
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print(
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"** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models."
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)
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yes_to_all = os.environ.get("INVOKE_MODEL_RECONFIGURE")
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if yes_to_all:
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print(
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"** Reconfiguration is being forced by environment variable INVOKE_MODEL_RECONFIGURE"
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)
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else:
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response = input(
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"Do you want to run invokeai-configure script to select and/or reinstall models? [y] "
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)
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if response.startswith(("n", "N")):
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return
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print("invokeai-configure is launching....\n")
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# Match arguments that were set on the CLI
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# only the arguments accepted by the configuration script are parsed
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root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
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config = ["--config", opt.conf] if opt.conf is not None else []
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previous_config = sys.argv
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sys.argv = ["invokeai-configure"]
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sys.argv.extend(root_dir)
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sys.argv.extend(config.to_dict())
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if yes_to_all is not None:
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for arg in yes_to_all.split():
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sys.argv.append(arg)
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from invokeai.frontend.install import invokeai_configure
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invokeai_configure()
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# TODO: Figure out how to restart
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# print('** InvokeAI will now restart')
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# sys.argv = previous_args
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# main() # would rather do a os.exec(), but doesn't exist?
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# sys.exit(0)
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