mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Multiple fixes
1. Model installer works correctly under Windows 11 Terminal
2. Fixed crash when configure script hands control off to installer
3. Kill install subprocess on keyboard interrupt
4. Command-line functionality for --yes configuration and model installation
restored.
5. New command-line features:
- install/delete lists of diffusers, LoRAS, controlnets and textual inversions
using repo ids, paths or URLs.
Help:
```
usage: invokeai-model-install [-h] [--diffusers [DIFFUSERS ...]] [--loras [LORAS ...]] [--controlnets [CONTROLNETS ...]] [--textual-inversions [TEXTUAL_INVERSIONS ...]] [--delete] [--full-precision | --no-full-precision]
[--yes] [--default_only] [--list-models {diffusers,loras,controlnets,tis}] [--config_file CONFIG_FILE] [--root_dir ROOT]
InvokeAI model downloader
options:
-h, --help show this help message and exit
--diffusers [DIFFUSERS ...]
List of URLs or repo_ids of diffusers to install/delete
--loras [LORAS ...] List of URLs or repo_ids of LoRA/LyCORIS models to install/delete
--controlnets [CONTROLNETS ...]
List of URLs or repo_ids of controlnet models to install/delete
--textual-inversions [TEXTUAL_INVERSIONS ...]
List of URLs or repo_ids of textual inversion embeddings to install/delete
--delete Delete models listed on command line rather than installing them
--full-precision, --no-full-precision
use 32-bit weights instead of faster 16-bit weights (default: False)
--yes, -y answer "yes" to all prompts
--default_only only install the default model
--list-models {diffusers,loras,controlnets,tis}
list installed models
--config_file CONFIG_FILE, -c CONFIG_FILE
path to configuration file to create
--root_dir ROOT path to root of install directory
```
This commit is contained in:
Lincoln Stein
@ -360,7 +360,7 @@ class editOptsForm(npyscreen.FormMultiPage):
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self.outdir = self.add_widget_intelligent(
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npyscreen.TitleFilename,
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name="(<tab> autocompletes, ctrl-N advances):",
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value=str(old_opts.outdir) or str(default_output_dir()),
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value=str(default_output_dir()),
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select_dir=True,
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must_exist=False,
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use_two_lines=False,
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@ -642,9 +642,6 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
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def default_startup_options(init_file: Path) -> Namespace:
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opts = InvokeAIAppConfig(argv=[])
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outdir = Path(opts.outdir)
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if not outdir.is_absolute():
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opts.outdir = str(config.root / opts.outdir)
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if not init_file.exists():
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opts.nsfw_checker = True
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return opts
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@ -690,7 +687,8 @@ def run_console_ui(
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) -> (Namespace, Namespace):
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# parse_args() will read from init file if present
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invokeai_opts = default_startup_options(initfile)
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invokeai_opts.root = program_opts.root
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set_min_terminal_size(MIN_COLS, MIN_LINES)
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# the install-models application spawns a subprocess to install
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@ -711,15 +709,16 @@ def write_opts(opts: Namespace, init_file: Path):
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"""
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Update the invokeai.yaml file with values from current settings.
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"""
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# this will load current settings
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config = InvokeAIAppConfig(argv=[])
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# this will load default settings
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new_config = InvokeAIAppConfig(argv=[])
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new_config.root = config.root
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for key,value in opts.__dict__.items():
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if hasattr(config,key):
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setattr(config,key,value)
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if hasattr(new_config,key):
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setattr(new_config,key,value)
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with open(init_file,'w', encoding='utf-8') as file:
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file.write(config.to_yaml())
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file.write(new_config.to_yaml())
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# -------------------------------------
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def default_output_dir() -> Path:
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@ -823,9 +822,12 @@ def main():
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)
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opt = parser.parse_args()
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# setting a global here
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global config
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config.root = Path(os.path.expanduser(get_root(opt.root) or ""))
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invoke_args = []
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if opt.root:
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invoke_args.extend(['--root',opt.root])
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if opt.full_precision:
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invoke_args.extend(['--precision','float32'])
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config.parse_args(invoke_args)
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errors = set()
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@ -838,8 +840,7 @@ def main():
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if old_init_file.exists() and not new_init_file.exists():
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print('** Migrating invokeai.init to invokeai.yaml')
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migrate_init_file(old_init_file)
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config = get_invokeai_config(argv=[]) # reread defaults
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config.parse_args([]) # reread defaults
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if not config.model_conf_path.exists():
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initialize_rootdir(config.root, opt.yes_to_all)
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@ -862,7 +863,7 @@ def main():
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if opt.skip_support_models:
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print("\n** SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST **")
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else:
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print("\n** DOWNLOADING SUPPORT MODELS **")
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print("\n** CHECKING/UPDATING SUPPORT MODELS **")
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download_bert()
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download_sd1_clip()
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download_sd2_clip()
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@ -876,6 +877,7 @@ def main():
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if opt.skip_sd_weights:
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print("\n** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
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elif models_to_download:
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print(models_to_download)
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print("\n** DOWNLOADING DIFFUSION WEIGHTS **")
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process_and_execute(opt, models_to_download)
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@ -53,8 +53,8 @@ Config_preamble = """
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@dataclass
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class ModelInstallList:
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'''Class for listing models to be installed/removed'''
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install_models: List[str]
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remove_models: List[str]
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install_models: List[str] = field(default_factory=list)
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remove_models: List[str] = field(default_factory=list)
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@dataclass
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class UserSelections():
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@ -108,36 +108,40 @@ def install_requested_models(
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# prevent circular import here
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from ..model_management import ModelManager
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model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
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model_manager.install_controlnet_models(controlnet.install_models, access_token=access_token)
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model_manager.delete_controlnet_models(controlnet.remove_models)
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if controlnet:
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model_manager.install_controlnet_models(controlnet.install_models, access_token=access_token)
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model_manager.delete_controlnet_models(controlnet.remove_models)
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model_manager.install_lora_models(lora.install_models, access_token=access_token)
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model_manager.delete_lora_models(lora.remove_models)
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if lora:
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model_manager.install_lora_models(lora.install_models, access_token=access_token)
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model_manager.delete_lora_models(lora.remove_models)
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model_manager.install_ti_models(ti.install_models, access_token=access_token)
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model_manager.delete_ti_models(ti.remove_models)
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if ti:
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model_manager.install_ti_models(ti.install_models, access_token=access_token)
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model_manager.delete_ti_models(ti.remove_models)
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# TODO: Replace next three paragraphs with calls into new model manager
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if diffusers.remove_models and len(diffusers.remove_models) > 0:
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logger.info("Processing requested deletions")
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for model in diffusers.remove_models:
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logger.info(f"{model}...")
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model_manager.del_model(model, delete_files=purge_deleted)
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model_manager.commit(config_file_path)
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if diffusers:
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# TODO: Replace next three paragraphs with calls into new model manager
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if diffusers.remove_models and len(diffusers.remove_models) > 0:
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logger.info("Processing requested deletions")
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for model in diffusers.remove_models:
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logger.info(f"{model}...")
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model_manager.del_model(model, delete_files=purge_deleted)
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model_manager.commit(config_file_path)
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if diffusers.install_models and len(diffusers.install_models) > 0:
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logger.info("Installing requested models")
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downloaded_paths = download_weight_datasets(
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models=diffusers.install_models,
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access_token=None,
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precision=precision,
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)
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successful = {x:v for x,v in downloaded_paths.items() if v is not None}
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if len(successful) > 0:
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update_config_file(successful, config_file_path)
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if len(successful) < len(diffusers.install_models):
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unsuccessful = [x for x in downloaded_paths if downloaded_paths[x] is None]
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logger.warning(f"Some of the model downloads were not successful: {unsuccessful}")
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if diffusers.install_models and len(diffusers.install_models) > 0:
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logger.info("Installing requested models")
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downloaded_paths = download_weight_datasets(
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models=diffusers.install_models,
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access_token=None,
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precision=precision,
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)
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successful = {x:v for x,v in downloaded_paths.items() if v is not None}
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if len(successful) > 0:
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update_config_file(successful, config_file_path)
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if len(successful) < len(diffusers.install_models):
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unsuccessful = [x for x in downloaded_paths if downloaded_paths[x] is None]
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logger.warning(f"Some of the model downloads were not successful: {unsuccessful}")
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# due to above, we have to reload the model manager because conf file
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# was changed behind its back
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@ -188,21 +192,20 @@ def yes_or_no(prompt: str, default_yes=True):
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return response[0] in ("y", "Y")
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# ---------------------------------------------
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def recommended_datasets() -> dict:
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datasets = dict()
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def recommended_datasets() -> List['str']:
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datasets = set()
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for ds in initial_models().keys():
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if initial_models()[ds].get("recommended", False):
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datasets[ds] = True
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return datasets
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datasets.add(ds)
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return list(datasets)
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# ---------------------------------------------
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def default_dataset() -> dict:
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datasets = dict()
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datasets = set()
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for ds in initial_models().keys():
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if initial_models()[ds].get("default", False):
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datasets[ds] = True
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return datasets
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datasets.add(ds)
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return list(datasets)
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# ---------------------------------------------
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@ -274,6 +277,9 @@ def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
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def download_from_hf(
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model_class: object, model_name: str, **kwargs
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):
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logger = InvokeAILogger.getLogger('InvokeAI')
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logger.addFilter(lambda x: 'fp16 is not a valid' not in x.getMessage())
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path = config.cache_dir
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model = model_class.from_pretrained(
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model_name,
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