mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
(config) fix f-string in prompt for output location
This commit is contained in:
Eugene Brodsky
Lincoln Stein
parent
678cf8519e
commit
f237744ab1
@ -65,7 +65,7 @@ this program and resume later.\n'''
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def postscript():
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print(
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'''\n** Model Installation Successful **\nYou're all set! You may now launch InvokeAI using one of these two commands:
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Web version:
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Web version:
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python scripts/invoke.py --web (connect to http://localhost:9090)
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Command-line version:
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python scripts/invoke.py
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@ -129,7 +129,7 @@ def select_datasets(action:str):
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if action == 'customized':
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print('''
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Choose the weight file(s) you wish to download. Before downloading you
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Choose the weight file(s) you wish to download. Before downloading you
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will be given the option to view and change your selections.
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'''
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)
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@ -144,7 +144,7 @@ will be given the option to view and change your selections.
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if Datasets[ds]['recommended']:
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datasets[ds]=counter
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counter += 1
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print('The following weight files will be downloaded:')
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for ds in datasets:
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dflt = '*' if dflt is None else ''
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@ -179,7 +179,7 @@ def all_datasets()->dict:
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#-------------------------------Authenticate against Hugging Face
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def authenticate():
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print('''
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To download the Stable Diffusion weight files from the official Hugging Face
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To download the Stable Diffusion weight files from the official Hugging Face
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repository, you need to read and accept the CreativeML Responsible AI license.
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This involves a few easy steps.
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@ -212,18 +212,18 @@ This involves a few easy steps.
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access_token = HfFolder.get_token()
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if access_token is not None:
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print('found')
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if access_token is None:
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print('not found')
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print('''
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4. Thank you! The last step is to enter your HuggingFace access token so that
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this script is authorized to initiate the download. Go to the access tokens
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page of your Hugging Face account and create a token by clicking the
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page of your Hugging Face account and create a token by clicking the
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"New token" button:
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https://huggingface.co/settings/tokens
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(You can enter anything you like in the token creation field marked "Name".
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(You can enter anything you like in the token creation field marked "Name".
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"Role" should be "read").
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Now copy the token to your clipboard and paste it at the prompt. Windows
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@ -246,7 +246,7 @@ def migrate_models_ckpt():
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if rename:
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print(f'model.ckpt => {new_name}')
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os.replace(os.path.join(model_path,'model.ckpt'),os.path.join(model_path,new_name))
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#---------------------------------------------
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def download_weight_datasets(models:dict, access_token:str):
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migrate_models_ckpt()
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@ -273,9 +273,9 @@ def download_weight_datasets(models:dict, access_token:str):
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HfFolder.save_token(access_token)
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keys = ', '.join(successful.keys())
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print(f'Successfully installed {keys}')
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print(f'Successfully installed {keys}')
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return successful
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#---------------------------------------------
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def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_token:str=None)->bool:
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model_dest = os.path.join(model_dir, model_name)
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@ -286,7 +286,7 @@ def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_t
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header = {"Authorization": f'Bearer {access_token}'} if access_token else {}
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open_mode = 'wb'
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exist_size = 0
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if os.path.exists(model_dest):
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exist_size = os.path.getsize(model_dest)
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header['Range'] = f'bytes={exist_size}-'
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@ -294,7 +294,7 @@ def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_t
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resp = requests.get(url, headers=header, stream=True)
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total = int(resp.headers.get('content-length', 0))
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if resp.status_code==416: # "range not satisfiable", which means nothing to return
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print(f'* {model_name}: complete file found. Skipping.')
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return True
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@ -342,12 +342,12 @@ def download_with_progress_bar(model_url:str, model_dest:str, label:str='the'):
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print(f'Error downloading {label} model')
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print(traceback.format_exc())
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#---------------------------------------------
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def update_config_file(successfully_downloaded:dict,opt:dict):
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config_file = opt.config_file or Default_config_file
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config_file = os.path.normpath(os.path.join(Globals.root,config_file))
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yaml = new_config_file_contents(successfully_downloaded,config_file)
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try:
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@ -366,8 +366,8 @@ def update_config_file(successfully_downloaded:dict,opt:dict):
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print(f'Successfully created new configuration file {config_file}')
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#---------------------------------------------
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#---------------------------------------------
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def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str:
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if os.path.exists(config_file):
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conf = OmegaConf.load(config_file)
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@ -377,19 +377,19 @@ def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str
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# find the VAE file, if there is one
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vaes = {}
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default_selected = False
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for model in successfully_downloaded:
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a = Datasets[model]['config'].split('/')
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if a[0] != 'VAE':
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continue
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vae_target = a[1] if len(a)>1 else 'default'
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vaes[vae_target] = Datasets[model]['file']
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for model in successfully_downloaded:
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if Datasets[model]['config'].startswith('VAE'): # skip VAE entries
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continue
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stanza = conf[model] if model in conf else { }
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stanza['description'] = Datasets[model]['description']
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stanza['weights'] = os.path.join(Model_dir,Weights_dir,Datasets[model]['file'])
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stanza['config'] = os.path.normpath(os.path.join(SD_Configs, Datasets[model]['config']))
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@ -408,7 +408,7 @@ def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str
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default_selected = True
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conf[model] = stanza
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return OmegaConf.to_yaml(conf)
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#---------------------------------------------
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# this will preload the Bert tokenizer fles
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def download_bert():
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@ -478,7 +478,7 @@ def download_clipseg():
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model_url = 'https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download'
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model_dest = os.path.join(Globals.root,'models/clipseg/clipseg_weights')
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weights_zip = 'models/clipseg/weights.zip'
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if not os.path.exists(model_dest):
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os.makedirs(os.path.dirname(model_dest), exist_ok=True)
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if not os.path.exists(f'{model_dest}/rd64-uni-refined.pth'):
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@ -601,7 +601,7 @@ def select_outputs(root:str,yes_to_all:bool=False):
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#-------------------------------------
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def initialize_rootdir(root:str,yes_to_all:bool=False):
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assert os.path.exists('./configs'),'Run this script from within the InvokeAI source code directory, "InvokeAI" or the runtime directory "invokeai".'
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print(f'** INITIALIZING INVOKEAI RUNTIME DIRECTORY **')
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root_selected = False
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while not root_selected:
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@ -685,7 +685,7 @@ def initialize_rootdir(root:str,yes_to_all:bool=False):
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# -Ak_euler_a -C10.0
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#
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''')
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#-------------------------------------
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class ProgressBar():
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def __init__(self,model_name='file'):
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@ -756,7 +756,7 @@ def main():
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except Exception as e:
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print(f'\nA problem occurred during initialization.\nThe error was: "{str(e)}"')
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print(traceback.format_exc())
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#-------------------------------------
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if __name__ == '__main__':
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main()
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