implemented tabbed model selection; not wired to backend yet

invokeai/backend/install/model_install_backend.py

@@ -6,6 +6,7 @@ import re
 import shutil
 import sys
 import warnings
+from dataclasses import dataclass
 from pathlib import Path
 from tempfile import TemporaryFile
 from typing import List, Dict
@@ -20,9 +21,8 @@ from tqdm import tqdm
 import invokeai.configs as configs
 
 from invokeai.app.services.config import get_invokeai_config
-from ..model_management import ModelManager
 from ..stable_diffusion import StableDiffusionGeneratorPipeline
-
+from ..util.logging import InvokeAILogger
 
 warnings.filterwarnings("ignore")
 
@@ -37,6 +37,9 @@ Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
 # initial models omegaconf
 Datasets = None
 
+# logger
+logger = InvokeAILogger.getLogger(name='InvokeAI')
+
 Config_preamble = """
 # This file describes the alternative machine learning models
 # available to InvokeAI script.
@@ -47,6 +50,11 @@ Config_preamble = """
 # was trained on.
 """
 
+@dataclass
+class ModelInstallList:
+    '''Class for listing models to be installed/removed'''
+    install_models: List[str]
+    remove_models: List[str]
 
 def default_config_file():
     return config.model_conf_path
@@ -61,11 +69,11 @@ def initial_models():
     return (Datasets := OmegaConf.load(Dataset_path)['diffusers'])
 
 def install_requested_models(
-        install_initial_models: List[str] = None,
+        diffusers: ModelInstallList = None,
-        remove_models: List[str] = None,
+        controlnet: ModelInstallList = None,
-        install_cn_models: List[str] = None,
+        lora: ModelInstallList = None,
-        remove_cn_models: List[str] = None,
+        ti: ModelInstallList = None,
-        cn_model_map: Dict[str,str] = None,
+        cn_model_map: Dict[str,str] = None, # temporary - move to model manager
         scan_directory: Path = None,
         external_models: List[str] = None,
         scan_at_startup: bool = False,
@@ -81,33 +89,30 @@ def install_requested_models(
     if not config_file_path.exists():
         open(config_file_path, "w")
 
-    install_controlnet_models(
+    # prevent circular import here
-        install_cn_models,
+    from ..model_management import ModelManager
-        short_name_map = cn_model_map,
-        precision=precision,
-        access_token=access_token,
-    )
-    delete_controlnet_models(remove_cn_models)
-
     model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
+    model_manager.install_controlnet_models(controlnet.install_models, access_token=access_token)
+    model_manager.delete_controlnet_models(controlnet.remove_models)
 
-    if remove_models and len(remove_models) > 0:
+    # TODO: Replace next three paragraphs with calls into new model manager
-        print("== DELETING UNCHECKED STARTER MODELS ==")
+    if diffusers.remove_models and len(diffusers.remove_models) > 0:
-        for model in remove_models:
+        logger.info("DELETING UNCHECKED STARTER MODELS")
-            print(f"{model}...")
+        for model in diffusers.remove_models:
+            logger.info(f"{model}...")
             model_manager.del_model(model, delete_files=purge_deleted)
         model_manager.commit(config_file_path)
 
-    if install_initial_models and len(install_initial_models) > 0:
+    if diffusers.install_models and len(diffusers.install_models) > 0:
-        print("== INSTALLING SELECTED STARTER MODELS ==")
+        logger.info("INSTALLING SELECTED STARTER MODELS")
         successfully_downloaded = download_weight_datasets(
-            models=install_initial_models,
+            models=diffusers.install_initial_models,
             access_token=None,
             precision=precision,
         )  # FIX: for historical reasons, we don't use model manager here
         update_config_file(successfully_downloaded, config_file_path)
-        if len(successfully_downloaded) < len(install_initial_models):
+        if len(successfully_downloaded) < len(diffusers.install_models):
-            print("** Some of the model downloads were not successful")
+            logger.warning("Some of the model downloads were not successful")
 
     # due to above, we have to reload the model manager because conf file
     # was changed behind its back
@@ -118,7 +123,7 @@ def install_requested_models(
         external_models.append(str(scan_directory))
 
     if len(external_models) > 0:
-        print("== INSTALLING EXTERNAL MODELS ==")
+        logger.info("INSTALLING EXTERNAL MODELS")
         for path_url_or_repo in external_models:
             try:
                 model_manager.heuristic_import(
@@ -190,10 +195,10 @@ def migrate_models_ckpt():
     if not os.path.exists(os.path.join(model_path, "model.ckpt")):
         return
     new_name = initial_models()["stable-diffusion-1.4"]["file"]
-    print(
+    logger.warning(
         'The Stable Diffusion v4.1 "model.ckpt" is already installed. The name will be changed to {new_name} to avoid confusion.'
     )
-    print(f"model.ckpt => {new_name}")
+    logger.warning(f"model.ckpt => {new_name}")
     os.replace(
         os.path.join(model_path, "model.ckpt"), os.path.join(model_path, new_name)
     )
@@ -206,7 +211,7 @@ def download_weight_datasets(
     migrate_models_ckpt()
     successful = dict()
     for mod in models:
-        print(f"Downloading {mod}:")
+        logger.info(f"Downloading {mod}:")
         successful[mod] = _download_repo_or_file(
             initial_models()[mod], access_token, precision=precision
         )
@@ -240,68 +245,6 @@ def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
     )
 
 
-# ---------------------------------------------
-def install_controlnet_models(
-        short_names: List[str],
-        short_name_map: Dict[str,str],
-        precision: str='float16',
-        access_token: str = None,
-):
-    '''
-    Download list of controlnet models, using their HuggingFace
-    repo_ids.
-    '''
-    dest_dir = config.controlnet_path
-    if not dest_dir.exists():
-        dest_dir.mkdir(parents=True,exist_ok=False)
-
-    # The model file may be fp32 or fp16, and may be either a
-    # .bin file or a .safetensors. We try each until we get one,
-    # preferring 'fp16' if using half precision, and preferring
-    # safetensors over over bin.
-    precisions = ['.fp16',''] if precision=='float16' else ['']
-    formats = ['.safetensors','.bin']
-    possible_filenames = list()
-    for p in precisions:
-        for f in formats:
-            possible_filenames.append(Path(f'diffusion_pytorch_model{p}{f}'))
-        
-    for directory_name in short_names:
-        repo_id = short_name_map[directory_name]
-        safe_name = directory_name.replace('/','--')
-        print(f'Downloading ControlNet model {directory_name} ({repo_id})')
-        hf_download_with_resume(
-            repo_id = repo_id,
-            model_dir = dest_dir / safe_name,
-            model_name = 'config.json',
-            access_token = access_token
-        )
-
-        path = None
-        for filename in possible_filenames:
-            suffix = filename.suffix
-            dest_filename = Path(f'diffusion_pytorch_model{suffix}')
-            print(f'Probing {directory_name}/{filename}...')
-            path = hf_download_with_resume(
-                repo_id = repo_id,
-                model_dir = dest_dir / safe_name,
-                model_name = str(filename),
-                access_token = access_token,
-                model_dest = Path(dest_dir, safe_name, dest_filename),
-            )
-            if path:
-                (path.parent / '.download_complete').touch()
-                break
-    
-# ---------------------------------------------
-def delete_controlnet_models(short_names: List[str]):
-    for name in short_names:
-        safe_name = name.replace('/','--')
-        directory = config.controlnet_path / safe_name
-        if directory.exists():
-            print(f'Purging controlnet model {name}')
-            shutil.rmtree(str(directory))
-            
 # ---------------------------------------------
 def download_from_hf(
     model_class: object, model_name: str, **kwargs
@@ -340,7 +283,7 @@ def _download_diffusion_weights(
             if str(e).startswith("fp16 is not a valid"):
                 pass
             else:
-                print(f"An unexpected error occurred while downloading the model: {e})")
+                logger.error(f"An unexpected error occurred while downloading the model: {e})")
         if path:
             break
     return path
@@ -374,17 +317,17 @@ def hf_download_with_resume(
     if (
         resp.status_code == 416
     ):  # "range not satisfiable", which means nothing to return
-        print(f"* {model_name}: complete file found. Skipping.")
+        logger.info(f"{model_name}: complete file found. Skipping.")
         return model_dest
     elif resp.status_code == 404:
-        print("** File not found")
+        logger.warning("File not found")
         return None
     elif resp.status_code != 200:
-        print(f"** Warning: {model_name}: {resp.reason}")
+        logger.warning(f"{model_name}: {resp.reason}")
     elif exist_size > 0:
-        print(f"* {model_name}: partial file found. Resuming...")
+        logger.info(f"{model_name}: partial file found. Resuming...")
     else:
-        print(f"* {model_name}: Downloading...")
+        logger.info(f"{model_name}: Downloading...")
 
     try:
         with open(model_dest, open_mode) as file, tqdm(
@@ -399,7 +342,7 @@ def hf_download_with_resume(
                 size = file.write(data)
                 bar.update(size)
     except Exception as e:
-        print(f"An error occurred while downloading {model_name}: {str(e)}")
+        logger.error(f"An error occurred while downloading {model_name}: {str(e)}")
         return None
     return model_dest
 
@@ -424,8 +367,8 @@ def update_config_file(successfully_downloaded: dict, config_file: Path):
     try:
         backup = None
         if os.path.exists(config_file):
-            print(
+            logger.warning(
-                f"** {config_file.name} exists. Renaming to {config_file.stem}.yaml.orig"
+                f"{config_file.name} exists. Renaming to {config_file.stem}.yaml.orig"
             )
             backup = config_file.with_suffix(".yaml.orig")
             ## Ugh. Windows is unable to overwrite an existing backup file, raises a WinError 183
@@ -442,16 +385,16 @@ def update_config_file(successfully_downloaded: dict, config_file: Path):
                 new_config.write(tmp.read())
 
     except Exception as e:
-        print(f"**Error creating config file {config_file}: {str(e)} **")
+        logger.error(f"Error creating config file {config_file}: {str(e)}")
         if backup is not None:
-            print("restoring previous config file")
+            logger.info("restoring previous config file")
             ## workaround, for WinError 183, see above
             if sys.platform == "win32" and config_file.is_file():
                 config_file.unlink()
             backup.rename(config_file)
         return
-
+    
-    print(f"Successfully created new configuration file {config_file}")
+    logger.info(f"Successfully created new configuration file {config_file}")
 
 
 # ---------------------------------------------
@@ -518,8 +461,8 @@ def delete_weights(model_name: str, conf_stanza: dict):
     if re.match("/VAE/", conf_stanza.get("config")):
         return
 
-    print(
+    logger.warning(
-        f"\n** The checkpoint version of {model_name} is superseded by the diffusers version. Deleting the original file {weights}?"
+        f"\nThe checkpoint version of {model_name} is superseded by the diffusers version. Deleting the original file {weights}?"
     )
 
     weights = Path(weights)
@@ -528,4 +471,4 @@ def delete_weights(model_name: str, conf_stanza: dict):
         try:
             weights.unlink()
         except OSError as e:
-            print(str(e))
+            logger.error(str(e))

invokeai/backend/model_management/model_manager.py

@@ -11,6 +11,7 @@ import gc
 import hashlib
 import os
 import re
+import shutil
 import sys
 import textwrap
 import time
@@ -49,6 +50,10 @@ from ..stable_diffusion import (
     StableDiffusionGeneratorPipeline,
 )
 from invokeai.app.services.config import get_invokeai_config
+from ..install.model_install_backend import (
+    Dataset_path,
+    hf_download_with_resume,
+)
 from ..util import CUDA_DEVICE, ask_user, download_with_resume
 
 class SDLegacyType(Enum):
@@ -1316,3 +1321,74 @@ class ModelManager(object):
         return (
             os.getenv("HF_HOME") is not None or os.getenv("XDG_CACHE_HOME") is not None
         )
+
+    def list_controlnet_models(self)->Dict[str,bool]:
+        '''Return a dict of installed controlnet models; key is repo_id or short name
+        of model (defined in INITIAL_MODELS), and valule is True if installed'''
+
+        cn_models = OmegaConf.load(Dataset_path).get('controlnet') or {}
+        installed_models = {x: False for x in cn_models.keys()}
+        
+        cn_dir = self.globals.controlnet_path
+        installed_cn_models = dict()
+        for root, dirs, files in os.walk(cn_dir):
+            for name in dirs:
+                if Path(root, name, '.download_complete').exists():
+                    installed_models.update({name.replace('--','/'): True})
+        return installed_models
+
+    def install_controlnet_models(self, model_names: list[str], access_token: str=None):
+        '''Download list of controlnet models; provide either repo_id or short name listed in INITIAL_MODELS.yaml'''
+        short_names = OmegaConf.load(Dataset_path).get('controlnet') or {}
+        dest_dir = self.globals.controlnet_path
+        dest_dir.mkdir(parents=True,exist_ok=True)
+        
+        # The model file may be fp32 or fp16, and may be either a
+        # .bin file or a .safetensors. We try each until we get one,
+        # preferring 'fp16' if using half precision, and preferring
+        # safetensors over over bin.
+        precisions = ['.fp16',''] if self.precision=='float16' else ['']
+        formats = ['.safetensors','.bin']
+        possible_filenames = list()
+        for p in precisions:
+            for f in formats:
+                possible_filenames.append(Path(f'diffusion_pytorch_model{p}{f}'))
+        
+        for directory_name in model_names:
+            repo_id = short_names.get(directory_name) or directory_name
+            safe_name = directory_name.replace('/','--')
+            self.logger.info(f'Downloading ControlNet model {directory_name} ({repo_id})')
+            hf_download_with_resume(
+                repo_id = repo_id,
+                model_dir = dest_dir / safe_name,
+                model_name = 'config.json',
+                access_token = access_token
+            )
+
+            path = None
+            for filename in possible_filenames:
+                suffix = filename.suffix
+                dest_filename = Path(f'diffusion_pytorch_model{suffix}')
+                self.logger.info(f'Checking availability of {directory_name}/{filename}...')
+                path = hf_download_with_resume(
+                    repo_id = repo_id,
+                    model_dir = dest_dir / safe_name,
+                    model_name = str(filename),
+                    access_token = access_token,
+                    model_dest = Path(dest_dir, safe_name, dest_filename),
+                )
+                if path:
+                    (path.parent / '.download_complete').touch()
+                    break
+
+    def delete_controlnet_models(self, model_names: List[str]):
+        '''Remove the list of controlnet models'''
+        for name in model_names:
+            safe_name = name.replace('/','--')
+            directory = self.globals.controlnet_path / safe_name
+            if directory.exists():
+                self.logger.info(f'Purging controlnet model {name}')
+                shutil.rmtree(str(directory))
+
+
+        

invokeai/frontend/install/model_install.py

@@ -30,11 +30,15 @@ from ...backend.install.model_install_backend import (
     default_dataset,
     install_requested_models,
     recommended_datasets,
+    ModelInstallList,
+    dataclass,
 )
+from ...backend import ModelManager
 from ...backend.util import choose_precision, choose_torch_device
 from .widgets import (
     CenteredTitleText,
     MultiSelectColumns,
+    SingleSelectColumns,
     OffsetButtonPress,
     TextBox,
     set_min_terminal_size,
@@ -53,12 +57,12 @@ class addModelsForm(npyscreen.FormMultiPage):
 
     def __init__(self, parentApp, name, multipage=False, *args, **keywords):
         self.multipage = multipage
+
+        model_manager = ModelManager(config.model_conf_path)
         
         self.initial_models = OmegaConf.load(Dataset_path)['diffusers']
-        self.control_net_models = OmegaConf.load(Dataset_path)['controlnet']
+        self.installed_cn_models = model_manager.list_controlnet_models()
-        self.installed_cn_models = self._get_installed_cn_models()
+
-        self._add_additional_cn_models(self.control_net_models,self.installed_cn_models)
-        
         try:
             self.existing_models = OmegaConf.load(default_config_file())
         except:
@@ -79,7 +83,7 @@ class addModelsForm(npyscreen.FormMultiPage):
             [x for x in list(self.initial_models.keys()) if x in self.existing_models]
         )
 
-        cn_model_list = sorted(self.control_net_models.keys())
+        cn_model_list = sorted(self.installed_cn_models.keys())
         
         self.nextrely -= 1
         self.add_widget_intelligent(
@@ -180,16 +184,34 @@ class addModelsForm(npyscreen.FormMultiPage):
             relx=4,
             scroll_exit=True,
         )
+
         self.add_widget_intelligent(
             CenteredTitleText,
-            name="== CONTROLNET MODELS ==",
+            name='_' * (window_width-5),
             editable=False,
-            color="CONTROL",
+            labelColor='CAUTION'
         )
-        self.nextrely -= 1
+            
-        self.add_widget_intelligent(
+        self.nextrely += 1
+        self.tabs = self.add_widget_intelligent(
+            SingleSelectColumns,
+            values=['ADD CONTROLNET MODELS','ADD LORA/LYCORIS MODELS', 'ADD TEXTUAL INVERSION MODELS'],
+            value=0,
+            columns = 4,
+            max_height = 2,
+            relx=8,
+            scroll_exit = True,
+        )
+        # self.add_widget_intelligent(
+        #     CenteredTitleText,
+        #     name="== CONTROLNET MODELS ==",
+        #     editable=False,
+        #     color="CONTROL",
+        # )
+        top_of_table = self.nextrely
+        self.cn_label_1 = self.add_widget_intelligent(
             CenteredTitleText,
-            name="Select the desired ControlNet models. Unchecked models will be purged from disk.",
+            name="Select the desired models to install. Unchecked models will be purged from disk.",
             editable=False,
             labelColor="CAUTION",
         )
@@ -202,14 +224,14 @@ class addModelsForm(npyscreen.FormMultiPage):
             value=[
                 cn_model_list.index(x)
                 for x in cn_model_list
-                if x in self.installed_cn_models
+                if self.installed_cn_models[x]
             ],
             max_height=len(cn_model_list)//columns + 1,
             relx=4,
             scroll_exit=True,
         )
         self.nextrely += 1        
-        self.add_widget_intelligent(
+        self.cn_label_2 = self.add_widget_intelligent(
             npyscreen.TitleFixedText,
             name='Additional ControlNet HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):',
             relx=4,
@@ -221,6 +243,55 @@ class addModelsForm(npyscreen.FormMultiPage):
         self.additional_controlnet_ids = self.add_widget_intelligent(
             TextBox, max_height=2, scroll_exit=True, editable=True, relx=4
         )
+
+        bottom_of_table = self.nextrely
+        self.nextrely = top_of_table
+        self.lora_label_1 = self.add_widget_intelligent(
+            npyscreen.TitleFixedText,
+            name='LoRA/LYCORIS models to download and install (Space separated. Use shift-control-V to paste):',
+            relx=4,
+            color='CONTROL',
+            editable=False,
+            hidden=True,
+            scroll_exit=True
+        )
+        self.nextrely -= 1
+        self.loras = self.add_widget_intelligent(
+            TextBox,
+            max_height=2,
+            scroll_exit=True,
+            editable=True,
+            relx=4,
+            hidden=True,
+        )
+        self.nextrely = top_of_table
+        self.ti_label_1 = self.add_widget_intelligent(
+            npyscreen.TitleFixedText,
+            name='Textual Inversion models to download and install (Space separated. Use shift-control-V to paste):',
+            relx=4,
+            color='CONTROL',
+            editable=False,
+            hidden=True,
+            scroll_exit=True
+        )
+        self.nextrely -= 1
+        self.tis = self.add_widget_intelligent(
+            TextBox,
+            max_height=2,
+            scroll_exit=True,
+            editable=True,
+            relx=4,
+            hidden=True,
+        )
+        self.nextrely = bottom_of_table
+        self.nextrely += 1
+        self.add_widget_intelligent(
+            CenteredTitleText,
+            name='_' * (window_width-5),
+            editable=False,
+            labelColor='CAUTION'
+        )
+
         self.cancel = self.add_widget_intelligent(
             npyscreen.ButtonPress,
             name="CANCEL",
@@ -254,6 +325,9 @@ class addModelsForm(npyscreen.FormMultiPage):
         for i in [self.autoload_directory, self.autoscan_on_startup]:
             self.show_directory_fields.addVisibleWhenSelected(i)
 
+        # self.tabs.when_value_edited = self._toggle_tables
+        self.tabs.on_changed = self._toggle_tables
+
         self.show_directory_fields.when_value_edited = self._clear_scan_directory
 
     def resize(self):
@@ -261,6 +335,21 @@ class addModelsForm(npyscreen.FormMultiPage):
         if hasattr(self, "models_selected"):
             self.models_selected.values = self._get_starter_model_labels()
 
+    def _toggle_tables(self, value=None):
+        selected_tab = value[0] if value else self.tabs.value[0]
+        widgets = [
+            [self.cn_label_1, self.cn_models_selected, self.cn_label_2, self.additional_controlnet_ids],
+            [self.lora_label_1,self.loras],
+            [self.ti_label_1,self.tis],
+        ]
+
+        for group in widgets:
+            for w in group:
+                w.hidden = True
+        for w in widgets[selected_tab]:
+            w.hidden = False
+        self.display()
+
     def _clear_scan_directory(self):
         if not self.show_directory_fields.value:
             self.autoload_directory.value = ""
@@ -361,20 +450,18 @@ class addModelsForm(npyscreen.FormMultiPage):
         selections.install_models = [x for x in starter_models if x not in self.existing_models]
         selections.remove_models = [x for x in self.starter_model_list if x in self.existing_models and x not in starter_models]
 
-        selections.control_net_map = self.control_net_models
         selections.install_cn_models = [self.cn_models_selected.values[x]
                                         for x in self.cn_models_selected.value
-                                        if self.cn_models_selected.values[x] not in self.installed_cn_models
+                                        if not self.installed_cn_models[self.cn_models_selected.values[x]]
                                         ]
         selections.remove_cn_models = [x
                                        for x in self.cn_models_selected.values
-                                       if x in self.installed_cn_models
+                                       if self.installed_cn_models[x]
                                        and self.cn_models_selected.values.index(x) not in self.cn_models_selected.value
                                        ]
         if (additional_cns := self.additional_controlnet_ids.value.split()):
             valid_cns = [x for x in additional_cns if '/' in x]
             selections.install_cn_models.extend(valid_cns)
-            selections.control_net_map.update({x: x for x in valid_cns})
 
         # load directory and whether to scan on startup
         if self.show_directory_fields.value:
@@ -387,22 +474,22 @@ class addModelsForm(npyscreen.FormMultiPage):
         # URLs and the like
         selections.import_model_paths = self.import_model_paths.value.split()
 
-
+@dataclass
+class UserSelections():
+    install_models: List[str]=None
+    remove_models: List[str]=None
+    purge_deleted_models: bool=False,
+    install_cn_models: List[str] = None,
+    remove_cn_models: List[str] = None,
+    scan_directory: Path=None,
+    autoscan_on_startup: bool=False,
+    import_model_paths: str=None,
+        
 class AddModelApplication(npyscreen.NPSAppManaged):
     def __init__(self):
         super().__init__()
         self.user_cancelled = False
-        self.user_selections = Namespace(
+        self.user_selections = UserSelections()
-            install_models=None,
-            remove_models=None,
-            purge_deleted_models=False,
-            install_cn_models = None,
-            remove_cn_models = None,
-            control_net_map = None,
-            scan_directory=None,
-            autoscan_on_startup=None,
-            import_model_paths=None,
-        )
 
     def onStart(self):
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
@@ -418,15 +505,9 @@ def process_and_execute(opt: Namespace, selections: Namespace):
     directory_to_scan = selections.scan_directory
     scan_at_startup = selections.autoscan_on_startup
     potential_models_to_install = selections.import_model_paths
-    print(f'selections.install_cn_models={selections.install_cn_models}')
-    print(f'selections.remove_cn_models={selections.remove_cn_models}')
-    print(f'selections.cn_model_map={selections.control_net_map}')
     install_requested_models(
-        install_initial_models=models_to_install,
+        diffusers = ModelInstallList(models_to_install, models_to_remove),
-        remove_models=models_to_remove,
+        controlnet = ModelInstallList(selections.install_cn_models, selections.remove_cn_models),
-        install_cn_models=selections.install_cn_models,
-        remove_cn_models=selections.remove_cn_models,
-        cn_model_map=selections.control_net_map,
         scan_directory=Path(directory_to_scan) if directory_to_scan else None,
         external_models=potential_models_to_install,
         scan_at_startup=scan_at_startup,

invokeai/frontend/install/widgets.py

@@ -94,13 +94,7 @@ class FloatTitleSlider(npyscreen.TitleText):
     _entry_type = FloatSlider
 
 
-class MultiSelectColumns(npyscreen.MultiSelect):
+class SelectColumnBase():
-    def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
-        self.columns = columns
-        self.value_cnt = len(values)
-        self.rows = math.ceil(self.value_cnt / self.columns)
-        super().__init__(screen, values=values, **keywords)
-
     def make_contained_widgets(self):
         self._my_widgets = []
         column_width = self.width // self.columns
@@ -150,6 +144,32 @@ class MultiSelectColumns(npyscreen.MultiSelect):
     def h_cursor_line_right(self, ch):
         super().h_cursor_line_down(ch)
 
+class MultiSelectColumns( SelectColumnBase, npyscreen.MultiSelect):
+    def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
+        self.columns = columns
+        self.value_cnt = len(values)
+        self.rows = math.ceil(self.value_cnt / self.columns)
+        super().__init__(screen, values=values, **keywords)
+
+
+class SingleSelectColumns(SelectColumnBase, npyscreen.SelectOne):
+    def __init__(self, screen, columns: int = 1, values: list = [], **keywords):
+        self.columns = columns
+        self.value_cnt = len(values)
+        self.rows = math.ceil(self.value_cnt / self.columns)
+        self.on_changed = None
+        super().__init__(screen, values=values, **keywords)
+
+    def h_select(self,ch):
+        super().h_select(ch)
+        if self.on_changed:
+            self.on_changed(self.value)
+
+    def when_value_edited(self):
+        self.h_select(self.cursor_line)
+
+    def when_cursor_moved(self):
+        self.h_select(self.cursor_line)
 
 class TextBox(npyscreen.MultiLineEdit):
     def update(self, clear=True):