listing, downloading and deleting LoRAs working; TI support pending

invokeai/backend/install/model_install_backend.py

@@ -95,6 +95,9 @@ def install_requested_models(
     model_manager.install_controlnet_models(controlnet.install_models, access_token=access_token)
     model_manager.delete_controlnet_models(controlnet.remove_models)
 
+    model_manager.install_lora_models(lora.install_models)
+    model_manager.delete_lora_models(lora.remove_models)
+
     # TODO: Replace next three paragraphs with calls into new model manager
     if diffusers.remove_models and len(diffusers.remove_models) > 0:
         logger.info("DELETING UNCHECKED STARTER MODELS")

invokeai/backend/model_management/model_manager.py

@@ -20,7 +20,7 @@ import warnings
 from enum import Enum, auto
 from pathlib import Path
 from shutil import move, rmtree
-from typing import Any, Optional, Union, Callable, types
+from typing import Any, Optional, Union, Callable, Dict, List, types
 
 import safetensors
 import safetensors.torch
@@ -1322,15 +1322,69 @@ class ModelManager(object):
             os.getenv("HF_HOME") is not None or os.getenv("XDG_CACHE_HOME") is not None
         )
 
+    def list_lora_models(self)->Dict[str,bool]:
+        '''Return a dict of installed lora models; key is either the shortname
+        defined in INITIAL_MODELS, or the basename of the file in the LoRA
+        directory. Value is True if installed'''
+
+        models = OmegaConf.load(Dataset_path).get('lora') or {}
+        installed_models = {x: False for x in models.keys()}
+        
+        dir = self.globals.lora_path
+        installed_models = dict()
+        for root, dirs, files in os.walk(dir):
+            for name in files:
+                if Path(name).suffix in ['.safetensors','.ckpt','.pt']:
+                    installed_models.update({name: True})
+        return installed_models
+    
+    def install_lora_models(self, model_names: list[str]):
+        '''Download list of LoRA/LyCORIS models'''
+        short_names = OmegaConf.load(Dataset_path).get('lora') or {}
+        for name in model_names:
+            url = short_names.get(name) or name
+            download_with_resume(url, self.globals.lora_path)
+    
+    def delete_lora_models(self, model_names: List[str]):
+        '''Remove the list of lora models'''
+        for name in model_names:
+            path = self.globals.lora_path / name
+            if path.exists():
+                self.logger.info(f'Purging lora model {name}')
+                path.unlink()
+                
+    def list_ti_models(self)->Dict[str,bool]:
+        '''Return a dict of installed textual models; key is either the shortname
+        defined in INITIAL_MODELS, or the basename of the file in the LoRA
+        directory. Value is True if installed'''
+
+        models = OmegaConf.load(Dataset_path).get('textual_inversion') or {}
+        installed_models = {x: False for x in models.keys()}
+        
+        dir = self.globals.embedding_path
+        installed_models = dict()
+        for root, dirs, files in os.walk(dir):
+            for name in files:
+                if name == 'learned_embeds.bin':
+                    name = str(Path(root,name).parent)
+                installed_models.update({name: True})
+        return installed_models
+
+    def install_ti_models(self, model_names: list[str]):
+        '''Download list of textual inversion embeddings'''
+        short_names = OmegaConf.load(Dataset_path).get('textual_inversion') or {}
+        for name in model_names:
+            url = short_names.get(name) or name
+            download_with_resume(url, self.globals.embedding_path)
+    
     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'''
+        of model (defined in INITIAL_MODELS), and value 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():