Minimal preload before dump current model data

backend/loader.py

@@ -30,6 +30,24 @@
 dir_path = os.path.dirname(__file__)
 
 
+def check_huggingface_component(component_name:str, cls_name:str, state_dict):
+    check_sd = False
+    comp_str = 'model'
+
+    if cls_name == 'AutoencoderKL':
+        check_sd = True
+        comp_str = 'VAE'
+    elif component_name.startswith('text_encoder') and cls_name in ['CLIPTextModel', 'CLIPTextModelWithProjection']:
+        check_sd = True
+        comp_str = 'CLIP'
+    elif cls_name == 'T5EncoderModel':
+        check_sd = True
+        comp_str = 'T5'
+
+    if check_sd and (not isinstance(state_dict, dict) or len(state_dict) <= 16):
+        raise AssertionError(f'You do not have {comp_str} state dict!')
+
+
 def load_huggingface_component(guess, component_name, lib_name, cls_name, repo_path, state_dict):
     config_path = os.path.join(repo_path, component_name)
 
@@ -269,16 +287,33 @@ def split_state_dict(sd, additional_state_dicts: list = None):
 
 
 @torch.inference_mode()
-def forge_loader(sd, additional_state_dicts=None):
+def forge_preloader(sd, additional_state_dicts=None):
+    """performs minimum steps to validate model params before reloading models"""
     try:
         state_dicts, estimated_config = split_state_dict(sd, additional_state_dicts=additional_state_dicts)
     except:
         raise ValueError('Failed to recognize model type!')
 
     repo_name = estimated_config.huggingface_repo
-
     local_path = os.path.join(dir_path, 'huggingface', repo_name)
+
     config: dict = DiffusionPipeline.load_config(local_path)
+
+    for component_name, v in config.items():
+        if isinstance(v, list) and len(v) == 2:
+            _, cls_name = v
+            component_sd = state_dicts.get(component_name, None)
+            # Raise AssertionError for invalid params
+            check_huggingface_component(component_name, cls_name, component_sd)
+
+    return state_dicts, estimated_config, config
+
+
+@torch.inference_mode()
+def forge_loader(state_dicts:dict, estimated_config, config:dict):    
+    repo_name = estimated_config.huggingface_repo
+    local_path = os.path.join(dir_path, 'huggingface', repo_name)
+
     huggingface_components = {}
     for component_name, v in config.items():
         if isinstance(v, list) and len(v) == 2:

modules/sd_models.py

@@ -18,7 +18,7 @@
 from modules.shared import opts, cmd_opts
 from modules.timer import Timer
 import numpy as np
-from backend.loader import forge_loader
+from backend.loader import forge_preloader, forge_loader
 from backend import memory_management
 from backend.args import dynamic_args
 from backend.utils import load_torch_file
@@ -475,7 +475,19 @@ def forge_model_reload():
 
     if model_data.forge_hash == current_hash:
         return model_data.sd_model, False
+
+    # verify model/components before reload
+    checkpoint_info = model_data.forge_loading_parameters['checkpoint_info']
 
+    if checkpoint_info is None:
+        raise ValueError('You do not have any model! Please download at least one model in [models/Stable-diffusion].')
+
+    state_dict = checkpoint_info.filename
+    additional_state_dicts = model_data.forge_loading_parameters.get('additional_modules', [])
+
+    state_dicts, estimated_config, config = forge_preloader(state_dict, additional_state_dicts=additional_state_dicts)
+
+    # reload model
     print('Loading Model: ' + str(model_data.forge_loading_parameters))
 
     timer = Timer()
@@ -488,20 +500,10 @@ def forge_model_reload():
 
     timer.record("unload existing model")
 
-    checkpoint_info = model_data.forge_loading_parameters['checkpoint_info']
-
-    if checkpoint_info is None:
-        raise ValueError('You do not have any model! Please download at least one model in [models/Stable-diffusion].')
-
-    state_dict = checkpoint_info.filename
-    additional_state_dicts = model_data.forge_loading_parameters.get('additional_modules', [])
-
-    timer.record("cache state dict")
-
     dynamic_args['forge_unet_storage_dtype'] = model_data.forge_loading_parameters.get('unet_storage_dtype', None)
     dynamic_args['embedding_dir'] = cmd_opts.embeddings_dir
     dynamic_args['emphasis_name'] = opts.emphasis
-    sd_model = forge_loader(state_dict, additional_state_dicts=additional_state_dicts)
+    sd_model = forge_loader(state_dicts, estimated_config, config)
     timer.record("forge model load")
 
     sd_model.extra_generation_params = {}