Add CDC-FM parameters to model metadata

- Add ss_use_cdc_fm, ss_cdc_k_neighbors, ss_cdc_k_bandwidth, ss_cdc_d_cdc, ss_cdc_gamma
- Ensures CDC-FM training parameters are tracked in model metadata
- Enables reproducibility and model provenance tracking

flux_train_network.py

@@ -461,6 +461,13 @@ class FluxNetworkTrainer(train_network.NetworkTrainer):
         metadata["ss_model_prediction_type"] = args.model_prediction_type
         metadata["ss_discrete_flow_shift"] = args.discrete_flow_shift
 
+        # CDC-FM metadata
+        metadata["ss_use_cdc_fm"] = getattr(args, "use_cdc_fm", False)
+        metadata["ss_cdc_k_neighbors"] = getattr(args, "cdc_k_neighbors", None)
+        metadata["ss_cdc_k_bandwidth"] = getattr(args, "cdc_k_bandwidth", None)
+        metadata["ss_cdc_d_cdc"] = getattr(args, "cdc_d_cdc", None)
+        metadata["ss_cdc_gamma"] = getattr(args, "cdc_gamma", None)
+
     def is_text_encoder_not_needed_for_training(self, args):
         return args.cache_text_encoder_outputs and not self.is_train_text_encoder(args)
 

train_network.py

@@ -652,7 +652,7 @@ class NetworkTrainer:
         if val_dataset_group is not None:
             self.cache_text_encoder_outputs_if_needed(args, accelerator, unet, vae, text_encoders, val_dataset_group, weight_dtype)
 
-        if unet is none:
+        if unet is None:
             # lazy load unet if needed. text encoders may be freed or replaced with dummy models for saving memory
             unet, text_encoders = self.load_unet_lazily(args, weight_dtype, accelerator, text_encoders)
 
@@ -661,10 +661,10 @@ class NetworkTrainer:
         accelerator.print("import network module:", args.network_module)
         network_module = importlib.import_module(args.network_module)
 
-        if args.base_weights is not none:
+        if args.base_weights is not None:
             # base_weights が指定されている場合は、指定された重みを読み込みマージする
             for i, weight_path in enumerate(args.base_weights):
-                if args.base_weights_multiplier is none or len(args.base_weights_multiplier) <= i:
+                if args.base_weights_multiplier is None or len(args.base_weights_multiplier) <= i:
                     multiplier = 1.0
                 else:
                     multiplier = args.base_weights_multiplier[i]