rename stuff

nodes.py

@@ -448,7 +448,7 @@ class FluxNetworkTrainer(NetworkTrainer):
         metadata["ss_model_prediction_type"] = args.model_prediction_type
         metadata["ss_discrete_flow_shift"] = args.discrete_flow_shift
 
-class SelectModelsTrainFlux:
+class FluxTrainModelSelect:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
@@ -500,10 +500,10 @@ class TrainDatasetConfig:
 
     RETURN_TYPES = ("TOML_DATASET",)
     RETURN_NAMES = ("dataset",)
-    FUNCTION = "loadmodel"
+    FUNCTION = "create_config"
     CATEGORY = "TrainFlux"
 
-    def loadmodel(self, dataset_path, class_tokens, width, height, batch_size, enable_bucket, color_aug, flip_aug, 
+    def create_config(self, dataset_path, class_tokens, width, height, batch_size, enable_bucket, color_aug, flip_aug, 
                   bucket_no_upscale, min_bucket_reso, max_bucket_reso):
         import toml
 
@@ -535,7 +535,7 @@ class TrainDatasetConfig:
         
         return (toml.dumps(dataset),)
     
-class TrainFlux:
+class InitFluxTraining:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {
@@ -548,8 +548,6 @@ class TrainFlux:
             #"max_train_epochs": ("INT", {"default": 4, "min": 1, "max": 1000, "step": 1, "tooltip": "max number of training epochs"}),
             "optimizer_type": (["adamw8bit", "adafactor", "prodigy"], {"default": "adamw8bit", "tooltip": "optimizer type"}),
             "max_train_steps": ("INT", {"default": 1500, "min": 1, "max": 10000, "step": 1, "tooltip": "max number of training steps"}),
-            "save_every_n_steps": ("INT", {"default": 250, "min": 1, "max": 10000, "step": 1, "tooltip": "save every n epochs"}),
-            "sample_every_n_steps": ("INT", {"default": 250, "min": 1, "max": 10000, "step": 1, "tooltip": "sample every n steps"}),
             "network_train_unet_only": ("BOOLEAN", {"default": True, "tooltip": "wheter to train the text encoder"}),
             "text_encoder_lr": ("FLOAT", {"default": 1e-4, "min": 0.0, "max": 10.0, "step": 0.00001, "tooltip": "text encoder learning rate"}),
             "apply_t5_attn_mask": ("BOOLEAN", {"default": True, "tooltip": "apply t5 attention mask"}),
@@ -572,11 +570,10 @@ class TrainFlux:
 
     RETURN_TYPES = ("NETWORKTRAINER",)
     RETURN_NAMES = ("network_trainer",)
-    FUNCTION = "loadmodel"
+    FUNCTION = "init_training"
     CATEGORY = "TrainFlux"
 
-    def loadmodel(self, flux_models, dataset, sample_prompts, output_name, optimizer_type, **kwargs,):
-        device = mm.get_torch_device()
+    def init_training(self, flux_models, dataset, sample_prompts, output_name, optimizer_type, **kwargs,):
         mm.soft_empty_cache()
 
         parser = setup_parser()
@@ -649,7 +646,7 @@ class TrainFlux:
 
         with torch.inference_mode(False):
             network_trainer = FluxNetworkTrainer()
-            training_loop = network_trainer.train(args)
+            training_loop = network_trainer.init_train(args)
 
         final_output_lora_path = os.path.join(output_dir, "output", output_name)
 
@@ -751,14 +748,14 @@ class TrainLoop:
     
 
 NODE_CLASS_MAPPINGS = {
-    "TrainFlux": TrainFlux,
-    "SelectModelsTrainFlux": SelectModelsTrainFlux,
+    "InitFluxTraining": InitFluxTraining,
+    "FluxTrainModelSelect": FluxTrainModelSelect,
     "TrainDatasetConfig": TrainDatasetConfig,
     "TrainLoop": TrainLoop
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "TrainFlux": "TrainFlux",
-    "SelectModelsTrainFlux": "SelectModelsTrainFlux",
+    "InitFluxTraining": "Init Flux Training",
+    "FluxTrainModelSelect": "FluxTrain ModelSelect",
     "TrainDatasetConfig": "Train Dataset Config",
     "TrainLoop": "Train Loop"
 }

train_network.py

@@ -238,7 +238,7 @@ class NetworkTrainer:
 
     # endregion
 
-    def train(self, args):
+    def init_train(self, args):
         session_id = random.randint(0, 2**32)
         training_started_at = time.time()
         train_util.verify_training_args(args)
@@ -993,7 +993,7 @@ class NetworkTrainer:
             text_encoders = []
 
         # For --sample_at_first
-        self.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizers, text_encoder, unet)
+        #self.sample_images(accelerator, args, 0, global_step, accelerator.device, vae, tokenizers, text_encoder, unet)
 
         # training loop
         if initial_step > 0:  # only if skip_until_initial_step is specified