added support for presets + automatic FPS

app.py

@@ -661,25 +661,39 @@ class VideoTrainerUI:
             training_dataset
         )
 
-    def update_training_params(self, preset_name: str) -> Dict:
+    def update_training_params(self, preset_name: str) -> Tuple:
         """Update UI components based on selected preset"""
         preset = TRAINING_PRESETS[preset_name]
-               
+        
+        # Find the display name that maps to our model type
+        model_display_name = next(
+            key for key, value in MODEL_TYPES.items() 
+            if value == preset["model_type"]
+        )
+            
         # Get preset description for display
         description = preset.get("description", "")
-        bucket_info = f"\nBucket configuration: {len(preset['training_buckets'])} buckets"
+        
+        # Get max values from buckets
+        buckets = preset["training_buckets"]
+        max_frames = max(frames for frames, _, _ in buckets)
+        max_height = max(height for _, height, _ in buckets)
+        max_width = max(width for _, _, width in buckets)
+        bucket_info = f"\nMaximum video size: {max_frames} frames at {max_width}x{max_height} resolution"
+        
         info_text = f"{description}{bucket_info}"
         
-        return {
-            "model_type": gr.Dropdown(value=MODEL_TYPES[preset["model_type"]]),
-            "lora_rank": gr.Dropdown(value=preset["lora_rank"]),
-            "lora_alpha": gr.Dropdown(value=preset["lora_alpha"]),
-            "num_epochs": gr.Number(value=preset["num_epochs"]),
-            "batch_size": gr.Number(value=preset["batch_size"]),
-            "learning_rate": gr.Number(value=preset["learning_rate"]),
-            "save_iterations": gr.Number(value=preset["save_iterations"]),
-            "preset_info": gr.Markdown(value=info_text)
-        }
+        # Return values in the same order as the output components
+        return (
+            model_display_name,
+            preset["lora_rank"],
+            preset["lora_alpha"],
+            preset["num_epochs"],
+            preset["batch_size"],
+            preset["learning_rate"],
+            preset["save_iterations"],
+            info_text
+        )
 
     def create_ui(self):
         """Create Gradio interface"""

config.py

@@ -262,7 +262,7 @@ class TrainingConfig:
             data_root=data_path,
             output_dir=output_path,
             batch_size=1,
-            train_epochs=70,
+            train_epochs=40,
             lr=3e-5,
             gradient_checkpointing=True,
             id_token="BW_STYLE",

training_service.py

@@ -19,7 +19,7 @@ import select
 from typing import Any, Optional, Dict, List, Union, Tuple
 
 from huggingface_hub import upload_folder, create_repo
-from config import TrainingConfig,TRAINING_PRESETS,  LOG_FILE_PATH, TRAINING_VIDEOS_PATH, STORAGE_PATH, TRAINING_PATH, MODEL_PATH, OUTPUT_PATH, HF_API_TOKEN, MODEL_TYPES
+from config import TrainingConfig, TRAINING_PRESETS,  LOG_FILE_PATH, TRAINING_VIDEOS_PATH, STORAGE_PATH, TRAINING_PATH, MODEL_PATH, OUTPUT_PATH, HF_API_TOKEN, MODEL_TYPES
 from utils import make_archive, parse_training_log, is_image_file, is_video_file
 from finetrainers_utils import prepare_finetrainers_dataset, copy_files_to_training_dir
 
@@ -214,8 +214,18 @@ class TrainingService:
             return f"Configuration validation failed: {str(e)}"
         
         
-    def start_training(self, model_type: str, lora_rank: str, lora_alpha: str, num_epochs: int, batch_size: int, 
-                  learning_rate: float, save_iterations: int, repo_id: str) -> Tuple[str, str]:
+    def start_training(
+        self,
+        model_type: str,
+        lora_rank: str,
+        lora_alpha: str,
+        num_epochs: int,
+        batch_size: int, 
+        learning_rate: float,
+        save_iterations: int,
+        repo_id: str,
+        preset_name: str,
+    ) -> Tuple[str, str]:
         """Start training with finetrainers"""
             
         self.clear_logs()