Update app.py

app.py

@@ -1,6 +1,7 @@
 # FILE: app.py
 # DESCRIPTION: Final Gradio web interface for the ADUC-SDR Video Suite.
-# This version definitively removes the guidance settings from the UI for a streamlined experience.
+# Features dimension sliders locked to multiples of 8, a unified LTX workflow,
+# advanced controls, integrated SeedVR upscaling, and detailed debug logging.
 
 import gradio as gr
 import traceback
@@ -11,22 +12,30 @@ import logging
 # ==============================================================================
 # --- IMPORTAÇÃO DOS SERVIÇOS DE BACKEND E UTILS ---
 # ==============================================================================
+
 try:
+    # Serviço principal para geração LTX
     from api.ltx_server_refactored_complete import video_generation_service
+    
+    # Nosso decorador de logging para depuração
     from api.utils.debug_utils import log_function_io
+
+    # Serviço especialista para upscaling de resolução (SeedVR)
     from api.seedvr_server import seedvr_server_singleton as seedvr_inference_server
+    
     logging.info("All backend services (LTX, SeedVR) and debug utils imported successfully.")
+
 except ImportError as e:
     def log_function_io(func): return func
-    logging.warning(f"Could not import a module. Some services or debug logs may be unavailable. Details: {e}")
+    logging.warning(f"Could not import a module, debug logger might be disabled. SeedVR might be unavailable. Details: {e}")
     if 'video_generation_service' not in locals():
         logging.critical(f"FATAL: Main LTX service failed to import.", exc_info=True)
         sys.exit(1)
     if 'seedvr_inference_server' not in locals():
         seedvr_inference_server = None
-        logging.warning("SeedVR server could not be initialized. Upscaling tab will be disabled.")
+        logging.warning("SeedVR server could not be initialized. The SeedVR upscaling tab will be disabled.")
 except Exception as e:
-    logging.critical(f"FATAL ERROR during backend initialization. Details: {e}", exc_info=True)
+    logging.critical(f"FATAL ERROR: An unexpected error occurred during backend initialization. Details: {e}", exc_info=True)
     sys.exit(1)
 
 # ==============================================================================
@@ -37,12 +46,14 @@ except Exception as e:
 def run_generate_base_video(
     generation_mode: str, prompt: str, neg_prompt: str, start_img: str, 
     height: int, width: int, duration: float,
+    fp_guidance_preset: str, fp_guidance_scale_list: str, fp_stg_scale_list: str,
     fp_num_inference_steps: int, fp_skip_initial_steps: int, fp_skip_final_steps: int,
     progress=gr.Progress(track_tqdm=True)
 ) -> tuple:
-    """Wrapper that collects UI data and calls the backend (without guidance parameters)."""
+    """Wrapper para a geração do vídeo base LTX."""
     try:
         logging.info(f"[UI] Request received. Selected mode: {generation_mode}")
+        
         initial_conditions = []
         if start_img:
             num_frames_estimate = int(duration * 24)
@@ -52,6 +63,9 @@ def run_generate_base_video(
             )
 
         ltx_configs = {
+            "guidance_preset": fp_guidance_preset,
+            "guidance_scale_list": fp_guidance_scale_list,
+            "stg_scale_list": fp_stg_scale_list,
             "num_inference_steps": fp_num_inference_steps,
             "skip_initial_inference_steps": fp_skip_initial_steps,
             "skip_final_inference_steps": fp_skip_final_steps,
@@ -64,6 +78,7 @@ def run_generate_base_video(
         )
         
         if not video_path: raise RuntimeError("Backend failed to return a valid video path.")
+
         new_state = {"low_res_video": video_path, "low_res_latents": tensor_path, "used_seed": final_seed}
         logging.info(f"[UI] Base video generation successful. Seed used: {final_seed}, Path: {video_path}")
         return video_path, new_state, gr.update(visible=True)
@@ -75,14 +90,16 @@ def run_generate_base_video(
 
 @log_function_io
 def run_ltx_refinement(state: dict, prompt: str, neg_prompt: str, progress=gr.Progress(track_tqdm=True)) -> tuple:
-    """Wrapper for the LTX texture refinement function."""
+    """Wrapper para o refinamento de textura LTX."""
     if not state or not state.get("low_res_latents"):
         raise gr.Error("Error: Please generate a base video in Step 1 before refining.")
+    
     try:
         logging.info(f"[UI] Requesting LTX refinement for latents: {state.get('low_res_latents')}")
         video_path, tensor_path = video_generation_service.generate_upscale_denoise(
             latents_path=state["low_res_latents"],
-            prompt=prompt, negative_prompt=neg_prompt,
+            prompt=prompt,
+            negative_prompt=neg_prompt,
             seed=state["used_seed"]
         )
         state["refined_video_ltx"] = video_path
@@ -96,18 +113,21 @@ def run_ltx_refinement(state: dict, prompt: str, neg_prompt: str, progress=gr.Pr
 
 @log_function_io
 def run_seedvr_upscaling(state: dict, seed: int, resolution: int, batch_size: int, fps: int, progress=gr.Progress(track_tqdm=True)) -> tuple:
-    """Wrapper for the SeedVR resolution upscaling service."""
+    """Wrapper para o upscale de resolução SeedVR."""
     if not state or not state.get("low_res_video"):
         raise gr.Error("Error: Please generate a base video in Step 1 before upscaling.")
     if not seedvr_inference_server:
         raise gr.Error("Error: The SeedVR upscaling server is not available.")
+    
     try:
         logging.info(f"[UI] Requesting SeedVR upscaling for video: {state.get('low_res_video')}")
         def progress_wrapper(p, desc=""): progress(p, desc=desc)
+        
         output_filepath = seedvr_inference_server.run_inference(
             file_path=state["low_res_video"], seed=int(seed), resolution=int(resolution),
             batch_size=int(batch_size), fps=float(fps), progress=progress_wrapper
         )
+        
         status_message = f"✅ Upscaling complete!\nSaved to: {output_filepath}"
         logging.info(f"[UI] SeedVR upscaling successful. Path: {output_filepath}")
         return gr.update(value=output_filepath), gr.update(value=status_message)
@@ -121,7 +141,7 @@ def run_seedvr_upscaling(state: dict, seed: int, resolution: int, batch_size: in
 # ==============================================================================
 
 def build_ui():
-    """Builds the entire Gradio application UI."""
+    """Constrói a interface completa do Gradio."""
     with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo")) as demo:
         app_state = gr.State(value={"low_res_video": None, "low_res_latents": None, "used_seed": None})
         ui_components = {}
@@ -137,9 +157,9 @@ def build_ui():
     return demo
 
 def _build_generation_controls(ui: dict):
-    """Builds the UI components for Step 1, with the guidance section removed."""
+    """Constrói os componentes da UI para a Etapa 1: Geração."""
     gr.Markdown("### Configurações de Geração")
-    ui['generation_mode'] = gr.Radio(label="Modo de Geração", choices=["Simples (Prompt Único)", "Narrativa (Múltiplos Prompts)"], value="Narrativa (Múltiplos Prompts)")
+    ui['generation_mode'] = gr.Radio(label="Modo de Geração", choices=["Simples (Prompt Único)", "Narrativa (Múltiplos Prompts)"], value="Narrativa (Múltiplos Prompts)", info="Simples para uma ação contínua, Narrativa para uma sequência (uma cena por linha).")
     ui['prompt'] = gr.Textbox(label="Prompt(s)", value="Um leão majestoso caminha pela savana\nEle sobe em uma grande pedra e olha o horizonte", lines=4)
     ui['neg_prompt'] = gr.Textbox(label="Negative Prompt", value="blurry, low quality, bad anatomy, deformed", lines=2)
     ui['start_image'] = gr.Image(label="Imagem de Início (Opcional)", type="filepath", sources=["upload"])
@@ -156,11 +176,18 @@ def _build_generation_controls(ui: dict):
         ui['fp_num_inference_steps'] = gr.Slider(label="Número de Passos", minimum=0, maximum=100, step=1, value=20, info="Padrão LTX: 20.")
         ui['fp_skip_initial_steps'] = gr.Slider(label="Pular Passos Iniciais", minimum=0, maximum=100, step=1, value=0)
         ui['fp_skip_final_steps'] = gr.Slider(label="Pular Passos Finais", minimum=0, maximum=100, step=1, value=0)
+        with gr.Tabs():
+            with gr.TabItem("Configurações de Guiagem (First Pass)"):
+                ui['fp_guidance_preset'] = gr.Dropdown(label="Preset de Guiagem", choices=["Padrão (Recomendado)", "Agressivo", "Suave", "Customizado"], value="Padrão (Recomendado)", info="Controla o comportamento da guiagem durante a difusão.")
+                with gr.Group(visible=False) as ui['custom_guidance_group']:
+                    gr.Markdown("⚠️ Edite as listas em formato JSON. Ex: `[1.0, 2.5, 3.0]`")
+                    ui['fp_guidance_scale_list'] = gr.Textbox(label="Lista de Guidance Scale", value="[1, 1, 6, 8, 6, 1, 1]")
+                    ui['fp_stg_scale_list'] = gr.Textbox(label="Lista de STG Scale (Movimento)", value="[0, 0, 4, 4, 4, 2, 1]")
     
     ui['generate_low_btn'] = gr.Button("1. Gerar Vídeo Base", variant="primary")
 
 def _build_postprod_controls(ui: dict):
-    """Builds the UI components for Step 2: Post-Production."""
+    """Constrói os componentes da UI para a Etapa 2: Pós-Produção."""
     with gr.Group(visible=False) as ui['post_prod_group']:
         gr.Markdown("--- \n## Etapa 2: Pós-Produção")
         with gr.Tabs():
@@ -188,13 +215,19 @@ def _build_postprod_controls(ui: dict):
                         ui['seedvr_status_box'] = gr.Textbox(label="Status do SeedVR", value="Aguardando...", lines=3, interactive=False)
 
 def _register_event_handlers(app_state: gr.State, ui: dict):
-    """Registers all Gradio event handlers."""
+    """Registra todos os manipuladores de eventos do Gradio."""
+    def toggle_custom_guidance(preset_choice: str) -> gr.update:
+        return gr.update(visible=(preset_choice == "Customizado"))
+    
+    ui['fp_guidance_preset'].change(fn=toggle_custom_guidance, inputs=ui['fp_guidance_preset'], outputs=ui['custom_guidance_group'])
+
     def update_seed_display(state):
         return state.get("used_seed", "N/A")
 
     gen_inputs = [
         ui['generation_mode'], ui['prompt'], ui['neg_prompt'], ui['start_image'],
         ui['height'], ui['width'], ui['duration'],
+        ui['fp_guidance_preset'], ui['fp_guidance_scale_list'], ui['fp_stg_scale_list'],
         ui['fp_num_inference_steps'], ui['fp_skip_initial_steps'], ui['fp_skip_final_steps'],
     ]
     gen_outputs = [ui['low_res_video_output'], app_state, ui['post_prod_group']]
@@ -214,6 +247,7 @@ def _register_event_handlers(app_state: gr.State, ui: dict):
 # ==============================================================================
 # --- PONTO DE ENTRADA DA APLICAÇÃO ---
 # ==============================================================================
+
 if __name__ == "__main__":
     log_level = os.environ.get("ADUC_LOG_LEVEL", "INFO").upper()
     logging.basicConfig(level=log_level, format='[%(levelname)s] [%(name)s] %(message)s')