Upload app.py

app.py

@@ -15,9 +15,9 @@ from PIL import Image as PILImage
 # --- IMPORTAÇÃO DOS SERVIÇOS DE BACKEND E UTILS (CAMINHOS ATUALIZADOS) ---
 # ==============================================================================
 
-from api.ltx.ltx_aduc_pipeline import video_generation_service
+from api.ltx.ltx_aduc_pipeline import ltx_aduc_pipeline
 from utils.debug_utils import log_function_io
-from api.seedvr.seedvr_aduc_pipeline import seed_aduc_pipeline as seedvr_inference_server
+from api.seedvr.seedvr_aduc_pipeline import seed_aduc_pipeline as seed_aduc_pipeline
 logging.info("All backend services and utils imported successfully from new paths.")
 
 # ==============================================================================
@@ -50,7 +50,7 @@ def run_generate_base_video(
             "skip_final_inference_steps": fp_skip_final_steps,
         }
 
-        video_path, tensor_path, final_seed = video_generation_service.generate_low_resolution(
+        video_path, tensor_path, final_seed = ltx_aduc_pipeline.generate_low_resolution(
             prompt_list=prompt_list,
             negative_prompt=neg_prompt,
             height=height, width=width, duration=duration,
@@ -75,7 +75,7 @@ def run_ltx_refinement(state: dict, prompt: str, neg_prompt: str, progress=gr.Pr
         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(
+        video_path, tensor_path = ltx_aduc_pipeline.generate_upscale_denoise(
             latents_path=state["low_res_latents"],
             prompt=prompt, negative_prompt=neg_prompt,
             seed=state["used_seed"]
@@ -95,12 +95,12 @@ def run_seedvr_upscaling(state: dict, seed: int, resolution: int, batch_size: in
     """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:
+    if not seed_aduc_pipeline:
         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(
+        output_filepath = seed_aduc_pipeline.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
         )
@@ -121,13 +121,13 @@ def build_ui():
     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 = {}
-        gr.Markdown("# ADUC-SDR Video Suite - LTX & SeedVR Workflow", elem_id="main-title")
+        gr.Markdown("🚀 ADUC-SDR Video Suite - Infinite LTX & SeedVR Workflow", elem_id="main-title")
         with gr.Row():
             with gr.Column(scale=1): _build_generation_controls(ui_components)
             with gr.Column(scale=1):
                 gr.Markdown("### Etapa 1: Vídeo Base Gerado")
-                ui_components['low_res_video_output'] = gr.Video(label="O resultado aparecerá aqui", interactive=False)
-                ui_components['used_seed_display'] = gr.Textbox(label="Seed Utilizada", interactive=False)
+                ui_components['low_res_video_output'] = gr.Video(label="O resultado aparecerá aqui", interactive=True)
+                ui_components['used_seed_display'] = gr.Textbox(label="Seed Utilizada", visible=False interactive=False)
         _build_postprod_controls(ui_components)
         _register_event_handlers(app_state, ui_components)
     return demo
@@ -135,15 +135,15 @@ def build_ui():
 def _build_generation_controls(ui: dict):
     """Constrói os componentes da UI, sem seleção de modo."""
     gr.Markdown("### Configurações de Geração")
-    ui['prompt'] = gr.Textbox(label="Prompt(s)", info="Para múltiplas cenas (modo narrativo), escreva um prompt por linha.", 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['prompt'] = gr.Textbox(label="Prompt(s)", info="Para múltiplas cenas escreva um linha por prompt.", value="", lines=6)
+    ui['neg_prompt'] = gr.Textbox(label="Negative Prompt", visible=False, value="blurry, low quality, bad anatomy, deformed", lines=2)
     ui['start_image'] = gr.Image(label="Imagem de Início (Opcional)", type="pil", sources=["upload"])
     
     with gr.Accordion("Parâmetros Principais", open=True):
         ui['duration'] = gr.Slider(label="Duração Total (s)", value=4, step=1, minimum=1, maximum=30)
         with gr.Row():
-            ui['height'] = gr.Slider(label="Height", value=432, step=8, minimum=256, maximum=1024)
-            ui['width'] = gr.Slider(label="Width", value=768, step=8, minimum=256, maximum=1024)
+            ui['height'] = gr.Slider(label="Height", value=432, step=128, minimum=256, maximum=1024)
+            ui['width'] = gr.Slider(label="Width", value=768, step=128, minimum=256, maximum=1024)
 
     with gr.Accordion("Opções Avançadas LTX", open=False):
         gr.Markdown("#### Configurações de Passos de Inferência")
@@ -168,7 +168,7 @@ def _build_postprod_controls(ui: dict):
                         ui['ltx_refined_video_output'] = gr.Video(label="Vídeo com Textura Refinada", interactive=False)
             
             with gr.TabItem("✨ Upscaler de Resolução (SeedVR)"):
-                is_seedvr_available = seedvr_inference_server is not None
+                is_seedvr_available = seed_aduc_pipeline is not None
                 if not is_seedvr_available:
                     gr.Markdown("🔴 **AVISO: O serviço SeedVR não está disponível.**")
                 with gr.Row():