Update api/ltx_server_refactored_complete.py

api/ltx_server_refactored_complete.py

@@ -1,6 +1,7 @@
 # FILE: api/ltx_server_refactored_complete.py
-# DESCRIPTION: Final high-level orchestrator for LTX-Video generation.
-# Incorporates a debug logging decorator for deep inspection of function I/O.
+# DESCRIPTION: Final orchestrator for LTX-Video generation.
+# Features path resolution for cached models, dedicated VAE device logic,
+# delegation to utility modules, and advanced debug logging.
 
 import gc
 import json
@@ -16,24 +17,33 @@ from typing import Dict, List, Optional, Tuple
 import torch
 import yaml
 import numpy as np
+from huggingface_hub import hf_hub_download
 
 # ==============================================================================
 # --- SETUP E IMPORTAÇÕES DO PROJETO ---
 # ==============================================================================
 
-# Constantes de configuração do ambiente
+# Configuração de logging e supressão de warnings
+# (Pode ser removido se o logging for configurado globalmente)
+import warnings
+warnings.filterwarnings("ignore")
+logging.getLogger("huggingface_hub").setLevel(logging.ERROR)
+log_level = os.environ.get("ADUC_LOG_LEVEL", "INFO").upper()
+logging.basicConfig(level=log_level, format='[%(levelname)s] [%(name)s] %(message)s')
+
+# --- Constantes de Configuração ---
 DEPS_DIR = Path("/data")
 LTX_VIDEO_REPO_DIR = DEPS_DIR / "LTX-Video"
 RESULTS_DIR = Path("/app/output")
 DEFAULT_FPS = 24.0
 FRAMES_ALIGNMENT = 8
+LTX_REPO_ID = "Lightricks/LTX-Video" # Repositório de onde os modelos são baixados
 
 # Garante que a biblioteca LTX-Video seja importável
 def add_deps_to_path():
     repo_path = str(LTX_VIDEO_REPO_DIR.resolve())
     if repo_path not in sys.path:
         sys.path.insert(0, repo_path)
-        # Usamos logging.info aqui, pois é uma informação importante de inicialização
         logging.info(f"[ltx_server] LTX-Video repository added to sys.path: {repo_path}")
 
 add_deps_to_path()
@@ -43,18 +53,13 @@ try:
     from api.gpu_manager import gpu_manager
     from managers.vae_manager import vae_manager_singleton
     from tools.video_encode_tool import video_encode_tool_singleton
-    
-    # Nosso módulo de utilitários LTX, que encapsula a complexidade
     from api.ltx.ltx_utils import (
         build_ltx_pipeline_on_cpu,
         seed_everything,
         load_image_to_tensor_with_resize_and_crop,
         ConditioningItem,
     )
-    
-    # Nosso novo decorador de logging para depuração
     from api.utils.debug_utils import log_function_io
-
 except ImportError as e:
     logging.critical(f"A crucial import from the local API/architecture failed. Error: {e}", exc_info=True)
     sys.exit(1)
@@ -66,7 +71,7 @@ except ImportError as e:
 @log_function_io
 def calculate_padding(orig_h: int, orig_w: int, target_h: int, target_w: int) -> Tuple[int, int, int, int]:
     """Calculates symmetric padding required to meet target dimensions."""
-    pad_h = target_h - orig_w
+    pad_h = target_h - orig_h
     pad_w = target_w - orig_w
     pad_top = pad_h // 2
     pad_bottom = pad_h - pad_top
@@ -87,7 +92,6 @@ class VideoService:
     @log_function_io
     def __init__(self):
         t0 = time.perf_counter()
-        # Logging de alto nível para o usuário
         logging.info("Initializing VideoService Orchestrator...")
         RESULTS_DIR.mkdir(parents=True, exist_ok=True)
 
@@ -96,6 +100,8 @@ class VideoService:
         logging.info(f"LTX allocated to devices: Main='{target_main_device_str}', VAE='{target_vae_device_str}'")
 
         self.config = self._load_config()
+        self._resolve_model_paths_from_cache() # Etapa crítica para encontrar os modelos
+
         self.pipeline, self.latent_upsampler = build_ltx_pipeline_on_cpu(self.config)
 
         self.main_device = torch.device("cpu")
@@ -106,7 +112,6 @@ class VideoService:
         vae_manager_singleton.attach_pipeline(self.pipeline, device=self.vae_device, autocast_dtype=self.runtime_autocast_dtype)
         logging.info(f"VideoService ready. Startup time: {time.perf_counter()-t0:.2f}s")
 
-    @log_function_io
     def _load_config(self) -> Dict:
         """Loads the YAML configuration file."""
         config_path = LTX_VIDEO_REPO_DIR / "configs" / "ltxv-13b-0.9.8-distilled-fp8.yaml"
@@ -114,6 +119,38 @@ class VideoService:
         with open(config_path, "r") as file:
             return yaml.safe_load(file)
 
+    def _resolve_model_paths_from_cache(self):
+        """
+        Uses hf_hub_download to find the absolute paths to model files in the cache,
+        updating the in-memory config. This makes the app resilient to cache structure.
+        """
+        logging.info("Resolving model paths from Hugging Face cache...")
+        cache_dir = os.environ.get("HF_HOME")
+        try:
+            # Resolve o caminho do checkpoint principal
+            main_ckpt_filename = self.config["checkpoint_path"]
+            main_ckpt_path = hf_hub_download(
+                repo_id=LTX_REPO_ID,
+                filename=main_ckpt_filename,
+                cache_dir=cache_dir
+            )
+            self.config["checkpoint_path"] = main_ckpt_path
+            logging.info(f"  -> Main checkpoint resolved to: {main_ckpt_path}")
+
+            # Resolve o caminho do upsampler, se existir
+            if self.config.get("spatial_upscaler_model_path"):
+                upscaler_filename = self.config["spatial_upscaler_model_path"]
+                upscaler_path = hf_hub_download(
+                    repo_id=LTX_REPO_ID,
+                    filename=upscaler_filename,
+                    cache_dir=cache_dir
+                )
+                self.config["spatial_upscaler_model_path"] = upscaler_path
+                logging.info(f"  -> Spatial upscaler resolved to: {upscaler_path}")
+        except Exception as e:
+            logging.critical(f"Failed to resolve model paths. Ensure setup.py ran correctly. Error: {e}", exc_info=True)
+            sys.exit(1)
+
     @log_function_io
     def move_to_device(self, main_device_str: str, vae_device_str: str):
         """Moves pipeline components to their designated target devices."""