Update api/ltx/ltx_utils.py

api/ltx/ltx_utils.py

@@ -1,6 +1,7 @@
 # FILE: api/ltx/ltx_utils.py
 # DESCRIPTION: A pure utility library for the LTX ecosystem.
-# Contains the official low-level builder function for core components and other stateless helpers.
+# Contains the official low-level builder function for the complete pipeline
+# and other stateless helper functions.
 
 import os
 import random
@@ -29,12 +30,15 @@ def add_deps_to_path():
 
 add_deps_to_path()
 
-if True:
+try:
     from ltx_video.pipelines.pipeline_ltx_video import LTXVideoPipeline
     from ltx_video.models.autoencoders.causal_video_autoencoder import CausalVideoAutoencoder
     from ltx_video.models.transformers.transformer3d import Transformer3DModel
     from ltx_video.models.transformers.symmetric_patchifier import SymmetricPatchifier
     from ltx_video.schedulers.rf import RectifiedFlowScheduler
+except ImportError as e:
+    logging.critical("Failed to import a core LTX-Video library component.", exc_info=True)
+    raise ImportError(f"Could not import from LTX-Video library. Check repo integrity at '{LTX_VIDEO_REPO_DIR}'. Error: {e}")
 
 # ==============================================================================
 # --- FUNÇÃO HELPER 'create_transformer' (Essencial) ---
@@ -62,12 +66,12 @@ def create_transformer(ckpt_path: str, precision: str) -> Transformer3DModel:
 # --- BUILDER DE BAIXO NÍVEL OFICIAL ---
 # ==============================================================================
 
-def build_components_on_cpu(checkpoint_path: str, config: Dict) -> Tuple[LTXVideoPipeline, CausalVideoAutoencoder]:
+def build_complete_pipeline_on_cpu(checkpoint_path: str, config: Dict) -> LTXVideoPipeline:
     """
-    Constrói o pipeline LTX principal (sem VAE) e o modelo VAE separadamente, na CPU.
+    Constrói o pipeline LTX COMPLETO, incluindo o VAE, e o mantém na CPU.
     Esta é a função de construção fundamental usada pelo LTXAducManager.
     """
-    logging.info(f"Building LTX components from checkpoint: {Path(checkpoint_path).name}")
+    logging.info(f"Building complete LTX pipeline from checkpoint: {Path(checkpoint_path).name}")
     
     with safe_open(checkpoint_path, framework="pt") as f:
         metadata = f.metadata() or {}
@@ -88,6 +92,7 @@ def build_components_on_cpu(checkpoint_path: str, config: Dict) -> Tuple[LTXVide
     if precision == "bfloat16":
         text_encoder.to(torch.bfloat16)
         vae.to(torch.bfloat16)
+        # O transformer já foi convertido para bfloat16 dentro de create_transformer, se aplicável
 
     pipeline = LTXVideoPipeline(
         transformer=transformer,
@@ -95,7 +100,7 @@ def build_components_on_cpu(checkpoint_path: str, config: Dict) -> Tuple[LTXVide
         text_encoder=text_encoder,
         tokenizer=tokenizer,
         scheduler=scheduler,
-        vae=vae, # VAE é desacoplado para ser gerenciado por um worker separado
+        vae=vae, # VAE é incluído para que o pipeline possa ser auto-suficiente
         allowed_inference_steps=allowed_inference_steps,
         prompt_enhancer_image_caption_model=None,
         prompt_enhancer_image_caption_processor=None,
@@ -103,7 +108,7 @@ def build_components_on_cpu(checkpoint_path: str, config: Dict) -> Tuple[LTXVide
         prompt_enhancer_llm_tokenizer=None,
     )
     
-    return pipeline, vae
+    return pipeline
 
 # ==============================================================================
 # --- FUNÇÕES AUXILIARES GENÉRICAS ---