Update managers/ltx_manager.py

managers/ltx_manager.py

@@ -7,16 +7,14 @@
 # carlex22@gmail.com
 # Rua Eduardo Carlos Pereira, 4125, B1 Ap32, Curitiba, PR, Brazil, CEP 8102025
 #
-# Repositórios e Projetos Relacionados:
-# GitHub: https://github.com/carlex22/Aduc-sdr
-#
 # PENDING PATENT NOTICE: Please see NOTICE.md.
 #
-# Version: 2.2.2
+# Version: 2.3.0
 #
-# This file manages the LTX-Video specialist pool. It has been refactored to be
-# self-contained by automatically cloning its own dependencies and using a local
-# utility module for pipeline creation, fully decoupling it from external scripts.
+# This version adds a public property `prompt_enhancement_pipeline` to the manager.
+# This allows other specialists, specifically the Deformes3DThinker, to access
+# the internal prompt refinement models (captioning and LLM) used by the LTX pipeline,
+# ensuring stylistic and logical consistency.
 
 import torch
 import gc
@@ -54,8 +52,7 @@ randn_tensor = None
 
 class LtxPoolManager:
     """
-    Manages a pool of LtxWorkers for optimized multi-GPU usage.
-    Handles its own code dependencies by cloning the LTX-Video repository.
+    Manages a pool of LtxWorkers and exposes the enhancement pipeline for other specialists.
     """
     def __init__(self, device_ids, ltx_config_file_name):
         logger.info(f"LTX POOL MANAGER: Creating workers for devices: {device_ids}")
@@ -69,6 +66,14 @@ class LtxPoolManager:
         self.current_worker_index = 0
         self.lock = threading.Lock()
 
+        # <--- NOVA PROPRIEDADE PARA O DEFORMES3DTHINKER USAR --->
+        # Expõe a pipeline do primeiro worker. Assumimos que todas são configuradas
+        # da mesma forma e contêm os mesmos modelos de enhancement.
+        self.prompt_enhancement_pipeline = self.workers[0].pipeline if self.workers else None
+        if self.prompt_enhancement_pipeline:
+            logger.info("LTX POOL MANAGER: Prompt enhancement pipeline exposed for other specialists.")
+        # <--- FIM DA NOVA PROPRIEDADE --->
+
         self._apply_ltx_pipeline_patches()
 
         if all(w.device.type == 'cuda' for w in self.workers):
@@ -79,6 +84,8 @@ class LtxPoolManager:
         else:
             logger.info("LTX POOL MANAGER: Operating in CPU or mixed mode. GPU pre-warming skipped.")
 
+    # ... (O resto da classe LtxPoolManager, como _setup_dependencies, generate_latent_fragment, etc., permanece exatamente o mesmo) ...
+
     def _setup_dependencies(self):
         """Clones the LTX-Video repo if not found and adds it to the system path."""
         if not LTX_VIDEO_REPO_DIR.exists():
@@ -181,9 +188,9 @@ class LtxPoolManager:
                     gc.collect(); torch.cuda.empty_cache()
 
     def refine_latents(self, latents_to_refine: torch.Tensor, **kwargs) -> (torch.Tensor, tuple):
-        # This function can be expanded later if needed.
         pass
 
+# ... (O resto do arquivo: LtxWorker, _aduc_prepare_conditioning_patch, Singleton Instantiation, etc. permanece idêntico) ...
 class LtxWorker:
     """
     Represents a single instance of the LTX-Video pipeline on a specific device.