Update api/ltx/ltx_aduc_pipeline.py

api/ltx/ltx_aduc_pipeline.py

@@ -199,6 +199,7 @@ class LtxAducPipeline:
             # --- ETAPA 1: GERAÇÃO DE CHUNKS E SALVAMENTO ---
             for i, chunk_prompt in enumerate(prompt_list):
                 logging.info(f"Processing scene {i+1}/{num_chunks}: '{chunk_prompt[:50]}...'")
+                call_kwargs.pop("prompt", None)
                 call_kwargs["prompt"] = chunk_prompt
                 
                 with torch.autocast(device_type=self.main_device.type, dtype=self.runtime_autocast_dtype, enabled="cuda" in self.main_device.type):
@@ -207,11 +208,14 @@ class LtxAducPipeline:
 
                 if is_narrative and i < num_chunks - 1:
                     overlap_latents = chunk_latents[:, :, -overlap_frames:, :, :].clone()
-                    call_kwargs["conditioning_items"] = [LatentConditioningItem(overlap_latents, 0, 1.0)]
+                    #call_kwargs["conditioning_items"] = [LatentConditioningItem(overlap_latents, 0, 1.0)]
                 else:
-                    call_kwargs.pop("conditioning_items", None)
-                    
-                if i > 0: chunk_latents = chunk_latents[:, :, overlap_frames:, :, :]
+                    #call_kwargs.pop("conditioning_items", None)
+
+                print (f"overlap_latents {overlap_latents.shape}")
+                print (f"chunk_latents {chunk_latents.shape}")
+           
+                #if i > 0: chunk_latents = chunk_latents[:, :, overlap_frames:, :, :]
                 
                 chunk_path = RESULTS_DIR / f"temp_chunk_{i}_{used_seed}.pt"
                 # --- NOVO: Rastreia o arquivo para limpeza ---