Update deformes4D_engine.py

deformes4D_engine.py

@@ -144,10 +144,10 @@ class Deformes4DEngine:
             logger.warning(f"A poda ({trim_percent}%) era muito grande. Ajustada para {trim_chunks} chunks para deixar 1 chunk de vídeo.")
 
         # 3. Definir fatias e alvos com base nos cálculos
-        VIDEO_CHUNK_COUNT = total_chunks_gerados - trim_chunks -1
+        VIDEO_CHUNK_COUNT = total_chunks_gerados - trim_chunks
         
-        HANDLER_CHUNK_INDICES = slice(total_chunks_gerados - 1, total_chunks_gerados)
-        ECO_CHUNK_INDICES = slice(VIDEO_CHUNK_COUNT, VIDEO_CHUNK_COUNT + 2)
+        HANDLER_CHUNK_INDICES = slice(total_chunks_gerados - 3, total_chunks_gerados-1)
+        ECO_CHUNK_INDICES = slice(VIDEO_CHUNK_COUNT-1, VIDEO_CHUNK_COUNT + 1)
         
         HANDLER_FRAME_TARGET = ((total_chunks_gerados-1) - VIDEO_CHUNK_COUNT) * 8
         FRAMES_TO_GENERATE = (((total_chunks_gerados)-1) * 8) +1
@@ -205,7 +205,7 @@ class Deformes4DEngine:
                conditioning_items.append(LatentConditioningItem(handler_latent_for_next_loop, HANDLER_FRAME_TARGET, handler_strength))
             
             img_dest = self._preprocess_image_for_latent_conversion(Image.open(destination_keyframe_path).convert("RGB"), target_resolution_tuple)
-            conditioning_items.append(LatentConditioningItem(self.pil_to_latent(img_dest), DESTINATION_FRAME_TARGET-1, destination_convergence_strength))
+            conditioning_items.append(LatentConditioningItem(self.pil_to_latent(img_dest), DESTINATION_FRAME_TARGET, destination_convergence_strength))
 
             current_ltx_params = {**base_ltx_params, "motion_prompt": motion_prompt}
             new_full_latents = self._generate_latent_tensor_internal(conditioning_items, current_ltx_params, target_resolution_tuple, FRAMES_TO_GENERATE)
@@ -215,7 +215,7 @@ class Deformes4DEngine:
             handler_latent_for_next_loop = new_full_latents[:, :, HANDLER_CHUNK_INDICES, :, :].clone()
             logger.info(f"  [GUIAS] Guias para a próxima iteração extraídas. Eco shape: {eco_latent_for_next_loop.shape}, Handler shape: {handler_latent_for_next_loop.shape}.")
 
-            latents_for_video = new_full_latents[:, :, :VIDEO_CHUNK_COUNT, :, :]
+            latents_for_video = new_full_latents[:, :, :VIDEO_CHUNK_COUNT-1, :, :]
             logger.info(f"  [2. EDIÇÃO] Tensor final para vídeo extraído com {latents_for_video.shape[2]} chunks.")
 
             base_name = f"fragment_{i}_{int(time.time())}"