Update api/ltx_server_refactored.py

api/ltx_server_refactored.py

@@ -283,7 +283,7 @@ class VideoService:
     def _generate_single_chunk_low(
         self, prompt, negative_prompt, 
         height, width, num_frames, guidance_scale, 
-        seed, itens_conditions=None, 
+        seed, itens_conditions_itens=None, 
         ltx_configs_override=None):
         """
         [NÓ DE GERAÇÃO]
@@ -333,7 +333,7 @@ class VideoService:
         first_pass_kwargs = {
             "prompt": prompt, "negative_prompt": negative_prompt, "height": downscaled_height, "width": downscaled_width,
             "num_frames": num_frames, "frame_rate": 24, "generator": generator, "output_type": "latent",
-            #"conditioning_items": itens_conditions
+            "conditioning_items": itens_conditions_itens
             **first_pass_config
         }
 
@@ -371,7 +371,7 @@ class VideoService:
     def generate_narrative_low(
         self, prompt: str, negative_prompt, 
         height, width, duration, guidance_scale, 
-        seed, initial_image_conditions=None, overlap_frames: int = 8,
+        seed, initial_conditions=None, overlap_frames: int = 8,
         ltx_configs_override: dict = None):
         """
         [ORQUESTRADOR NARRATIVO]
@@ -443,24 +443,26 @@ class VideoService:
             latentes_bruto_r = self._generate_single_chunk_low(
                 prompt=chunk_prompt, negative_prompt=negative_prompt, height=height, width=width,
                 num_frames=frames_per_chunk, guidance_scale=guidance_scale, seed=used_seed + i,
-                itens_conditions=None, 
+                itens_conditions_itens=None, 
                 ltx_configs_override=ltx_configs_override
             )
 
 
-            
-
-            
             print(f"[DEBUG]  generate_narrative_low.frames_per_chunk: {frames_per_chunk}")
-
-    
             log_tensor_info(latentes_bruto_r, f"latentes_bruto_r recebidk: {i}...'")
         
             #latent_path_bufer = load_tensor(latent_path)
             #final_latents = torch.cat(lista_tensores, dim=2).to(self.device)
 
-        
 
+            if i== 0:
+                initial_conditions = None
+            else:
+                initial_conditions = initial_conditions
+                
+                
+            
+            
             #poda inicio overlap
             if i > 0 and poda_latents_num > 0 and latentes_bruto_r.shape[2]>poda_latents_num:
                  latentes_bruto = latentes_bruto_r[:, :, poda_latents_num:, :, :].clone()
@@ -517,7 +519,7 @@ class VideoService:
     def generate_single_low(
         self, prompt: str, negative_prompt, 
         height, width, duration, guidance_scale, 
-        seed, initial_image_conditions=None,
+        seed, initial_conditions=None,
         ltx_configs_override: dict = None):
         """
         [ORQUESTRADOR SIMPLES]
@@ -537,28 +539,25 @@ class VideoService:
         results_dir = "/app/output"; os.makedirs(results_dir, exist_ok=True)
 
         # Chama a função de geração de chunk único para fazer todo o trabalho
-        latent_path = self._generate_single_chunk_low(
+        final_latents = self._generate_single_chunk_low(
             prompt=prompt, negative_prompt=negative_prompt, height=height, width=width,
             num_frames=total_actual_frames, guidance_scale=guidance_scale, seed=used_seed,
-            itens_conditions=initial_image_conditions, 
+            itens_conditions_itens=initial_conditions, 
             ltx_configs_override=ltx_configs_override
         )
 
-        print(f"[DEBUG]  generate_single_low.total_actual_frames: {total_actual_frames}")
-        
-
-        final_latents = torch.load(latent_path).to(self.device)
         print("\n--- Finalizando Geração Simples: Salvando e decodificando ---")
         log_tensor_info(final_latents, "Tensor de Latentes Final")
 
         try: 
             with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device.type == 'cuda'):
-                pixel_tensor = vae_manager_singleton.decode(final_latents.clone(), decode_timestep=float(self.config.get("decode_timestep", 0.05)))
-                video_path = self._save_and_log_video(pixel_tensor, "single_video", FPS, temp_dir, results_dir, used_seed)
-                latents_cpu = final_latents.detach().to("cpu")
-                tensor_path = os.path.join(results_dir, f"latents_single.pt")
-                torch.save(latents_cpu, tensor_path)
-            return video_path, tensor_path, used_seed
+                pixel_tensor = vae_manager_singleton.decode(final_latents, decode_timestep=float(self.config.get("decode_timestep", 0.05)))
+                pixel_tensor_cpu = pixel_tensor.detach().to("cpu")
+                video_path = self._save_and_log_video(pixel_tensor_cpu, "narrative_video", FPS, temp_dir, results_dir, used_seed)
+                final_latents_cpu = final_latents.detach().to("cpu")
+                final_latents_patch = os.path.join(results_dir, f"latents_low_fim.pt")
+                torch.save(final_latents_cpu, final_latents_patch)
+            return video_path, final_latents_patch, used_seed
         except Exception as e:
             print("-" * 20 + " ERRO: generate_single_low --------------------")
             traceback.print_exc()