Update api/ltx_server_refactored.py

api/ltx_server_refactored.py

@@ -228,7 +228,7 @@ class VideoService:
             conditioning_items.append(ConditioningItem(tensor, safe_frame, float(weight)))
         return conditioning_items
 
-    def generate_low_old(self, prompt, negative_prompt, height, width, duration, guidance_scale, seed, conditioning_items=None):
+    def generate_low(self, prompt, negative_prompt, height, width, duration, guidance_scale, seed, conditioning_items=None):
         used_seed = random.randint(0, 2**32 - 1) if seed is None else int(seed)
         seed_everething(used_seed)
         FPS = 24.0
@@ -267,13 +267,17 @@ class VideoService:
             torch.cuda.ipc_collect()
             self.finalize(keep_paths=[])
 
+    # ==============================================================================
+    # --- FUNÇÃO #1: GERADOR DE CHUNK ÚNICO (AUXILIAR INTERNA) ---
+    # ==============================================================================
     def _generate_single_chunk_low(self, prompt, negative_prompt, height, width, num_frames, guidance_scale, seed, initial_latent_condition=None, image_conditions=None, ltx_configs_override=None):
         """
         [NÓ DE GERAÇÃO]
         Gera um ÚNICO chunk de latentes brutos. Esta é a unidade de trabalho fundamental.
         """
-        # (Esta função auxiliar permanece a mesma da nossa última versão, com a lógica de override)
         print("\n" + "-"*20 + " INÍCIO: _generate_single_chunk_low " + "-"*20)
+
+        # --- NÓ 1.1: SETUP DE PARÂMETROS ---
         height_padded = ((height - 1) // 8 + 1) * 8
         width_padded = ((width - 1) // 8 + 1) * 8
         generator = torch.Generator(device=self.device).manual_seed(seed)
@@ -286,6 +290,7 @@ class VideoService:
         x_height = int(height_padded * downscale_factor)
         downscaled_height = x_height - (x_height % vae_scale_factor)
         
+        # --- NÓ 1.2: MONTAGEM DE CONDIÇÕES E OVERRIDES ---
         all_conditions = []
         if image_conditions: all_conditions.extend(image_conditions)
         if initial_latent_condition: all_conditions.append(initial_latent_condition)
@@ -294,12 +299,23 @@ class VideoService:
 
         if ltx_configs_override:
             print("[DEBUG] Sobrepondo configurações do LTX com valores da UI...")
+            preset = ltx_configs_override.get("guidance_preset")
+            if preset == "Customizado":
+                try:
+                    first_pass_config["guidance_scale"] = json.loads(ltx_configs_override["guidance_scale_list"])
+                    first_pass_config["stg_scale"] = json.loads(ltx_configs_override["stg_scale_list"])
+                    first_pass_config["guidance_timesteps"] = json.loads(ltx_configs_override["timesteps_list"])
+                except Exception as e:
+                    print(f"  > ERRO ao parsear valores customizados: {e}. Usando Padrão como fallback.")
+            elif preset == "Agressivo":
+                first_pass_config["guidance_scale"] = [1, 2, 8, 12, 8, 2, 1]
+                first_pass_config["stg_scale"] = [0, 0, 5, 6, 5, 3, 2]
+            elif preset == "Suave":
+                first_pass_config["guidance_scale"] = [1, 1, 4, 5, 4, 1, 1]
+                first_pass_config["stg_scale"] = [0, 0, 2, 2, 2, 1, 0]
+            
             if "first_pass_num_inference_steps" in ltx_configs_override:
                 first_pass_config["num_inference_steps"] = ltx_configs_override["first_pass_num_inference_steps"]
-            #if "first_pass_guidance_scale" in ltx_configs_override:
-            #    max_val = max(first_pass_config.get("guidance_scale", [1]))
-            #    new_max_val = ltx_configs_override["first_pass_guidance_scale"]
-            #    first_pass_config["guidance_scale"] = [new_max_val if x==max_val else x for x in first_pass_config["guidance_scale"]]
         
         first_pass_kwargs = {
             "prompt": prompt, "negative_prompt": negative_prompt, "height": downscaled_height, "width": downscaled_width,
@@ -307,10 +323,8 @@ class VideoService:
             "conditioning_items": all_conditions if all_conditions else None,
             **first_pass_config
         }
-        # Removido guidance_scale daqui pois agora está dentro do first_pass_config
-        #if "guidance_scale" in first_pass_kwargs:
-        #    del first_pass_kwargs['guidance_scale']
-
+        
+        # --- NÓ 1.3: CHAMADA AO PIPELINE ---
         with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device.type == 'cuda'):
             latents_bruto = self.pipeline(**first_pass_kwargs).images
             log_tensor_info(latents_bruto, f"Latente Bruto Gerado para: '{prompt[:40]}...'")
@@ -318,6 +332,9 @@ class VideoService:
         print("-" * 20 + " FIM: _generate_single_chunk_low " + "-"*20)
         return latents_bruto
 
+    # ==============================================================================
+    # --- FUNÇÃO #2: ORQUESTRADOR NARRATIVO (MÚLTIPLOS PROMPTS) ---
+    # ==============================================================================
     def generate_narrative_low(self, prompt: str, negative_prompt, height, width, duration, guidance_scale, seed, initial_image_conditions=None, overlap_frames: int = 8, ltx_configs_override: dict = None):
         """
         [ORQUESTRADOR NARRATIVO]
@@ -377,9 +394,7 @@ class VideoService:
             
             latentes_bruto = self._generate_single_chunk_low(
                 prompt=chunk_prompt, negative_prompt=negative_prompt, height=height, width=width,
-                num_frames=num_frames_para_gerar, 
-                #guidance_scale=guidance_scale, 
-                seed=used_seed + i,
+                num_frames=num_frames_para_gerar, guidance_scale=guidance_scale, seed=used_seed + i,
                 initial_latent_condition=condition_item_latent_overlap, image_conditions=current_image_conditions,
                 ltx_configs_override=ltx_configs_override
             )
@@ -404,12 +419,15 @@ class VideoService:
         torch.save(final_latents.cpu(), tensor_path)
         
         with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device.type == 'cuda'):
-            pixel_tensor = vae_manager_singleton.decode(final_latents, decode_timestep=float(self.config.get("decode_timestep", 0.05)))
+            pixel_tensor = vae_manager_singleton.decode(final_latents, decode_timestep=float(ltx_configs_override.get("decode_timestep", 0.05) if ltx_configs_override else 0.05))
             video_path = self._save_and_log_video(pixel_tensor, "narrative_video", FPS, temp_dir, results_dir, used_seed)
         
         self.finalize(keep_paths=[video_path, tensor_path])
         return video_path, tensor_path, used_seed
-        
+
+    # ==============================================================================
+    # --- FUNÇÃO #3: ORQUESTRADOR SIMPLES (PROMPT ÚNICO) ---
+    # ==============================================================================
     def generate_single_low(self, prompt: str, negative_prompt, height, width, duration, guidance_scale, seed, initial_image_conditions=None, ltx_configs_override: dict = None):
         """
         [ORQUESTRADOR SIMPLES]
@@ -430,14 +448,9 @@ class VideoService:
 
         # Chama a função de geração de chunk único para fazer todo o trabalho
         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,
-            image_conditions=initial_image_conditions,
-            ltx_configs_override=ltx_configs_override
+            prompt=prompt, negative_prompt=negative_prompt, height=height, width=width,
+            num_frames=total_actual_frames, guidance_scale=guidance_scale, seed=used_seed,
+            image_conditions=initial_image_conditions, ltx_configs_override=ltx_configs_override
         )
 
         print("\n--- Finalizando Geração Simples: Salvando e decodificando ---")
@@ -447,12 +460,15 @@ class VideoService:
         torch.save(final_latents.cpu(), tensor_path)
 
         with torch.autocast(device_type="cuda", dtype=self.runtime_autocast_dtype, enabled=self.device.type == 'cuda'):
-            pixel_tensor = vae_manager_singleton.decode(final_latents, decode_timestep=float(self.config.get("decode_timestep", 0.05)))
+            pixel_tensor = vae_manager_singleton.decode(final_latents, decode_timestep=float(ltx_configs_override.get("decode_timestep", 0.05) if ltx_configs_override else 0.05))
             video_path = self._save_and_log_video(pixel_tensor, "single_video", FPS, temp_dir, results_dir, used_seed)
 
         self.finalize(keep_paths=[video_path, tensor_path])
         return video_path, tensor_path, used_seed
-        
+
+    # ==============================================================================
+    # --- FUNÇÃO #4: ORQUESTRADOR  (Upscaler + texturas hd) ---
+    # ==============================================================================
     def generate_upscale_denoise(self, latents_path, prompt, negative_prompt, guidance_scale, seed):
             used_seed = random.randint(0, 2**32 - 1) if seed is None else int(seed)
             seed_everething(used_seed)