Update api/ltx_server_refactored.py

api/ltx_server_refactored.py

@@ -270,7 +270,12 @@ class VideoService:
     # ==============================================================================
     # --- 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):
+    def _generate_single_chunk_low(
+        self, prompt, negative_prompt, 
+        height, width, num_frames, guidance_scale, 
+        seed, initial_latent_condition=None, image_conditions=None, 
+        fp_num_inference_steps:int=30, ship_initial_inference_steps:int=0, ship_final_inference_steps:int=0,
+        ltx_configs_override=None):
         """
         [NÓ DE GERAÇÃO]
         Gera um ÚNICO chunk de latentes brutos. Esta é a unidade de trabalho fundamental.
@@ -304,7 +309,7 @@ class VideoService:
                 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"])
+                    #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":
@@ -314,9 +319,21 @@ class VideoService:
                 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:
+            if fp_num_inference_steps>0:
+                first_pass_config["num_inference_steps"] = fp_num_inference_steps
+            else:
                 first_pass_config["num_inference_steps"] = ltx_configs_override["first_pass_num_inference_steps"]
-        
+
+            if ship_initial_inference_steps>0:
+                first_pass_config["skip_initial_inference_steps"] = ship_initial_inference_steps
+            else:
+                first_pass_config["skip_initial_inference_steps"] = ltx_configs_override["skip_initial_inference_steps"]
+                
+            if ship_final_inference_steps>0:
+                first_pass_config["skip_final_inference_steps"] = ship_final_inference_steps
+            else:
+                first_pass_config["skip_final_inference_steps"] = ltx_configs_override["skip_final_inference_steps"]
+                
         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",
@@ -335,7 +352,12 @@ class VideoService:
     # ==============================================================================
     # --- 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):
+    def generate_narrative_low(
+        self, prompt: str, negative_prompt, 
+        height, width, duration, guidance_scale, 
+        seed, initial_image_conditions=None, overlap_frames: int = 8,
+        fp_num_inference_steps:int=30, ship_initial_inference_steps:int=0, ship_final_inference_steps:int=0,
+        ltx_configs_override: dict = None):
         """
         [ORQUESTRADOR NARRATIVO]
         Gera um vídeo em múltiplos chunks sequenciais a partir de um prompt com várias linhas.
@@ -353,6 +375,7 @@ class VideoService:
         if num_chunks == 0: raise ValueError("O prompt está vazio ou não contém linhas válidas.")
         
         total_actual_frames = max(9, int(round((round(duration * FPS) - 1) / 8.0) * 8 + 1))
+
         
         if num_chunks > 1:
             total_blocks = (total_actual_frames - 1) // 8
@@ -396,6 +419,7 @@ class VideoService:
                 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,
                 initial_latent_condition=condition_item_latent_overlap, image_conditions=current_image_conditions,
+                fp_num_inference_steps=fp_num_inference_steps, ship_initial_inference_steps=ship_initial_inference_steps, ship_final_inference_steps=ship_final_inference_steps,
                 ltx_configs_override=ltx_configs_override
             )
             
@@ -428,7 +452,12 @@ class VideoService:
     # ==============================================================================
     # --- 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):
+    def generate_single_low(
+        self, prompt: str, negative_prompt, 
+        height, width, duration, guidance_scale, 
+        seed, initial_image_conditions=None,
+        fp_num_inference_steps:int=30, ship_initial_inference_steps:int=0, ship_final_inference_steps:int=0,
+        ltx_configs_override: dict = None):
         """
         [ORQUESTRADOR SIMPLES]
         Gera um vídeo completo em um único chunk. Ideal para prompts simples e curtos.
@@ -450,7 +479,9 @@ class VideoService:
         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
+            image_conditions=initial_image_conditions, 
+            fp_num_inference_steps=fp_num_inference_steps, ship_initial_inference_steps=ship_initial_inference_steps, ship_final_inference_steps=ship_final_inference_steps,
+            ltx_configs_override=ltx_configs_override
         )
 
         print("\n--- Finalizando Geração Simples: Salvando e decodificando ---")
@@ -469,7 +500,11 @@ class VideoService:
     # ==============================================================================
     # --- FUNÇÃO #4: ORQUESTRADOR  (Upscaler + texturas hd) ---
     # ==============================================================================
-    def generate_upscale_denoise(self, latents_path, prompt, negative_prompt, guidance_scale, seed):
+    def generate_upscale_denoise(
+        self, latents_path, prompt, negative_prompt, 
+        guidance_scale, seed,
+        fp_num_inference_steps:int=30, ship_initial_inference_steps:int=0, ship_final_inference_steps:int=0
+    ):
             used_seed = random.randint(0, 2**32 - 1) if seed is None else int(seed)
             seed_everething(used_seed)
             temp_dir = tempfile.mkdtemp(prefix="ltxv_up_"); self._register_tmp_dir(temp_dir)