import random import time import urllib.request import json import gc import torch import comfy.model_management as mm from . import register_node global_loop_index = 0 global_accumulated_frames = 0 global_source_frame_count = 1 global_select_every_nth = 1 global_server_port = 8188 # 💡 NUEVO: Única fuente de la verdad para el puerto global_ltx_mode = False global_step_by_chunk = False @register_node class SequentialLoopStart: @classmethod def INPUT_TYPES(s): return { "required": { "reset_loop": ("BOOLEAN", {"default": False}), "loop_idx": ("INT", {"default": 0, "min": 0, "max": 10000}), "port": ("INT", {"default": 8188, "min": 1, "max": 65535}), # 💡 FAIL-FAST: Pedimos el puerto aquí } } RETURN_TYPES = ("INT",) RETURN_NAMES = ("current_loop_index",) FUNCTION = "get_index" CATEGORY = "🔁 Sequential Batcher/Loop" @classmethod def IS_CHANGED(cls, **kwargs): return time.time() def get_index(self, reset_loop, loop_idx, port): global global_loop_index global global_accumulated_frames global global_server_port global global_ltx_mode print(f"\n{'='*50}") print(f"🚀 [DEBUG] NODO: Loop Start (Motor Dinámico)") # --- PRE-FLIGHT CHECK (FAIL-FAST) --- print(f" -> 📡 Verificando conexión con ComfyUI en el puerto {port}...") try: # Ping ultrarrápido a la API. Si falla, colapsa en 0.1 segundos. req = urllib.request.Request(f"http://127.0.0.1:{port}/system_stats") urllib.request.urlopen(req, timeout=2) global_server_port = port # Guardamos el puerto correcto para el Trigger print(f" -> ✅ Conexión establecida. Puerto blindado.") except Exception as e: print(f" -> ❌ ERROR FATAL: No se pudo conectar al puerto {port}.") raise ValueError(f"🚨 EL PUERTO {port} ES INCORRECTO O COMFYUI NO RESPONDE. " f"Cambia el puerto en el nodo 'Loop Start' antes de procesar. (Error: {e})") # ------------------------------------ is_reset = str(reset_loop).lower() in ['true', '1', 't', 'y'] if is_reset or loop_idx == 0: global_loop_index = 0 global_accumulated_frames = 0 global_ltx_mode = False # 💡 REINICIO DE SEGURIDAD PARA LTX print(" -> 🔄 Bucle y Acumulador reiniciados a 0.") else: global_loop_index = loop_idx print(f" -> 📍 Índice actual de bucle: {global_loop_index}") print(f"{'='*50}\n") return (global_loop_index,) @register_node class AutoLoopCalculatorLTX: @classmethod def INPUT_TYPES(s): return { "required": { "source_frame_count": ("INT", {"forceInput": True}), "target_frames_per_loop": ("INT", {"default": 81, "min": 9, "max": 257, "step": 8}), "select_every_nth": ("INT", {"default": 1, "min": 1, "max": 100}), "current_loop_index": ("INT", {"forceInput": True}), }, "optional": { "safe_faces_list": ("FACE_CUTS", {"forceInput": True}), "scene_cuts_list": ("SCENE_CUTS", {"forceInput": True}), } } RETURN_TYPES = ("INT", "INT", "INT") RETURN_NAMES = ("chunk_frames", "skip_frames", "select_every_nth") FUNCTION = "calculate" CATEGORY = "🔁 Sequential Batcher/Loop" def calculate(self, source_frame_count, target_frames_per_loop, select_every_nth, current_loop_index, safe_faces_list=None, scene_cuts_list=None): if source_frame_count <= 0 or target_frames_per_loop <= 0: return (1, 0, select_every_nth) source_frame_count = min(source_frame_count, 10000) select_every_nth = max(1, select_every_nth) # --- Ajuste Proporcional LTX (Regla DiT 8n + 1) --- import math from . import loop loop.global_step_by_chunk = False potential_effective_frames = source_frame_count // select_every_nth if potential_effective_frames < 9: safe_effective_frames = 9 else: safe_effective_frames = ((potential_effective_frames + 6) // 8) * 8 + 1 # 🚀 FIX: Conservamos el límite FÍSICO real del vídeo para el timeline global physical_source_frame_count = potential_effective_frames * select_every_nth loop.global_source_frame_count = physical_source_frame_count effective_padding = safe_effective_frames - potential_effective_frames # Ajuste proporcional estimated_loops = math.ceil(safe_effective_frames / target_frames_per_loop) if estimated_loops > 0: optimal_target = math.ceil(safe_effective_frames / estimated_loops) adjusted_target = ((optimal_target - 1 + 7) // 8) * 8 + 1 target_frames_per_loop = adjusted_target print(f" -> ⚖️ Ajuste Proporcional LTX: Target recalculado a {adjusted_target} frames por ciclo.") print(f" -> 🎞️ Capacidad del video original: {potential_effective_frames} frames (Nth: {select_every_nth})") if effective_padding > 0: print(f" -> 🛡️ Ajuste VAE: Se pedirán {safe_effective_frames} frames (Acolchado técnico: Se rellenarán {effective_padding} frames)") else: print(f" -> ✅ Ajuste VAE: Perfecto. Regla 8n+1 detectada.") print(f" -> 📊 Timeline final: 0 a {physical_source_frame_count} (Límite Físico Real)") loop.global_select_every_nth = select_every_nth global global_ltx_mode global_ltx_mode = True current_pos = loop.global_accumulated_frames print(f"\n{'='*50}") print(f"📊 [DEBUG] NODO: Auto Loop Calculator (LTX 2.3)") print(f" -> Timeline físico ajustado a LTX: {current_pos} / {physical_source_frame_count} (Original: {source_frame_count})") if current_pos >= physical_source_frame_count: return (1, current_pos, select_every_nth) frames_left = physical_source_frame_count - current_pos equitable_target = target_frames_per_loop * select_every_nth ideal_cut = current_pos + equitable_target if frames_left <= equitable_target: best_cut = physical_source_frame_count print(f" -> 🧮 Absorbiendo resto final: meta fijada en frame {best_cut}") else: if scene_cuts_list and len(scene_cuts_list) > 0: future_cuts = [c for c in scene_cuts_list if c > current_pos] if future_cuts: next_cut = min(future_cuts) if next_cut <= ideal_cut: chunk_from_scene = math.ceil((next_cut - current_pos) / select_every_nth) safe_chunk_from_scene = ((chunk_from_scene + 6) // 8) * 8 + 1 if safe_chunk_from_scene < 9: safe_chunk_from_scene = 9 best_cut = current_pos + (safe_chunk_from_scene * select_every_nth) print(f" -> 🎬 Bucle aislado por escena (Ajustado x8). Cortando en el frame: {best_cut} (Corte real: {next_cut})") else: best_cut = ideal_cut print(f" -> ⚠️ Toma demasiado larga para la VRAM. Cortando por límite técnico en: {best_cut} (El plano real acaba en {next_cut})") else: best_cut = ideal_cut print(f" -> 🎬 No quedan cortes de cámara por delante. Forzando corte final por VRAM en: {ideal_cut}") elif safe_faces_list and len(safe_faces_list) > 0: closest_face = min(safe_faces_list, key=lambda x: abs(x - ideal_cut)) chunk_from_face = math.ceil((closest_face - current_pos) / select_every_nth) safe_chunk_from_face = ((chunk_from_face + 6) // 8) * 8 + 1 if safe_chunk_from_face < 9: safe_chunk_from_face = 9 best_cut = current_pos + (safe_chunk_from_face * select_every_nth) print(f" -> ✂️ Corte Facial Inteligente (Ajustado x8): {best_cut} (Meta equitativa: {ideal_cut})") else: best_cut = ideal_cut print(f" -> ⚖️ Sin detectores conectados. Forzando corte equitativo x8: {ideal_cut}") effective_chunk_frames = math.ceil((best_cut - current_pos) / select_every_nth) effective_chunk_frames = ((effective_chunk_frames + 6) // 8) * 8 + 1 if effective_chunk_frames < 9: effective_chunk_frames = 9 if current_pos + (effective_chunk_frames * select_every_nth) >= physical_source_frame_count: print(f" -> 🏁 Chunk final LTX detectado. Ajustando a {effective_chunk_frames} frames para mantener regla 8n+1.") loop.global_is_final_chunk = True else: loop.global_is_final_chunk = False skip_frames = current_pos print(f" -> 🚀 Ciclo {current_loop_index}: Solicitando {effective_chunk_frames} frames efectivos (Saltando {skip_frames})") print(f"{'='*50}\n") return (effective_chunk_frames, skip_frames, select_every_nth) @register_node class SequentialLoopTrigger: @classmethod def INPUT_TYPES(s): return { "required": { "trigger_dependency": ("*", ), }, "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"} } RETURN_TYPES = () RETURN_NAMES = () OUTPUT_NODE = True FUNCTION = "trigger_next" CATEGORY = "🔁 Sequential Batcher/Loop" @classmethod def IS_CHANGED(cls, **kwargs): return time.time() def trigger_next(self, trigger_dependency, prompt=None, extra_pnginfo=None): global global_loop_index global global_accumulated_frames global global_source_frame_count global global_server_port next_loop = global_loop_index + 1 is_final = global_accumulated_frames >= global_source_frame_count print(f"\n{'='*50}") print(f"🎯 [DEBUG] NODO: Loop Trigger") print(f" -> Progreso del vídeo: {global_accumulated_frames} / {global_source_frame_count} frames.") if not is_final: print(f" -> ⚙️ Preparando Ciclo {next_loop}...") if prompt is not None: # 🧠 HACKER MODE: Estimamos si el ciclo que vamos a encolar es el final estimated_chunk = 50 found_calculator = False m_seeds = 0 master_switches = [] for node_id, node_data in prompt.items(): class_type = node_data.get("class_type", "") inputs = node_data.get("inputs", {}) if not found_calculator and "AutoLoopCalculator" in class_type: if class_type == "AutoLoopCalculatorTTS": estimated_chunk = 1 else: estimated_chunk = inputs.get("target_frames_per_loop", 50) found_calculator = True # Mutación de semillas for key in ["seed", "noise_seed"]: if key in inputs and isinstance(inputs[key], (int, float)): inputs[key] = random.randint(1, 0xffffffff) m_seeds += 1 # Mutación de índice if class_type == "SequentialLoopStart": inputs["loop_idx"] = next_loop inputs["reset_loop"] = False # 🔪 Recolectamos Master Switch para cirugía diferida if class_type == "MasterSwitch": master_switches.append(inputs) is_next_final = (global_accumulated_frames + estimated_chunk) >= global_source_frame_count for inputs in master_switches: print(f" -> 🔀 [Cirugía de Grafo Segura] Mutando Master Switch para Ciclo {next_loop}...") # Sobrescribimos el cable que viene del Stitcher por un booleano estático inputs["is_final_cycle"] = is_next_final # 🛑 Eliminada la destrucción de cables (del inputs["on_true"/"on_false"]) # Confiamos en la Evaluación Perezosa Nativa del MasterSwitch. print(f" -> 🎲 Semillas mutadas: {m_seeds}") p = {"prompt": prompt} if extra_pnginfo: p["extra_data"] = {"extra_pnginfo": extra_pnginfo} data = json.dumps(p).encode('utf-8') req = urllib.request.Request(f"http://127.0.0.1:{global_server_port}/prompt", data=data, headers={'Content-Type': 'application/json'}) try: urllib.request.urlopen(req, timeout=5) print(f" -> ✅ Ciclo {next_loop} inyectado en la cola.") except Exception as e: print(f" -> ❌ Error HTTP: {e}") # Limpieza ligera por ciclo para evitar fragmentación import gc import torch import comfy.model_management as mm gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() else: print(f" -> 🏁 ¡Generación Finalizada! Todos los frames ensamblados.") # --- LIMPIEZA EXTREMA DE VRAM AUTOMÁTICA (Multi-Plataforma) --- print(f" -> 🧹 Iniciando vaciado automático de VRAM...") import gc import torch import comfy.model_management as mm try: # 1. Obligamos al motor interno de ComfyUI a soltar los modelos mm.unload_all_models() mm.soft_empty_cache() except Exception as e: print(f" -> ⚠️ Aviso: No se pudo usar model_management: {e}") # 2. Forzamos al recolector de basura de Python gc.collect() # 3. Le arrancamos a PyTorch la memoria reservada (CUDA, ROCm y Mac MPS) if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() # HACK: Forzar la desfragmentación de la memoria caché de PyTorch import ctypes try: ctypes.CDLL('libc.so.6').malloc_trim(0) except Exception: pass elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): torch.mps.empty_cache() print(f" -> ✨ VRAM liberada con éxito. Gráfica lista para nuevos flujos.") print(f"{'='*50}\n") return ()