Meisoftcoltd
3afcee2bef
Added input validations (`source_frame_count <= 0` or `target_frames_per_loop <= 0`) to `AutoLoopCalculatorLTX` (in `loop.py`), and `AutoLoopCalculator`, `AutoLoopCalculatorWan` (in `video.py`). When conditions are met, a safe default is returned to prevent math domain errors. Also included a new comprehensive unit test suite in `tests/test_loop_calculator.py`.
342 lines
15 KiB
Python
342 lines
15 KiB
Python
import random
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import time
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import urllib.request
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import json
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import gc
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import torch
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import comfy.model_management as mm
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from . import register_node
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global_loop_index = 0
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global_accumulated_frames = 0
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global_source_frame_count = 1
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global_select_every_nth = 1
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global_server_port = 8188 # 💡 NUEVO: Única fuente de la verdad para el puerto
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global_ltx_mode = False
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global_step_by_chunk = False
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@register_node
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class SequentialLoopStart:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"reset_loop": ("BOOLEAN", {"default": False}),
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"loop_idx": ("INT", {"default": 0, "min": 0, "max": 10000}),
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"port": ("INT", {"default": 8188, "min": 1, "max": 65535}), # 💡 FAIL-FAST: Pedimos el puerto aquí
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}
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}
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RETURN_TYPES = ("INT",)
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RETURN_NAMES = ("current_loop_index",)
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FUNCTION = "get_index"
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CATEGORY = "🔁 Sequential Batcher/Loop"
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return time.time()
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def get_index(self, reset_loop, loop_idx, port):
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global global_loop_index
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global global_accumulated_frames
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global global_server_port
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global global_ltx_mode
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print(f"\n{'='*50}")
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print(f"🚀 [DEBUG] NODO: Loop Start (Motor Dinámico)")
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# --- PRE-FLIGHT CHECK (FAIL-FAST) ---
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print(f" -> 📡 Verificando conexión con ComfyUI en el puerto {port}...")
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try:
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# Ping ultrarrápido a la API. Si falla, colapsa en 0.1 segundos.
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req = urllib.request.Request(f"http://127.0.0.1:{port}/system_stats")
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urllib.request.urlopen(req, timeout=2)
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global_server_port = port # Guardamos el puerto correcto para el Trigger
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print(f" -> ✅ Conexión establecida. Puerto blindado.")
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except Exception as e:
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print(f" -> ❌ ERROR FATAL: No se pudo conectar al puerto {port}.")
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raise ValueError(f"🚨 EL PUERTO {port} ES INCORRECTO O COMFYUI NO RESPONDE. "
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f"Cambia el puerto en el nodo 'Loop Start' antes de procesar. (Error: {e})")
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# ------------------------------------
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is_reset = str(reset_loop).lower() in ['true', '1', 't', 'y']
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if is_reset or loop_idx == 0:
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global_loop_index = 0
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global_accumulated_frames = 0
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global_ltx_mode = False # 💡 REINICIO DE SEGURIDAD PARA LTX
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print(" -> 🔄 Bucle y Acumulador reiniciados a 0.")
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else:
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global_loop_index = loop_idx
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print(f" -> 📍 Índice actual de bucle: {global_loop_index}")
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print(f"{'='*50}\n")
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return (global_loop_index,)
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@register_node
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class AutoLoopCalculatorLTX:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"source_frame_count": ("INT", {"forceInput": True}),
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"target_frames_per_loop": ("INT", {"default": 81, "min": 9, "max": 257, "step": 8}),
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"select_every_nth": ("INT", {"default": 1, "min": 1, "max": 100}),
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"current_loop_index": ("INT", {"forceInput": True}),
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},
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"optional": {
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"safe_faces_list": ("FACE_CUTS", {"forceInput": True}),
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"scene_cuts_list": ("SCENE_CUTS", {"forceInput": True}),
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}
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}
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RETURN_TYPES = ("INT", "INT", "INT")
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RETURN_NAMES = ("chunk_frames", "skip_frames", "select_every_nth")
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FUNCTION = "calculate"
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CATEGORY = "🔁 Sequential Batcher/Loop"
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def calculate(self, source_frame_count, target_frames_per_loop, select_every_nth, current_loop_index, safe_faces_list=None, scene_cuts_list=None):
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if source_frame_count <= 0 or target_frames_per_loop <= 0:
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return (1, 0, select_every_nth)
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source_frame_count = min(source_frame_count, 10000)
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select_every_nth = max(1, select_every_nth)
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# --- Ajuste Proporcional LTX (Regla DiT 8n + 1) ---
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import math
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from . import loop
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loop.global_step_by_chunk = False
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potential_effective_frames = source_frame_count // select_every_nth
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if potential_effective_frames < 9:
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safe_effective_frames = 9
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else:
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safe_effective_frames = ((potential_effective_frames + 6) // 8) * 8 + 1
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# 🚀 FIX: Conservamos el límite FÍSICO real del vídeo para el timeline global
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physical_source_frame_count = potential_effective_frames * select_every_nth
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loop.global_source_frame_count = physical_source_frame_count
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effective_padding = safe_effective_frames - potential_effective_frames
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# Ajuste proporcional
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estimated_loops = math.ceil(safe_effective_frames / target_frames_per_loop)
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if estimated_loops > 0:
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optimal_target = math.ceil(safe_effective_frames / estimated_loops)
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adjusted_target = ((optimal_target - 1 + 7) // 8) * 8 + 1
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target_frames_per_loop = adjusted_target
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print(f" -> ⚖️ Ajuste Proporcional LTX: Target recalculado a {adjusted_target} frames por ciclo.")
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print(f" -> 🎞️ Capacidad del video original: {potential_effective_frames} frames (Nth: {select_every_nth})")
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if effective_padding > 0:
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print(f" -> 🛡️ Ajuste VAE: Se pedirán {safe_effective_frames} frames (Acolchado técnico: Se rellenarán {effective_padding} frames)")
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else:
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print(f" -> ✅ Ajuste VAE: Perfecto. Regla 8n+1 detectada.")
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print(f" -> 📊 Timeline final: 0 a {physical_source_frame_count} (Límite Físico Real)")
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loop.global_select_every_nth = select_every_nth
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global global_ltx_mode
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global_ltx_mode = True
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current_pos = loop.global_accumulated_frames
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print(f"\n{'='*50}")
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print(f"📊 [DEBUG] NODO: Auto Loop Calculator (LTX 2.3)")
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print(f" -> Timeline físico ajustado a LTX: {current_pos} / {physical_source_frame_count} (Original: {source_frame_count})")
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if current_pos >= physical_source_frame_count:
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return (1, current_pos, select_every_nth)
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frames_left = physical_source_frame_count - current_pos
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equitable_target = target_frames_per_loop * select_every_nth
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ideal_cut = current_pos + equitable_target
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if frames_left <= equitable_target:
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best_cut = physical_source_frame_count
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print(f" -> 🧮 Absorbiendo resto final: meta fijada en frame {best_cut}")
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else:
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if scene_cuts_list and len(scene_cuts_list) > 0:
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future_cuts = [c for c in scene_cuts_list if c > current_pos]
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if future_cuts:
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next_cut = min(future_cuts)
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if next_cut <= ideal_cut:
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chunk_from_scene = math.ceil((next_cut - current_pos) / select_every_nth)
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safe_chunk_from_scene = ((chunk_from_scene + 6) // 8) * 8 + 1
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if safe_chunk_from_scene < 9: safe_chunk_from_scene = 9
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best_cut = current_pos + (safe_chunk_from_scene * select_every_nth)
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print(f" -> 🎬 Bucle aislado por escena (Ajustado x8). Cortando en el frame: {best_cut} (Corte real: {next_cut})")
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else:
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best_cut = ideal_cut
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print(f" -> ⚠️ Toma demasiado larga para la VRAM. Cortando por límite técnico en: {best_cut} (El plano real acaba en {next_cut})")
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else:
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best_cut = ideal_cut
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print(f" -> 🎬 No quedan cortes de cámara por delante. Forzando corte final por VRAM en: {ideal_cut}")
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elif safe_faces_list and len(safe_faces_list) > 0:
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closest_face = min(safe_faces_list, key=lambda x: abs(x - ideal_cut))
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chunk_from_face = math.ceil((closest_face - current_pos) / select_every_nth)
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safe_chunk_from_face = ((chunk_from_face + 6) // 8) * 8 + 1
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if safe_chunk_from_face < 9: safe_chunk_from_face = 9
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best_cut = current_pos + (safe_chunk_from_face * select_every_nth)
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print(f" -> ✂️ Corte Facial Inteligente (Ajustado x8): {best_cut} (Meta equitativa: {ideal_cut})")
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else:
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best_cut = ideal_cut
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print(f" -> ⚖️ Sin detectores conectados. Forzando corte equitativo x8: {ideal_cut}")
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effective_chunk_frames = math.ceil((best_cut - current_pos) / select_every_nth)
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effective_chunk_frames = ((effective_chunk_frames + 6) // 8) * 8 + 1
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if effective_chunk_frames < 9:
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effective_chunk_frames = 9
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if current_pos + (effective_chunk_frames * select_every_nth) >= physical_source_frame_count:
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print(f" -> 🏁 Chunk final LTX detectado. Ajustando a {effective_chunk_frames} frames para mantener regla 8n+1.")
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loop.global_is_final_chunk = True
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else:
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loop.global_is_final_chunk = False
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skip_frames = current_pos
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print(f" -> 🚀 Ciclo {current_loop_index}: Solicitando {effective_chunk_frames} frames efectivos (Saltando {skip_frames})")
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print(f"{'='*50}\n")
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return (effective_chunk_frames, skip_frames, select_every_nth)
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@register_node
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class SequentialLoopTrigger:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"trigger_dependency": ("*", ),
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},
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}
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}
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RETURN_TYPES = ()
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RETURN_NAMES = ()
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OUTPUT_NODE = True
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FUNCTION = "trigger_next"
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CATEGORY = "🔁 Sequential Batcher/Loop"
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return time.time()
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def trigger_next(self, trigger_dependency, prompt=None, extra_pnginfo=None):
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global global_loop_index
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global global_accumulated_frames
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global global_source_frame_count
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global global_server_port
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next_loop = global_loop_index + 1
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is_final = global_accumulated_frames >= global_source_frame_count
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print(f"\n{'='*50}")
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print(f"🎯 [DEBUG] NODO: Loop Trigger")
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print(f" -> Progreso del vídeo: {global_accumulated_frames} / {global_source_frame_count} frames.")
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if not is_final:
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print(f" -> ⚙️ Preparando Ciclo {next_loop}...")
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if prompt is not None:
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# 🧠 HACKER MODE: Estimamos si el ciclo que vamos a encolar es el final
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estimated_chunk = 50
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found_calculator = False
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m_seeds = 0
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master_switches = []
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for node_id, node_data in prompt.items():
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class_type = node_data.get("class_type", "")
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inputs = node_data.get("inputs", {})
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if not found_calculator and "AutoLoopCalculator" in class_type:
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if class_type == "AutoLoopCalculatorTTS":
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estimated_chunk = 1
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else:
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estimated_chunk = inputs.get("target_frames_per_loop", 50)
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found_calculator = True
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# Mutación de semillas
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for key in ["seed", "noise_seed"]:
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if key in inputs and isinstance(inputs[key], (int, float)):
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inputs[key] = random.randint(1, 0xffffffff)
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m_seeds += 1
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# Mutación de índice
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if class_type == "SequentialLoopStart":
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inputs["loop_idx"] = next_loop
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inputs["reset_loop"] = False
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# 🔪 Recolectamos Master Switch para cirugía diferida
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if class_type == "MasterSwitch":
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master_switches.append(inputs)
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is_next_final = (global_accumulated_frames + estimated_chunk) >= global_source_frame_count
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for inputs in master_switches:
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print(f" -> 🔀 [Cirugía de Grafo Segura] Mutando Master Switch para Ciclo {next_loop}...")
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# Sobrescribimos el cable que viene del Stitcher por un booleano estático
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inputs["is_final_cycle"] = is_next_final
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# 🛑 Eliminada la destrucción de cables (del inputs["on_true"/"on_false"])
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# Confiamos en la Evaluación Perezosa Nativa del MasterSwitch.
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print(f" -> 🎲 Semillas mutadas: {m_seeds}")
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p = {"prompt": prompt}
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if extra_pnginfo:
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p["extra_data"] = {"extra_pnginfo": extra_pnginfo}
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data = json.dumps(p).encode('utf-8')
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req = urllib.request.Request(f"http://127.0.0.1:{global_server_port}/prompt", data=data, headers={'Content-Type': 'application/json'})
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try:
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urllib.request.urlopen(req, timeout=5)
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print(f" -> ✅ Ciclo {next_loop} inyectado en la cola.")
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except Exception as e:
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print(f" -> ❌ Error HTTP: {e}")
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# Limpieza ligera por ciclo para evitar fragmentación
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import gc
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import torch
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import comfy.model_management as mm
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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else:
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print(f" -> 🏁 ¡Generación Finalizada! Todos los frames ensamblados.")
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# --- LIMPIEZA EXTREMA DE VRAM AUTOMÁTICA (Multi-Plataforma) ---
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print(f" -> 🧹 Iniciando vaciado automático de VRAM...")
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import gc
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import torch
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import comfy.model_management as mm
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try:
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# 1. Obligamos al motor interno de ComfyUI a soltar los modelos
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mm.unload_all_models()
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mm.soft_empty_cache()
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except Exception as e:
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print(f" -> ⚠️ Aviso: No se pudo usar model_management: {e}")
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# 2. Forzamos al recolector de basura de Python
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gc.collect()
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# 3. Le arrancamos a PyTorch la memoria reservada (CUDA, ROCm y Mac MPS)
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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# HACK: Forzar la desfragmentación de la memoria caché de PyTorch
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import ctypes
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try:
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ctypes.CDLL('libc.so.6').malloc_trim(0)
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except Exception:
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pass
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elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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torch.mps.empty_cache()
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print(f" -> ✨ VRAM liberada con éxito. Gráfica lista para nuevos flujos.")
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print(f"{'='*50}\n")
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return ()
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