#!/usr/bin/env python3 """ Standalone SeedVR2 Video Upscaler CLI Script """ import sys import os import argparse import time import multiprocessing as mp # Ensure safe CUDA usage with multiprocessing if mp.get_start_method(allow_none=True) != 'spawn': mp.set_start_method('spawn', force=True) # ------------------------------------------------------------- # 1) Gestion VRAM (cudaMallocAsync) déjà en place os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync") # 2) Pré-parse de la ligne de commande pour récupérer --cuda_device _pre_parser = argparse.ArgumentParser(add_help=False) _pre_parser.add_argument("--cuda_device", type=str, default=None) _pre_args, _ = _pre_parser.parse_known_args() if _pre_args.cuda_device is not None: device_list_env = [x.strip() for x in _pre_args.cuda_device.split(',') if x.strip()!=''] if len(device_list_env) == 1: # Single GPU: restrict visibility now os.environ["CUDA_VISIBLE_DEVICES"] = device_list_env[0] # ------------------------------------------------------------- # 3) Imports lourds (torch, etc.) après la configuration env import torch import cv2 import numpy as np from datetime import datetime from pathlib import Path from src.utils.downloads import download_weight # Add project root to sys.path for src module imports script_dir = os.path.dirname(os.path.abspath(__file__)) if script_dir not in sys.path: sys.path.insert(0, script_dir) root_dir = os.path.join(script_dir, '..', '..') if root_dir not in sys.path: sys.path.insert(0, root_dir) def extract_frames_from_video(video_path, debug=False, skip_first_frames=0, load_cap=None): """ Extract frames from video and convert to tensor format Args: video_path (str): Path to input video debug (bool): Enable debug logging skip_first_frame (bool): Skip the first frame during extraction load_cap (int): Maximum number of frames to load (None for all) Returns: torch.Tensor: Frames tensor in format [T, H, W, C] (Float16, normalized 0-1) """ if debug: print(f"🎬 Extracting frames from video: {video_path}") if not os.path.exists(video_path): raise FileNotFoundError(f"Video file not found: {video_path}") # Open video cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise ValueError(f"Cannot open video file: {video_path}") # Get video properties fps = cap.get(cv2.CAP_PROP_FPS) frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) if debug: print(f"📊 Video info: {frame_count} frames, {width}x{height}, {fps:.2f} FPS") if skip_first_frames: print(f"⏭️ Will skip first {skip_first_frames} frames") if load_cap: print(f"🔢 Will load maximum {load_cap} frames") frames = [] frame_idx = 0 frames_loaded = 0 while True: ret, frame = cap.read() if not ret: break # Skip first frame if requested if frame_idx < skip_first_frames: frame_idx += 1 if debug: print(f"⏭️ Skipped first frame") continue # Check load cap if load_cap is not None and load_cap > 0 and frames_loaded >= load_cap: if debug: print(f"🔢 Reached load cap of {load_cap} frames") break # Convert BGR to RGB frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Convert to float32 and normalize to 0-1 frame = frame.astype(np.float32) / 255.0 frames.append(frame) frame_idx += 1 frames_loaded += 1 if debug and frames_loaded % 100 == 0: total_to_load = min(frame_count, load_cap) if load_cap else frame_count print(f"📹 Extracted {frames_loaded}/{total_to_load} frames") cap.release() if len(frames) == 0: raise ValueError(f"No frames extracted from video: {video_path}") if debug: print(f"✅ Extracted {len(frames)} frames") # Convert to tensor [T, H, W, C] and cast to Float16 for ComfyUI compatibility frames_tensor = torch.from_numpy(np.stack(frames)).to(torch.float16) if debug: print(f"📊 Frames tensor shape: {frames_tensor.shape}, dtype: {frames_tensor.dtype}") return frames_tensor, fps def save_frames_to_video(frames_tensor, output_path, fps=30.0, debug=False): """ Save frames tensor to video file Args: frames_tensor (torch.Tensor): Frames in format [T, H, W, C] (Float16, 0-1) output_path (str): Output video path fps (float): Output video FPS debug (bool): Enable debug logging """ if debug: print(f"🎬 Saving {frames_tensor.shape[0]} frames to video: {output_path}") # Ensure output directory exists os.makedirs(os.path.dirname(output_path), exist_ok=True) # Convert tensor to numpy and denormalize frames_np = frames_tensor.cpu().numpy() frames_np = (frames_np * 255.0).astype(np.uint8) # Get video properties T, H, W, C = frames_np.shape # Initialize video writer fourcc = cv2.VideoWriter_fourcc(*'mp4v') out = cv2.VideoWriter(output_path, fourcc, fps, (W, H)) if not out.isOpened(): raise ValueError(f"Cannot create video writer for: {output_path}") # Write frames for i, frame in enumerate(frames_np): # Convert RGB to BGR for OpenCV frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) out.write(frame_bgr) if debug and (i + 1) % 100 == 0: print(f"💾 Saved {i + 1}/{T} frames") out.release() if debug: print(f"✅ Video saved successfully: {output_path}") def save_frames_to_png(frames_tensor, output_dir, base_name, debug=False): """ Save frames tensor as sequential PNG images. Args: frames_tensor (torch.Tensor): Frames in format [T, H, W, C] (Float16, 0-1) output_dir (str): Directory to save PNGs base_name (str): Base name for output files (without extension) debug (bool): Enable debug logging """ if debug: print(f"🖼️ Saving {frames_tensor.shape[0]} frames as PNGs to directory: {output_dir}") # Ensure output directory exists os.makedirs(output_dir, exist_ok=True) # Convert to numpy uint8 RGB frames_np = (frames_tensor.cpu().numpy() * 255.0).astype(np.uint8) total = frames_np.shape[0] digits = max(5, len(str(total))) # at least 5 digits for idx, frame in enumerate(frames_np): filename = f"{base_name}_{idx:0{digits}d}.png" file_path = os.path.join(output_dir, filename) # Convert RGB to BGR for cv2 frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) cv2.imwrite(file_path, frame_bgr) if debug and (idx + 1) % 100 == 0: print(f"💾 Saved {idx + 1}/{total} PNGs") if debug: print(f"✅ PNG saving completed: {total} files in '{output_dir}'") def _worker_process(proc_idx, device_id, frames_np, shared_args, return_queue, progress_queue=None): # Adicionado progress_queue """Worker que executa o upscaling em uma GPU dedicada.""" os.environ["CUDA_VISIBLE_DEVICES"] = str(device_id) os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync") import torch from src.core.model_manager import configure_runner from src.core.generation import generation_loop frames_tensor = torch.from_numpy(frames_np).to(torch.float16) # Cria uma função de callback local que envia o progresso para a fila local_progress_callback = None if progress_queue: def callback_wrapper(batch_idx, total_batches, current_frames, message): progress_queue.put((batch_idx, total_batches, message)) local_progress_callback = callback_wrapper runner = configure_runner(shared_args["model"], shared_args["model_dir"], shared_args["preserve_vram"], shared_args["debug"]) result_tensor = generation_loop( runner=runner, images=frames_tensor, cfg_scale=shared_args["cfg_scale"], seed=shared_args["seed"], res_w=shared_args["res_w"], batch_size=shared_args["batch_size"], preserve_vram=shared_args["preserve_vram"], temporal_overlap=shared_args["temporal_overlap"], debug=shared_args["debug"], progress_callback=local_progress_callback # Passa o callback para o generation_loop ) return_queue.put((proc_idx, result_tensor.cpu().numpy())) def _worker_process1(proc_idx, device_id, frames_np, shared_args, return_queue): """Worker process that performs upscaling on a slice of frames using a dedicated GPU.""" # 1. Limit CUDA visibility to the chosen GPU BEFORE importing torch-heavy deps os.environ["CUDA_VISIBLE_DEVICES"] = str(device_id) # Keep same cudaMallocAsync setting os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "backend:cudaMallocAsync") import torch # local import inside subprocess from src.core.model_manager import configure_runner from src.core.generation import generation_loop # Reconstruct frames tensor frames_tensor = torch.from_numpy(frames_np).to(torch.float16) # Prepare runner model_dir = shared_args["model_dir"] model_name = shared_args["model"] # ensure model weights present (each process checks but very fast if already downloaded) if shared_args["debug"]: print(f"🔄 Configuring runner for device {device_id}") runner = configure_runner(model_name, model_dir, shared_args["preserve_vram"], shared_args["debug"]) # Run generation result_tensor = generation_loop( runner=runner, images=frames_tensor, cfg_scale=shared_args["cfg_scale"], seed=shared_args["seed"], res_w=shared_args["res_w"], batch_size=shared_args["batch_size"], preserve_vram=shared_args["preserve_vram"], temporal_overlap=shared_args["temporal_overlap"], debug=shared_args["debug"], ) # Send back result as numpy array to avoid CUDA transfers return_queue.put((proc_idx, result_tensor.cpu().numpy())) def _gpu_processing(frames_tensor, device_list, args, progress_callback=None): # Adicionado progress_callback """Divide os frames e os processa em paralelo em múltiplas GPUs.""" num_devices = len(device_list) chunks = torch.chunk(frames_tensor, num_devices, dim=0) manager = mp.Manager() return_queue = manager.Queue() progress_queue = manager.Queue() if progress_callback else None # Cria a fila de progresso workers = [] shared_args = { "model": args.model, "model_dir": args.model_dir or "./models/SEEDVR2", "preserve_vram": args.preserve_vram, "debug": args.debug, "cfg_scale": 1.0, "seed": args.seed, "res_w": args.resolution, "batch_size": args.batch_size, "temporal_overlap": 0, } for idx, (device_id, chunk_tensor) in enumerate(zip(device_list, chunks)): p = mp.Process(target=_worker_process, args=(idx, device_id, chunk_tensor.cpu().numpy(), shared_args, return_queue, progress_queue)) p.start() workers.append(p) results_np = [None] * num_devices collected = 0 total_batches_per_worker = -1 # Para calcular o progresso total while collected < num_devices: # Verifica as duas filas (resultado e progresso) de forma não-bloqueante if progress_queue and not progress_queue.empty(): batch_idx, total_batches, message = progress_queue.get() if total_batches_per_worker == -1: total_batches_per_worker = total_batches total_progress = (collected + (batch_idx / total_batches_per_worker)) / num_devices progress_callback(total_progress, desc=f"GPU {collected+1}/{num_devices}: {message}") if not return_queue.empty(): proc_idx, res_np = return_queue.get() results_np[proc_idx] = res_np collected += 1 time.sleep(0.1) # Evita busy-waiting for p in workers: p.join() return torch.from_numpy(np.concatenate(results_np, axis=0)).to(torch.float16) def _gpu_processing1(frames_tensor, device_list, args): """Split frames and process them in parallel on multiple GPUs.""" num_devices = len(device_list) # split frames tensor along time dimension chunks = torch.chunk(frames_tensor, num_devices, dim=0) manager = mp.Manager() return_queue = manager.Queue() workers = [] shared_args = { "model": args.model, "model_dir": args.model_dir if args.model_dir is not None else "./models/SEEDVR2", "preserve_vram": args.preserve_vram, "debug": args.debug, "cfg_scale": 1.0, "seed": args.seed, "res_w": args.resolution, "batch_size": args.batch_size, "temporal_overlap": 0, } for idx, (device_id, chunk_tensor) in enumerate(zip(device_list, chunks)): p = mp.Process( target=_worker_process, args=(idx, device_id, chunk_tensor.cpu().numpy(), shared_args, return_queue), ) p.start() workers.append(p) results_np = [None] * num_devices collected = 0 while collected < num_devices: proc_idx, res_np = return_queue.get() results_np[proc_idx] = res_np collected += 1 for p in workers: p.join() # Concatenate results in original order result_tensor = torch.from_numpy(np.concatenate(results_np, axis=0)).to(torch.float16) return result_tensor def parse_arguments(): """Parse command line arguments""" parser = argparse.ArgumentParser(description="SeedVR2 Video Upscaler CLI") parser.add_argument("--video_path", type=str, required=True, help="Path to input video file") parser.add_argument("--seed", type=int, default=100, help="Random seed for generation (default: 100)") parser.add_argument("--resolution", type=int, default=1072, help="Target resolution of the short side (default: 1072)") parser.add_argument("--batch_size", type=int, default=1, help="Number of frames per batch (default: 5)") parser.add_argument("--model", type=str, default="seedvr2_ema_3b_fp8_e4m3fn.safetensors", choices=[ "seedvr2_ema_3b_fp16.safetensors", "seedvr2_ema_3b_fp8_e4m3fn.safetensors", "seedvr2_ema_7b_fp16.safetensors", "seedvr2_ema_7b_fp8_e4m3fn.safetensors" ], help="Model to use (default: 3B FP8)") parser.add_argument("--model_dir", type=str, default="seedvr2_models", help="Directory containing the model files (default: use cache directory)") parser.add_argument("--skip_first_frames", type=int, default=0, help="Skip the first frames during processing") parser.add_argument("--load_cap", type=int, default=0, help="Maximum number of frames to load from video (default: load all)") parser.add_argument("--output", type=str, default=None, help="Output path (default: auto-generated, if output_format is png, it will be a directory)") parser.add_argument("--output_format", type=str, default="video", choices=["video", "png"], help="Output format: 'video' (mp4) or 'png' images (default: video)") parser.add_argument("--preserve_vram", action="store_true", help="Enable VRAM preservation mode") parser.add_argument("--debug", action="store_true", help="Enable debug logging") parser.add_argument("--cuda_device", type=str, default=None, help="CUDA device id(s). Single id (e.g., '0') or comma-separated list '0,1' for multi-GPU") return parser.parse_args() def main(): """Main CLI function""" print(f"🚀 SeedVR2 Video Upscaler CLI started at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") # Parse arguments args = parse_arguments() if args.debug: print(f"📋 Arguments:") for key, value in vars(args).items(): print(f" {key}: {value}") if args.debug: # Show actual CUDA device visibility print(f"🖥️ CUDA_VISIBLE_DEVICES: {os.environ.get('CUDA_VISIBLE_DEVICES', 'Not set (all)')}") if torch.cuda.is_available(): print(f"🖥️ torch.cuda.device_count(): {torch.cuda.device_count()}") print(f"🖥️ Using device index 0 inside script (mapped to selected GPU)") try: # Ensure --output is a directory when using PNG format if args.output_format == "png": output_path_obj = Path(args.output) if output_path_obj.suffix: # an extension is present, strip it args.output = str(output_path_obj.with_suffix('')) if args.debug: print(f"📁 Output will be saved to: {args.output}") # Extract frames from video print(f"🎬 Extracting frames from video...") start_time = time.time() frames_tensor, original_fps = extract_frames_from_video( args.video_path, args.debug, args.skip_first_frames, args.load_cap ) if args.debug: print(f"🔄 Frame extraction time: {time.time() - start_time:.2f}s") # print(f"📊 Initial VRAM: {torch.cuda.memory_allocated() / 1024**3:.2f}GB") # may initialize cuda # Parse GPU list device_list = [d.strip() for d in str(args.cuda_device).split(',') if d.strip()] if args.cuda_device else ["0"] if args.debug: print(f"🚀 Using devices: {device_list}") processing_start = time.time() download_weight(args.model, args.model_dir) result = _gpu_processing(frames_tensor, device_list, args) generation_time = time.time() - processing_start if args.debug: print(f"🔄 Generation time: {generation_time:.2f}s") print(f"📊 Peak VRAM usage: {torch.cuda.max_memory_allocated() / 1024**3:.2f}GB") print(f"📊 Result shape: {result.shape}, dtype: {result.dtype}") # After generation_time calculation, choose saving method if args.output_format == "png": # Ensure output treated as directory output_dir = args.output base_name = Path(args.video_path).stem + "_upscaled" if args.debug: print(f"🖼️ Saving PNG frames to directory: {output_dir}") save_start = time.time() save_frames_to_png(result, output_dir, base_name, args.debug) if args.debug: print(f"🔄 Save time: {time.time() - save_start:.2f}s") else: # Save video if args.debug: print(f"💾 Saving upscaled video to: {args.output}") save_start = time.time() save_frames_to_video(result, args.output, original_fps, args.debug) if args.debug: print(f"🔄 Save time: {time.time() - save_start:.2f}s") total_time = time.time() - start_time print(f"✅ Upscaling completed successfully!") if args.output_format == "png": print(f"📁 PNG frames saved in directory: {args.output}") else: print(f"📁 Output saved to video: {args.output}") print(f"🕒 Total processing time: {total_time:.2f}s") print(f"⚡ Average FPS: {len(frames_tensor) / generation_time:.2f} frames/sec") except Exception as e: print(f"❌ Error during processing: {e}") import traceback traceback.print_exc() sys.exit(1) finally: print(f"🧹 Process {os.getpid()} terminating - VRAM will be automatically freed") def run_inference_logic(args, progress_callback=None): """ Função principal que executa o pipeline de upscaling. Pode ser chamada tanto pelo CLI quanto por outra parte do código. 'args' pode ser um objeto argparse ou qualquer objeto com atributos correspondentes. """ if args.debug: print(f"📋 Argumentos da Lógica de Inferência:") for key, value in vars(args).items(): print(f" {key}: {value}") # 1. Extrair Frames print("🎬 Extraindo frames do vídeo...") start_time = time.time() frames_tensor, original_fps = extract_frames_from_video( args.video_path, args.debug, args.skip_first_frames, args.load_cap ) if args.debug: print(f"🔄 Tempo de extração de frames: {time.time() - start_time:.2f}s") # 2. Preparar e Executar a Inferência (Multi-GPU) # ATENÇÃO: A lógica Multi-GPU com `multiprocessing` é complexa de passar um callback de progresso. # Para simplificar e garantir o funcionamento, vamos focar em single-process/multi-GPU. # A função `_gpu_processing` já chama `generation_loop`, que pode aceitar um callback. # Precisamos garantir que ele seja passado adiante. device_list = [d.strip() for d in str(args.cuda_device).split(',') if d.strip()] if args.cuda_device else ["0"] if args.debug: print(f"🚀 Usando dispositivos: {device_list}") processing_start = time.time() download_weight(args.model, args.model_dir) # MODIFICAÇÃO: A função _gpu_processing deve ser ajustada para aceitar e passar o callback # No entanto, como _gpu_processing usa multiprocessing, passar um callback de Gradio # é complexo. Uma abordagem mais simples é remover a camada de multiprocessing por enquanto # e chamar a lógica de inferência principal diretamente se estivermos em modo de API. # Por agora, vamos assumir que o `_gpu_processing` lida com isso internamente. # A maneira mais fácil de simular progresso aqui é pelo tempo. # Esta chamada precisa ser investigada para passar o callback adiante. # Por enquanto, o progresso virá antes e depois desta chamada. result_tensor = _gpu_processing(frames_tensor, device_list, args) # Esta chamada é bloqueante generation_time = time.time() - processing_start if args.debug: print(f"🔄 Tempo de Geração: {generation_time:.2f}s") print(f"📊 Resultado: {result_tensor.shape}, dtype: {result_tensor.dtype}") # 3. Retornar o resultado em memória return result_tensor, original_fps, generation_time, len(frames_tensor) # FUNÇÃO MAIN ORIGINAL (agora um wrapper) def main(): """Função principal do CLI""" print(f"🚀 SeedVR2 Video Upscaler CLI iniciado às {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") args = parse_arguments() try: # Chama a nova função de lógica result_tensor, original_fps, _, _ = run_inference_logic(args) # A parte de salvar o arquivo permanece apenas para o modo CLI print(f"💾 Salvando vídeo em: {args.output}") save_start = time.time() save_frames_to_video(result_tensor, args.output, original_fps, args.debug) if args.debug: print(f"🔄 Tempo de salvamento: {time.time() - save_start:.2f}s") print("✅ Upscaling CLI concluído com sucesso!") except Exception as e: print(f"❌ Erro durante o processamento: {e}") import traceback traceback.print_exc() sys.exit(1) # Ponto de entrada para execução via linha de comando if __name__ == "__main__": main()