diff --git a/vace/vace_wan_inference.py b/vace/vace_wan_inference.py index 11488bd..9ee9bef 100644 --- a/vace/vace_wan_inference.py +++ b/vace/vace_wan_inference.py @@ -2,6 +2,8 @@ # Copyright (c) Alibaba, Inc. and its affiliates. import argparse +import gc +import json import time from datetime import datetime import logging @@ -178,9 +180,48 @@ def get_parser(): type=float, default=5.0, help="Classifier free guidance scale.") + parser.add_argument( + "--batch_manifest", + type=str, + default=None, + help=( + "JSON or JSONL file with many inference jobs. The WanVace model is " + "loaded once, then each job is processed sequentially. Each job can " + "override src_video, src_mask, src_ref_images, save_dir, save_file, " + "prompt, base_seed, frame_num, sample_steps, sample_shift, " + "sample_guide_scale, sample_solver, size, and use_prompt_extend." + )) + parser.add_argument( + "--skip_existing", + type=str2bool, + default=False, + help="In batch mode, skip jobs whose output video already exists.") + parser.add_argument( + "--batch_continue_on_error", + type=str2bool, + default=True, + help="In batch mode, continue after a failed job instead of aborting.") return parser +BATCH_JOB_FIELDS = { + "size", + "frame_num", + "save_dir", + "save_file", + "src_video", + "src_mask", + "src_ref_images", + "prompt", + "use_prompt_extend", + "base_seed", + "sample_solver", + "sample_steps", + "sample_shift", + "sample_guide_scale", +} + + def _init_logging(rank): # logging if rank == 0: @@ -193,6 +234,175 @@ def _init_logging(rank): logging.basicConfig(level=logging.ERROR) +def load_batch_jobs(path): + with open(path, "r", encoding="utf-8") as f: + text = f.read().strip() + if not text: + return [] + + if path.endswith(".jsonl"): + jobs = [] + for line_no, line in enumerate(text.splitlines(), start=1): + line = line.strip() + if not line: + continue + job = json.loads(line) + if not isinstance(job, dict): + raise ValueError(f"{path}:{line_no} is not a JSON object") + jobs.append(job) + return jobs + + data = json.loads(text) + if isinstance(data, dict) and "jobs" in data: + data = data["jobs"] + if not isinstance(data, list): + raise ValueError("--batch_manifest JSON must be a list or an object with a 'jobs' list") + if not all(isinstance(job, dict) for job in data): + raise ValueError("--batch_manifest jobs must be JSON objects") + return data + + +def make_job_args(base_args, job): + values = vars(base_args).copy() + for static_key in ("model_name", "ckpt_dir"): + if static_key in job and str(job[static_key]) != str(values[static_key]): + raise ValueError( + f"Batch job cannot override {static_key}; the model is loaded once " + f"from the top-level {static_key}." + ) + for key, value in job.items(): + normalized = key.replace("-", "_") + if normalized in BATCH_JOB_FIELDS and value is not None: + values[normalized] = value + return argparse.Namespace(**values) + + +def maybe_extend_prompt(args, prompt_expander, rank): + if args.prompt is None: + args.prompt = "" + if args.use_prompt_extend and args.use_prompt_extend != 'plain': + logging.info("Extending prompt ...") + if rank == 0: + prompt = prompt_expander.forward(args.prompt) + logging.info(f"Prompt extended from '{args.prompt}' to '{prompt}'") + input_prompt = [prompt] + else: + input_prompt = [None] + if dist.is_initialized(): + dist.broadcast_object_list(input_prompt, src=0) + args.prompt = input_prompt[0] + logging.info(f"Extended prompt: {args.prompt}") + return args.prompt + + +def output_path_for_args(args, model_name): + if args.save_file is not None: + return args.save_file + if args.save_dir is None: + save_dir = os.path.join('results', model_name, time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))) + else: + save_dir = args.save_dir + return os.path.join(save_dir, 'out_video.mp4') + + +def save_outputs(video, src_video, src_mask, src_ref_images, cfg, args): + ret_data = {} + if args.save_dir is None: + save_dir = os.path.join('results', args.model_name, time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))) + else: + save_dir = args.save_dir + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + if args.save_file is not None: + save_file = args.save_file + else: + save_file = os.path.join(save_dir, 'out_video.mp4') + cache_video( + tensor=video[None], + save_file=save_file, + fps=cfg.sample_fps, + nrow=1, + normalize=True, + value_range=(-1, 1)) + logging.info(f"Saving generated video to {save_file}") + ret_data['out_video'] = save_file + + save_file = os.path.join(save_dir, 'src_video.mp4') + cache_video( + tensor=src_video[0][None], + save_file=save_file, + fps=cfg.sample_fps, + nrow=1, + normalize=True, + value_range=(-1, 1)) + logging.info(f"Saving src_video to {save_file}") + ret_data['src_video'] = save_file + + save_file = os.path.join(save_dir, 'src_mask.mp4') + cache_video( + tensor=src_mask[0][None], + save_file=save_file, + fps=cfg.sample_fps, + nrow=1, + normalize=True, + value_range=(0, 1)) + logging.info(f"Saving src_mask to {save_file}") + ret_data['src_mask'] = save_file + + if src_ref_images[0] is not None: + for i, ref_img in enumerate(src_ref_images[0]): + save_file = os.path.join(save_dir, f'src_ref_image_{i}.png') + cache_image( + tensor=ref_img[:, 0, ...], + save_file=save_file, + nrow=1, + normalize=True, + value_range=(-1, 1)) + logging.info(f"Saving src_ref_image_{i} to {save_file}") + ret_data[f'src_ref_image_{i}'] = save_file + return ret_data + + +def run_one_job(wan_vace, cfg, args, device, rank, prompt_expander=None): + maybe_extend_prompt(args, prompt_expander, rank) + logging.info(f"Input prompt: {args.prompt}") + + src_video, src_mask, src_ref_images = wan_vace.prepare_source( + [args.src_video], + [args.src_mask], + [None if args.src_ref_images is None else args.src_ref_images.split(',')], + args.frame_num, + SIZE_CONFIGS[args.size], + device, + ) + + logging.info(f"Generating video...") + video = wan_vace.generate( + args.prompt, + src_video, + src_mask, + src_ref_images, + size=SIZE_CONFIGS[args.size], + frame_num=args.frame_num, + shift=args.sample_shift, + sample_solver=args.sample_solver, + sampling_steps=args.sample_steps, + guide_scale=args.sample_guide_scale, + seed=args.base_seed, + offload_model=args.offload_model) + + ret_data = {} + if rank == 0: + ret_data = save_outputs(video, src_video, src_mask, src_ref_images, cfg, args) + + del video, src_video, src_mask, src_ref_images + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + return ret_data + + def main(args): args = argparse.Namespace(**args) if isinstance(args, dict) else args args = validate_args(args) @@ -235,6 +445,7 @@ def main(args): ulysses_degree=args.ulysses_size, ) + prompt_expander = None if args.use_prompt_extend and args.use_prompt_extend != 'plain': prompt_expander = get_annotator(config_type='prompt', config_task=args.use_prompt_extend, return_dict=False) @@ -250,26 +461,12 @@ def main(args): dist.broadcast_object_list(base_seed, src=0) args.base_seed = base_seed[0] - if args.prompt is None: + if args.batch_manifest is None and args.prompt is None: args.prompt = EXAMPLE_PROMPT[args.model_name]["prompt"] args.src_video = EXAMPLE_PROMPT[args.model_name].get("src_video", None) args.src_mask = EXAMPLE_PROMPT[args.model_name].get("src_mask", None) args.src_ref_images = EXAMPLE_PROMPT[args.model_name].get("src_ref_images", None) - logging.info(f"Input prompt: {args.prompt}") - if args.use_prompt_extend and args.use_prompt_extend != 'plain': - logging.info("Extending prompt ...") - if rank == 0: - prompt = prompt_expander.forward(args.prompt) - logging.info(f"Prompt extended from '{args.prompt}' to '{prompt}'") - input_prompt = [prompt] - else: - input_prompt = [None] - if dist.is_initialized(): - dist.broadcast_object_list(input_prompt, src=0) - args.prompt = input_prompt[0] - logging.info(f"Extended prompt: {args.prompt}") - logging.info("Creating WanT2V pipeline.") wan_vace = WanVace( config=cfg, @@ -282,82 +479,41 @@ def main(args): t5_cpu=args.t5_cpu, ) - src_video, src_mask, src_ref_images = wan_vace.prepare_source([args.src_video], - [args.src_mask], - [None if args.src_ref_images is None else args.src_ref_images.split(',')], - args.frame_num, SIZE_CONFIGS[args.size], device) - - logging.info(f"Generating video...") - video = wan_vace.generate( - args.prompt, - src_video, - src_mask, - src_ref_images, - size=SIZE_CONFIGS[args.size], - frame_num=args.frame_num, - shift=args.sample_shift, - sample_solver=args.sample_solver, - sampling_steps=args.sample_steps, - guide_scale=args.sample_guide_scale, - seed=args.base_seed, - offload_model=args.offload_model) - - ret_data = {} - if rank == 0: - if args.save_dir is None: - save_dir = os.path.join('results', args.model_name, time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time()))) - else: - save_dir = args.save_dir - if not os.path.exists(save_dir): - os.makedirs(save_dir) + if args.batch_manifest is not None: + jobs = load_batch_jobs(args.batch_manifest) + logging.info(f"Loaded {len(jobs)} batch jobs from {args.batch_manifest}") + results = [] + for idx, job in enumerate(jobs): + job_args = validate_args(make_job_args(args, job)) + job_name = job.get("name") or job.get("id") or idx + out_video = output_path_for_args(job_args, job_args.model_name) + if args.skip_existing and rank == 0 and os.path.exists(out_video): + logging.info(f"[batch {idx + 1}/{len(jobs)}] skip existing {job_name}: {out_video}") + results.append({"index": idx, "name": job_name, "status": "skipped", "out_video": out_video}) + continue + + logging.info(f"[batch {idx + 1}/{len(jobs)}] start {job_name}") + try: + ret = run_one_job(wan_vace, cfg, job_args, device, rank, prompt_expander) + if rank == 0: + results.append({"index": idx, "name": job_name, "status": "ok", **ret}) + logging.info(f"[batch {idx + 1}/{len(jobs)}] ok {job_name}") + except Exception as exc: + logging.exception(f"[batch {idx + 1}/{len(jobs)}] failed {job_name}: {exc}") + if rank == 0: + results.append({"index": idx, "name": job_name, "status": "failed", "error": str(exc)}) + if not args.batch_continue_on_error: + raise - if args.save_file is not None: - save_file = args.save_file - else: - save_file = os.path.join(save_dir, 'out_video.mp4') - cache_video( - tensor=video[None], - save_file=save_file, - fps=cfg.sample_fps, - nrow=1, - normalize=True, - value_range=(-1, 1)) - logging.info(f"Saving generated video to {save_file}") - ret_data['out_video'] = save_file - - save_file = os.path.join(save_dir, 'src_video.mp4') - cache_video( - tensor=src_video[0][None], - save_file=save_file, - fps=cfg.sample_fps, - nrow=1, - normalize=True, - value_range=(-1, 1)) - logging.info(f"Saving src_video to {save_file}") - ret_data['src_video'] = save_file - - save_file = os.path.join(save_dir, 'src_mask.mp4') - cache_video( - tensor=src_mask[0][None], - save_file=save_file, - fps=cfg.sample_fps, - nrow=1, - normalize=True, - value_range=(0, 1)) - logging.info(f"Saving src_mask to {save_file}") - ret_data['src_mask'] = save_file - - if src_ref_images[0] is not None: - for i, ref_img in enumerate(src_ref_images[0]): - save_file = os.path.join(save_dir, f'src_ref_image_{i}.png') - cache_image( - tensor=ref_img[:, 0, ...], - save_file=save_file, - nrow=1, - normalize=True, - value_range=(-1, 1)) - logging.info(f"Saving src_ref_image_{i} to {save_file}") - ret_data[f'src_ref_image_{i}'] = save_file + if rank == 0: + result_path = args.batch_manifest + ".results.jsonl" + with open(result_path, "w", encoding="utf-8") as f: + for row in results: + f.write(json.dumps(row, ensure_ascii=False) + "\n") + logging.info(f"Wrote batch results to {result_path}") + ret_data = {"batch_results": results if rank == 0 else []} + else: + ret_data = run_one_job(wan_vace, cfg, args, device, rank, prompt_expander) logging.info("Finished.") return ret_data