mirror of
https://github.com/hpcaitech/Open-Sora.git
synced 2026-04-11 21:42:26 +02:00
180 lines
7.2 KiB
Python
180 lines
7.2 KiB
Python
import argparse
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import json
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import os
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from glob import glob
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from mmengine.config import Config
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from torch.utils.tensorboard import SummaryWriter
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def load_prompts(prompt_path):
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with open(prompt_path, "r") as f:
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prompts = [line.strip() for line in f.readlines()]
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return prompts
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def parse_args(training=False):
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parser = argparse.ArgumentParser()
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# model config
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parser.add_argument("config", help="model config file path")
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# ======================================================
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# General
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# ======================================================
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parser.add_argument("--seed", default=42, type=int, help="generation seed")
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parser.add_argument("--ckpt-path", type=str, help="path to model ckpt; will overwrite cfg.ckpt_path if specified")
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parser.add_argument("--batch-size", default=None, type=int, help="batch size")
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parser.add_argument("--outputs", default=None, type=str, help="the dir to save model weights")
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# ======================================================
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# Inference
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# ======================================================
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if not training:
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# output
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parser.add_argument("--save-dir", default=None, type=str, help="path to save generated samples")
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parser.add_argument("--sample-name", default=None, type=str, help="sample name, default is sample_idx")
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parser.add_argument("--start-index", default=None, type=int, help="start index for sample name")
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parser.add_argument("--end-index", default=None, type=int, help="end index for sample name")
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parser.add_argument("--num-sample", default=None, type=int, help="number of samples to generate for one prompt")
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parser.add_argument("--prompt-as-path", action="store_true", help="use prompt as path to save samples")
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parser.add_argument("--data-path", default=None, type=str, help="path to data csv")
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# prompt
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parser.add_argument("--prompt-path", default=None, type=str, help="path to prompt txt file")
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parser.add_argument("--prompt", default=None, type=str, nargs="+", help="prompt list")
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# image/video
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parser.add_argument("--num-frames", default=None, type=int, help="number of frames")
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parser.add_argument("--fps", default=None, type=int, help="fps")
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parser.add_argument("--image-size", default=None, type=int, nargs=2, help="image size")
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# hyperparameters
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parser.add_argument("--num-sampling-steps", default=None, type=int, help="sampling steps")
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parser.add_argument("--cfg-scale", default=None, type=float, help="balance between cond & uncond")
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# reference
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parser.add_argument("--loop", default=None, type=int, help="loop")
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parser.add_argument("--condition-frame-length", default=None, type=int, help="condition frame length")
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parser.add_argument("--reference-path", default=None, type=str, nargs="+", help="reference path")
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parser.add_argument("--mask-strategy", default=None, type=str, nargs="+", help="mask strategy")
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# ======================================================
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# Training
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# ======================================================
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else:
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parser.add_argument("--wandb", default=None, type=bool, help="enable wandb")
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parser.add_argument("--load", default=None, type=str, help="path to continue training")
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parser.add_argument("--data-path", default=None, type=str, help="path to data csv")
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parser.add_argument("--start-from-scratch", action="store_true", help="start training from scratch")
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return parser.parse_args()
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def merge_args(cfg, args, training=False):
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if args.ckpt_path is not None:
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cfg.model["from_pretrained"] = args.ckpt_path
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if cfg.get("discriminator") is not None:
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cfg.discriminator["from_pretrained"] = args.ckpt_path
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args.ckpt_path = None
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if args.data_path is not None:
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cfg.dataset["data_path"] = args.data_path
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args.data_path = None
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if not training and args.cfg_scale is not None:
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cfg.scheduler["cfg_scale"] = args.cfg_scale
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args.cfg_scale = None
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if not training and args.num_sampling_steps is not None:
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cfg.scheduler["num_sampling_steps"] = args.num_sampling_steps
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args.num_sampling_steps = None
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for k, v in vars(args).items():
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if v is not None:
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cfg[k] = v
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if not training:
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# Inference only
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# - Allow not set
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if "reference_path" not in cfg:
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cfg["reference_path"] = None
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if "loop" not in cfg:
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cfg["loop"] = 1
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if "frame_interval" not in cfg:
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cfg["frame_interval"] = 1
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if "sample_name" not in cfg:
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cfg["sample_name"] = None
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if "num_sample" not in cfg:
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cfg["num_sample"] = 1
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if "prompt_as_path" not in cfg:
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cfg["prompt_as_path"] = False
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# - Prompt handling
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if "prompt" not in cfg or cfg["prompt"] is None:
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if ("prompt" not in cfg or cfg["prompt"] is None) and cfg.get("prompt_path", None) is not None:
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cfg["prompt"] = load_prompts(cfg["prompt_path"])
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if args.start_index is not None and args.end_index is not None:
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cfg["prompt"] = cfg["prompt"][args.start_index : args.end_index]
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elif args.start_index is not None:
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cfg["prompt"] = cfg["prompt"][args.start_index :]
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elif args.end_index is not None:
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cfg["prompt"] = cfg["prompt"][: args.end_index]
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else:
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# Training only
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# - Allow not set
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if "mask_ratios" not in cfg:
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cfg["mask_ratios"] = None
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if "start_from_scratch" not in cfg:
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cfg["start_from_scratch"] = False
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if "bucket_config" not in cfg:
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cfg["bucket_config"] = None
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if "transform_name" not in cfg.dataset:
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cfg.dataset["transform_name"] = "center"
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if "num_bucket_build_workers" not in cfg:
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cfg["num_bucket_build_workers"] = 1
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# Both training and inference
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if "multi_resolution" not in cfg:
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cfg["multi_resolution"] = False
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return cfg
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def parse_configs(training=False):
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args = parse_args(training)
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cfg = Config.fromfile(args.config)
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cfg = merge_args(cfg, args, training)
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return cfg
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def create_experiment_workspace(cfg, get_last_workspace=False):
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"""
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This function creates a folder for experiment tracking.
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Args:
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args: The parsed arguments.
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Returns:
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exp_dir: The path to the experiment folder.
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"""
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# Make outputs folder (holds all experiment subfolders)
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os.makedirs(cfg.outputs, exist_ok=True)
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experiment_index = len(glob(f"{cfg.outputs}/*"))
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if get_last_workspace:
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experiment_index -= 1
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# Create an experiment folder
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model_name = cfg.model["type"].replace("/", "-")
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exp_name = f"{experiment_index:03d}-{model_name}"
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exp_dir = f"{cfg.outputs}/{exp_name}"
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os.makedirs(exp_dir, exist_ok=True)
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return exp_name, exp_dir
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def save_training_config(cfg, experiment_dir):
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with open(f"{experiment_dir}/config.txt", "w") as f:
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json.dump(cfg, f, indent=4)
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def create_tensorboard_writer(exp_dir):
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tensorboard_dir = f"{exp_dir}/tensorboard"
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os.makedirs(tensorboard_dir, exist_ok=True)
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writer = SummaryWriter(tensorboard_dir)
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return writer
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