from utils.lib import * from utils.dist import dist_init from config import BasicArgs from utils.wutils_ldm import import_filename def str_to_bool(value): if value.lower() in {'false', 'f', '0', 'no', 'n'}: return False elif value.lower() in {'true', 't', '1', 'yes', 'y'}: return True raise ValueError(f'{value} is not a valid boolean value') def parse_with_config(parsed_args): """This function will set args based on the input config file. (1) it only overwrites unset parameters, i.e., these parameters not set from user command line input (2) it also sets configs in the config file but declared in the parser """ # convert to EasyDict object, # enabling access from attributes even for nested config # e.g., args.train_datasets[0].name args = edict(vars(parsed_args)) if args.config is not None: config_args = json.load(open(args.config)) override_keys = {arg[2:].split("=")[0] for arg in sys.argv[1:] if arg.startswith("--")} for k, v in config_args.items(): if k not in override_keys: setattr(args, k, v) del args.config return args def parse_with_cf(parsed_args): """This function will set args based on the input config file. (1) it only overwrites unset parameters, i.e., these parameters not set from user command line input (2) it also sets configs in the config file but declared in the parser """ # convert to EasyDict object, # enabling access from attributes even for nested config # e.g., args.train_datasets[0].name args = edict(vars(parsed_args)) if os.path.exists(parsed_args.cf): cf = import_filename(parsed_args.cf) config_args = edict(vars(cf.Args)) override_keys = {arg[2:].split("=")[0] for arg in sys.argv[1:] if arg.startswith("--")} for k, v in config_args.items(): if k not in override_keys: setattr(args, k, v) else: raise NotImplementedError('Config filename %s does not exist.' % args.cf) return args def update_args(parsed_args, args): for key in vars(parsed_args): val = getattr(parsed_args, key) if key in ["epochs", "train_batch_size", "eval_batch_size", "train_yaml", "val_yaml", "learning_rate"]: if val is None: continue if key == "log_dir": if val is None: continue else: val = os.path.join(BasicArgs.root_dir, parsed_args.log_dir) if key == "root_dir": continue setattr(args, key, val) return args class Args(object): """Shared options for pre-training and downstream tasks. For each downstream task, implement a get_*_args function, see `get_pretraining_args()` Usage: >>> shared_configs = SharedConfigs() >>> pretraining_config = shared_configs.get_pretraining_args() """ def __init__(self, desc="shared config"): parser = argparse.ArgumentParser(description=desc) parser.add_argument('--root_dir', default=None, type=str) parser.add_argument('--cf', default=None, type=str, required=True) parser.add_argument('--pretrained_model', default=None, type=str) parser.add_argument('--pretrained_model_lora', default=None, type=str) parser.add_argument('--pretrained_model_controlnet', default=None, type=str) parser.add_argument('--debug', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--debug_seed', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--debug_dataloader', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--log_dir', default=None, type=str) parser.add_argument('--deepspeed', help="use deepspeed", type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--use_amp', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--seed', type=int, default=42, help="random seed for initialization.") parser.add_argument('--fix_dist_seed', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--tiktok_data_root', default=None, type=str) # parser.add_argument('--tiktok_ann_root', default=None, type=str) # input parser.add_argument("--img_size", default=256, type=int, help="image input size") parser.add_argument("--max_video_len", default=4, type=int, help="frame input length") parser.add_argument("--debug_max_video_len", default=4, type=int, help="debug frame input length") parser.add_argument("--conds", default=[], # nargs='+', type=str, choices=["poses", "masks", "densepose", "hed", "canny_100_200", "midas", "mlsd_0.1_0.1", "uniformer"], help="used in uni-controlnet/tsv datasets") # Model setting parser.add_argument('--gradient_checkpointing', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--find_unused_parameters', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--enable_xformers_memory_efficient_attention', type=str_to_bool, nargs='?', const=True, default=False) # SD related parser.add_argument( "--trainable_modules", type=str, nargs='+', default=None) parser.add_argument("--scale_factor", default=0.18215, type=float, help="scale factor in SD") parser.add_argument("--loss_target", default="noise", type=str, choices=["noise", "x0", "mixed"], help="unet_loss_target") parser.add_argument("--x0_steps", default=200, type=int, help="steps to calc x0 loss") parser.add_argument('--pretrained_model_path', default='diffusers/stable-diffusion-v1-4', type=str) # training configs parser.add_argument("--num_workers", default=4, type=int, help="number of workers") parser.add_argument('--node_split_sampler', help="use node_split_sampler", type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--gradient_accumulate_steps', default=1, type=int) parser.add_argument('--max_grad_norm', default=-1, type=float) parser.add_argument('--learning_rate', default=5e-6, type=float) parser.add_argument("--decay", default=1e-3, type=float, help="Weight deay.") parser.add_argument("--warmup_ratio", default=0.1, type=float, help="warm up ratio of lr") parser.add_argument("--max_train_samples", default=None, type=int, help="number train samples") parser.add_argument("--debug_max_train_samples", default=100, type=int, help="number train samples in debug mode") parser.add_argument("--drop_text", default=1.0, type=float, # drop text only activate in args.null_caption help="prob to drop text") # Note that tbs/ebs is the Global batch size = GPU_PER_NODE * NODE_COUNT * LOCAL_BATCH_SIZE parser.add_argument('--local_train_batch_size', default=1, type=int) parser.add_argument('--epochs', default=10, type=int) parser.add_argument('--eval_step', default=5, type=float) parser.add_argument('--save_step', default=5, type=float) parser.add_argument('--do_train', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--train_yaml', default=None, type=str) parser.add_argument('--resume', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--null_caption', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--refer_sdvae', type=str_to_bool, # use sd vae to process the reference image nargs='?', const=True, default=False) parser.add_argument('--controlnet_conditioning_scale_cond', default=1.0, type=float) parser.add_argument('--controlnet_conditioning_scale_ref', default=1.0, type=float) ### for temporal disco parser.add_argument("--nframes", type=int, default=8, help="the number of frames for synthesis") parser.add_argument("--frame_interval", type=int, default=1, help="frame interval for synthesis") parser.add_argument("--eval_sample_interval", type=float, default=1, help="eval interval for fast evalaution, only valid for video datasets") parser.add_argument("--train_sample_interval", type=float, default=1, help="train interval for fast training") ### reference image attention parser.add_argument("--unet_unfreeze_type", default=None, type=str, # if set --freeze_unet=False, will ft all the unet choices=["crossattn-kv", "crossattn", "transblocks", "all", "null"], help="which structure of unet will be unfreezed") parser.add_argument('--controlnet_attn', type=str_to_bool, nargs='?', const=True, default=False, help="if use ref image to replace the text prompt in controlnet") parser.add_argument('--use_cfg', type=str_to_bool, nargs='?', const=True, default=False, help="if use classifier free guidance for image") ### reference image attention with clip parser.add_argument('--refer_clip_preprocess', type=str_to_bool, nargs='?', const=True, default=False, help='if use clip preprocess in diffusers to process the reference image') parser.add_argument('--refer_clip_proj', type=str_to_bool, nargs='?', const=True, default=False, help='if use pretrained clip visual projection layer for the reference image') ### reference image attention with clip + combine controlnet path parser.add_argument('--ref_null_caption', type=str_to_bool, # use null caption for the reference controlnet path nargs='?', const=True, default=False) parser.add_argument('--combine_clip_local', type=str_to_bool, nargs='?', const=True, default=False, help='if use local clip feature in combine controlnet path (a bit messey here)') parser.add_argument('--combine_use_mask', type=str_to_bool, nargs='?', const=True, default=False, help='if add mask annotation to the (attn + controlnet) structure; default: attn human pose; controlnet background') parser.add_argument("--drop_ref", default=0., type=float, # drop the reference image during trianing? help="prob to drop reference image") parser.add_argument('--my_adapter', type=str_to_bool, nargs='?', const=True, default=False, help='if use my adapter?') ### no crop in training, resize after generation (post resize) parser.add_argument('--pos_resize_img', type=str_to_bool, nargs='?', const=True, default=False, help='resize img to the targe size after generation?') parser.add_argument("--fg_variation", default=0., type=float, help="if add foreground variation during training") parser.add_argument('--strong_aug_stage2', type=str_to_bool, nargs='?', const=True, default=False, help='if use strong aug in stage 1 for warm up?') parser.add_argument('--strong_rand_stage2', type=str_to_bool, nargs='?', const=True, default=False, help='if use strong rand in stage 2 for warm up?') ### stage 1, warm up with training attribute parser.add_argument('--strong_aug_stage1', type=str_to_bool, nargs='?', const=True, default=False, help='if use strong aug in stage 1 for warm up?') parser.add_argument('--stage1_pretrain_path', default=None, type=str, help='if use stage 1 attribute pretraining to initialize the model (unet + ref controlnet') parser.add_argument('--stage2_only_pose', type=str_to_bool, nargs='?', const=True, default=False, help='if only train pose controlnet in the 2nd stage') parser.add_argument('--constant_lr', type=str_to_bool, nargs='?', const=True, default=False, help='no linear lr decay?') ### SD-2-1 args parser.add_argument('--SD2_not_add_image_emb_noise', type=str_to_bool, nargs='?', const=True, default=False, help='if not add noise to image embedding? default add noise') # evaluation configs parser.add_argument('--val_yaml', default=None, type=str) parser.add_argument("--max_eval_samples", default=None, type=int, help="number eval samples") parser.add_argument("--debug_max_eval_samples", default=20, type=int, help="number eval samples") parser.add_argument('--pose_normalize', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--normalize_by_1st_frm', type=str_to_bool, nargs='?', const=True, default=False) # Note that tbs/ebs is the Global batch size = GPU_PER_NODE * NODE_COUNT * LOCAL_BATCH_SIZE parser.add_argument('--local_eval_batch_size', default=1, type=int) parser.add_argument('--eval_visu', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--eval_visu_trainsample', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--eval_visu_imagefolder', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--eval_visu_changepose', type=str_to_bool, nargs='?', const=True, default=False, help='if change pose from the other data source?') parser.add_argument('--eval_visu_changefore', type=str_to_bool, nargs='?', const=True, default=False, help='if change foreground from the other data source?') parser.add_argument('--eval_save_filename', default='eval_visu', type=str) parser.add_argument('--eval_before_train', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--eval_scheduler', default="ddim", type=str, choices=["pndms", "ddpm", 'ddim'], help="frame interpolation mode") parser.add_argument('--eval_enc_dec_only', type=str_to_bool, nargs='?', const=True, default=False) parser.add_argument('--num_inf_videos_per_prompt', default=1, type=int) parser.add_argument('--num_inference_steps', default=50, type=int) parser.add_argument('--guidance_scale', default=3, type=float) parser.add_argument('--stepwise_sample_depth',default=-1, type=int) parser.add_argument('--interpolation', default=None, type=str, choices=["copy", "average", 'interpolate', 'average_noise', None], help="frame interpolation mode") parser.add_argument('--interpolate_mode',default=None, type=str, choices=["nearest", "bilinear", 'trilinear', 'area', 'nearest-exact', None], help="frame interpolation mode") ### save the visualization file? parser.add_argument('--visu_save', type=str_to_bool, # if True, means save the visualization file with each filename nargs='?', const=True, default=False) ### stage3 ft on specific images parser.add_argument('--freeze_pose', type=str_to_bool, nargs='?', const=True, default=False, help='freeze the pose path?') parser.add_argument('--freeze_background', type=str_to_bool, nargs='?', const=True, default=False, help='freeze the background path?') parser.add_argument('--ft_img_num', default=0, type=int, help='use how many frame as the training sample? 0 denote all') parser.add_argument('--ft_one_ref_image', type=str_to_bool, nargs='?', const=True, default=True, help='only use first frame as the ref image?') parser.add_argument('--ft_iters', default=None, type=int) ### dreampose baseline ### parser.add_argument("--s1", default=1.0, type=float, # the param s1 for dreampose help="the param s1 for dreampose") parser.add_argument("--s2", default=1.0, type=float, # the param s2 for dreampose help="the param s2 for dreampose") ### stage 3 ft ### parser.add_argument("--ft_idx", default=None, type=str, # if set --freeze_unet=False, will ft all the unet help="ft video idx from web folder") ### stage 2 ref mode ### parser.add_argument("--ref_mode", default='first', type=str, help="ref mode") self.parser = parser def parse_args(self): parsed_args = self.parser.parse_args() if parsed_args.root_dir: BasicArgs.root_dir = parsed_args.root_dir else: parsed_args.root_dir = BasicArgs.root_dir parsed_args.pretrained_model_path = os.path.join(parsed_args.root_dir, parsed_args.pretrained_model_path) args = parse_with_cf(parsed_args) if args.debug: args.max_video_len = getattr(args, 'debug_max_video_len', 1) args.max_train_samples = getattr(args, 'debug_max_train_samples', 100) args.max_eval_samples = getattr(args, 'debug_max_eval_samples', 20) args.num_workers = 0 args.epochs = 2 args.eval_step = 1 args.save_step = 1 if hasattr(args, 'debug_train_yaml'): args.train_yaml = args.debug_train_yaml if hasattr(args, 'debug_val_yaml'): args.val_yaml = args.debug_val_yaml args.n_gpu = T.cuda.device_count() # local size args.local_size = args.n_gpu if args.root_dir not in args.log_dir: args.log_dir = os.path.join(args.root_dir, args.log_dir) # if len(args.conds) > 0: # args.log_dir += "_".join(args.conds) # args.wandb_project = [ # p_.replace(".py", "") # for p_ in args.cf.split("/") # if p_ != "config" and p_ != "."] # args.wandb_project = [args.method_name] + args.log_dir.split('/')[-2:] args.wandb_project = args.log_dir.split('/')[-2:] # args.project_name = "/".join( # args.wandb_project) args.project_name = args.wandb_project[1] # args.wandb_project = args.task_name #args.wandb_project[0] args.wandb_project = args.wandb_project[0] if args.stepwise_sample_depth == -1: args.interpolation = None args.interpolate_mode = None if args.interpolation != "interpolate": args.interpolate_mode = None assert args.eval_step > 0, "eval_step must be positive" assert args.save_step > 0, "save_step must be positive" dist_init(args) args.dist = args.distributed args.nodes = args.num_nodes args.world_size = args.num_gpus args.train_batch_size = args.local_train_batch_size * args.world_size args.eval_batch_size = args.local_eval_batch_size * args.world_size return args sharedArgs = Args() # def get_args(distributed=True): # args = sharedArgs.parse_args() # dist_init(args, distributed) # if not args.distributed: # args.deepspeed = False # args.effective_batch_size = args.size_batch * args.num_gpus # if os.path.exists(args.path_ckpt): # path_ckpt_dir = os.path.dirname(args.path_ckpt) # training_args = f"{path_ckpt_dir}/args.json" # if os.path.exists(training_args): # args = update_args(args) # return args # def update_args(args): # path_ckpt_dir = os.path.dirname(args.path_ckpt) # training_args = edict(json.load(open(f"{path_ckpt_dir}/args.json", "r"))) # print("===============Loaded model training args=================") # print(f"\t\t{json.dumps(training_args)}") # print("===============Default args=================") # print(f"\t\t{json.dumps(args)}") # toUpdate = [ # "vis_backbone", "vis_backbone_size", "temporal_fusion", # "imagenet", "kinetics", "swinbert", # "txt_backbone", "fusion_encoder", # "txt_backbone_embed_only", "tokenizer", "mask_pos", "fuse_type", # "num_fuse_block_t2i", "num_fuse_block_i2t"] # if args.size_epoch == 0: # toUpdate += ['size_frame', 'size_txt', 'size_img', 'img_transform'] # args.imagenet_norm = False # for key in training_args: # if key == "imagenet_norm": # args.imagenet_norm = training_args.imagenet_norm # if key in toUpdate: # args[key] = training_args[key] # if "vidswin" in key: # new_key = key.replace("vidswin", "vis_backbone") # print(f"Make old key compatible, old: {key}, new {new_key}") # args[new_key] = training_args[key] # if "backbone" in key and not ( # 'vis_backbone' in key or 'txt_backbone' in key): # new_key = key.replace("backbone", "vis_backbone") # print(f"Make old key compatible, old: {key}, new {new_key}") # if new_key in toUpdate: # args[new_key] = training_args[key] # if "vis_backbone" not in training_args and "backbone" not in training_args: # print(f"Evaluating models without specific backbone," # f"revert to default: vidswin") # args.vis_backbone = "vidswin" # return args