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on
Zero
Running
on
Zero
| import argparse | |
| from .get_opt import get_opt | |
| class GenerateOptions(): | |
| def __init__(self, app=False): | |
| self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| self.initialize() | |
| def initialize(self): | |
| self.parser.add_argument("--opt_path", type=str, default='./checkpoints/t2m/t2m_condunet1d_batch64/opt.txt', help='option file path for loading model') | |
| self.parser.add_argument("--gpu_id", type=int, default=0, help='GPU id') | |
| self.parser.add_argument("--output_dir", type=str, default='', help='Directory path to save generation result') | |
| self.parser.add_argument("--footskate_cleanup", action="store_true", help='Where use footskate cleanup in inference') | |
| # inference | |
| self.parser.add_argument("--num_inference_steps", type=int, default=10, help='Number of iterative denoising steps during inference.') | |
| self.parser.add_argument("--which_ckpt", type=str, default='latest', help='name of checkpoint to load') | |
| self.parser.add_argument("--diffuser_name", type=str, default='dpmsolver', help='sampler\'s scheduler class name in the diffuser library') | |
| self.parser.add_argument("--no_ema", action="store_true", help='Where use EMA model in inference') | |
| self.parser.add_argument("--no_fp16", action="store_true", help='Whether use FP16 in inference') | |
| self.parser.add_argument('--batch_size', type=int, default=1, help='Batch size for generate') | |
| self.parser.add_argument("--seed", default=0, type=int, help="For fixing random seed.") | |
| self.parser.add_argument('--dropout', type=float, default=0.1, help='dropout') | |
| # generate prompts | |
| self.parser.add_argument('--text_prompt', type=str, default="", help='One text description pompt for motion generation') | |
| self.parser.add_argument("--motion_length", default=4.0, type=float, help="The length of the generated motion [in seconds] when using prompts. Maximum is 9.8 for HumanML3D (text-to-motion)") | |
| self.parser.add_argument('--input_text', type=str, default='', help='File path of texts when using multiple texts.') | |
| self.parser.add_argument('--input_lens', type=str, default='', help='File path of expected motion frame lengths when using multitext.') | |
| self.parser.add_argument("--num_samples", type=int, default=10, help='Number of samples for generate when using dataset.') | |
| self.parser.add_argument('--debug', action="store_true", help='debug mode') | |
| self.parser.add_argument('--self_attention', action="store_true", help='self_attention use or not') | |
| self.parser.add_argument('--no_eff', action='store_true', help='whether use efficient linear attention') | |
| self.parser.add_argument('--vis_attn', action='store_true', help='vis attention value or not') | |
| self.parser.add_argument('--edit_mode', action='store_true', help='editing mode') | |
| def parse(self): | |
| self.opt = self.parser.parse_args() | |
| opt_path = self.opt.opt_path | |
| get_opt(self.opt, opt_path) | |
| return self.opt | |
| def parse_app(self): | |
| self.opt = self.parser.parse_args( | |
| args=['--motion_length', '8', '--self_attention', '--no_eff', '--opt_path', './checkpoints/t2m/release/opt.txt', '--edit_mode'] | |
| ) | |
| opt_path = self.opt.opt_path | |
| get_opt(self.opt, opt_path) | |
| return self.opt |