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| import os | |
| import sys | |
| import gradio as gr | |
| try: | |
| os.system("pip install -r requirements.txt") | |
| except Exception as e: | |
| print(e) | |
| sys.path.insert(0, '.') | |
| from utils.get_opt import get_opt | |
| from os.path import join as pjoin | |
| import numpy as np | |
| from trainers import DDPMTrainer | |
| from models import MotionTransformer | |
| device = 'cpu' | |
| opt = get_opt("checkpoints/t2m/t2m_motiondiffuse/opt.txt", device) | |
| opt.do_denoise = True | |
| assert opt.dataset_name == "t2m" | |
| opt.data_root = './dataset/HumanML3D' | |
| opt.motion_dir = pjoin(opt.data_root, 'new_joint_vecs') | |
| opt.text_dir = pjoin(opt.data_root, 'texts') | |
| opt.joints_num = 22 | |
| opt.dim_pose = 263 | |
| mean = np.load(pjoin(opt.meta_dir, 'mean.npy')) | |
| std = np.load(pjoin(opt.meta_dir, 'std.npy')) | |
| def build_models(opt): | |
| encoder = MotionTransformer( | |
| input_feats=opt.dim_pose, | |
| num_frames=opt.max_motion_length, | |
| num_layers=opt.num_layers, | |
| latent_dim=opt.latent_dim, | |
| no_clip=opt.no_clip, | |
| no_eff=opt.no_eff) | |
| return encoder | |
| encoder = build_models(opt).to(device) | |
| trainer = DDPMTrainer(opt, encoder) | |
| trainer.load(pjoin(opt.model_dir, opt.which_epoch + '.tar')) | |
| trainer.eval_mode() | |
| trainer.to(opt.device) | |
| def generate(prompt, length): | |
| from tools.visualization import process | |
| result_path = "outputs/" + str(hash(prompt)) + ".mp4" | |
| process(trainer, opt, device, mean, std, prompt, int(length), result_path) | |
| return result_path | |
| demo = gr.Interface( | |
| fn=generate, | |
| inputs=["text", gr.Slider(20, 196, value=60)], | |
| examples=[ | |
| ["the man throws a punch with each hand.", 58], | |
| ["a person jogs clockwise in a circle.", 178], | |
| ["a person spins quickly and takes off running.", 29], | |
| ["a person is walking slowly forward.", 142], | |
| ["a person quickly waves with their right hand", 46], | |
| ["a person performing a slight bow", 89], | |
| ], | |
| outputs="video", | |
| title="MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model", | |
| description="This is an interactive demo for MotionDiffuse. For more information, feel free to visit our project page(https://mingyuan-zhang.github.io/projects/MotionDiffuse.html).") | |
| demo.launch(share=True) |