import os
import time
import random
import datetime
import os.path as osp
from functools import partial
import tqdm
from omegaconf import OmegaConf
import torch
import gradio as gr
from mld.config import get_module_config
from mld.data.get_data import get_dataset
from mld.models.modeltype.mld import MLD
from mld.utils.utils import set_seed
from mld.data.humanml.utils.plot_script import plot_3d_motion
os.environ["TOKENIZERS_PARALLELISM"] = "false"
WEBSITE = """
MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model
1Tsinghua University
2Shanghai AI Laboratory
"""
WEBSITE_bottom = """
"""
EXAMPLES = [
"a person does a jump",
"a person waves both arms in the air.",
"The person takes 4 steps backwards.",
"this person bends forward as if to bow.",
"The person was pushed but did not fall.",
"a man walks forward in a snake like pattern.",
"a man paces back and forth along the same line.",
"with arms out to the sides a person walks forward",
"A man bends down and picks something up with his right hand.",
"The man walked forward, spun right on one foot and walked back to his original position.",
"a person slightly bent over with right hand pressing against the air walks forward slowly"
]
if not os.path.exists("./experiments_t2m/"):
os.system("bash prepare/download_pretrained_models.sh")
if not os.path.exists('./deps/glove/'):
os.system("bash prepare/download_glove.sh")
if not os.path.exists('./deps/sentence-t5-large/'):
os.system("bash prepare/prepare_t5.sh")
if not os.path.exists('./deps/t2m/'):
os.system("bash prepare/download_t2m_evaluators.sh")
if not os.path.exists('./datasets/humanml3d/'):
os.system("bash prepare/prepare_tiny_humanml3d.sh")
DEFAULT_TEXT = "A person is "
MAX_VIDEOS = 8
NUM_ROWS = 2
NUM_COLS = MAX_VIDEOS // NUM_ROWS
EXAMPLES_PER_PAGE = 12
T2M_CFG = "./configs_v1/motionlcm_t2m.yaml"
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print("device: ", device)
cfg = OmegaConf.load(T2M_CFG)
cfg_root = os.path.dirname(T2M_CFG)
cfg_model = get_module_config(cfg.model, cfg.model.target, cfg_root)
cfg = OmegaConf.merge(cfg, cfg_model)
set_seed(cfg.SEED_VALUE)
name_time_str = osp.join(cfg.NAME, datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S"))
cfg.output_dir = osp.join(cfg.TEST_FOLDER, name_time_str)
vis_dir = osp.join(cfg.output_dir, 'samples')
os.makedirs(cfg.output_dir, exist_ok=False)
os.makedirs(vis_dir, exist_ok=False)
state_dict = torch.load(cfg.TEST.CHECKPOINTS, map_location="cpu")["state_dict"]
print("Loading checkpoints from {}".format(cfg.TEST.CHECKPOINTS))
is_lcm = False
lcm_key = 'denoiser.time_embedding.cond_proj.weight' # unique key for CFG
if lcm_key in state_dict:
is_lcm = True
time_cond_proj_dim = state_dict[lcm_key].shape[1]
cfg.model.denoiser.params.time_cond_proj_dim = time_cond_proj_dim
print(f'Is LCM: {is_lcm}')
dataset = get_dataset(cfg)
model = MLD(cfg, dataset)
model.to(device)
model.eval()
model.requires_grad_(False)
model.load_state_dict(state_dict)
FPS = eval(f"cfg.DATASET.{cfg.DATASET.NAME.upper()}.FRAME_RATE")
@torch.no_grad()
def generate(text_, motion_len_):
batch = {"text": [text_] * MAX_VIDEOS, "length": [motion_len_] * MAX_VIDEOS}
s = time.time()
joints = model(batch)[0]
runtime_infer = round(time.time() - s, 3)
s = time.time()
path = []
for i in tqdm.tqdm(range(len(joints))):
uid = random.randrange(999999999)
video_path = osp.join(vis_dir, f"sample_{uid}.mp4")
plot_3d_motion(video_path, joints[i].detach().cpu().numpy(), '', fps=FPS)
path.append(video_path)
runtime_draw = round(time.time() - s, 3)
runtime_info = f'Inference {len(joints)} motions, Runtime (Inference): {runtime_infer}s, ' \
f'Runtime (Draw Skeleton): {runtime_draw}s, device: {device} '
return path, runtime_info
def generate_component(generate_function, text_, motion_len_, num_inference_steps_, guidance_scale_):
if text_ == DEFAULT_TEXT or text_ == "" or text_ is None:
return [None] * MAX_VIDEOS + ["Please modify the default text prompt."]
model.cfg.model.scheduler.num_inference_steps = num_inference_steps_
model.guidance_scale = guidance_scale_
motion_len_ = max(36, min(int(float(motion_len_) * FPS), 196))
paths, info = generate_function(text_, motion_len_)
paths = paths + [None] * (MAX_VIDEOS - len(paths))
return paths + [info]
theme = gr.themes.Default(primary_hue="purple", secondary_hue="gray")
generate_and_show = partial(generate_component, generate)
with gr.Blocks(theme=theme) as demo:
gr.HTML(WEBSITE)
videos = []
with gr.Row():
with gr.Column(scale=3):
text = gr.Textbox(
show_label=True,
label="Text prompt",
value=DEFAULT_TEXT,
)
with gr.Row():
with gr.Column(scale=1):
motion_len = gr.Slider(
minimum=1.8,
maximum=9.8,
step=0.2,
value=5.0,
label="Motion length",
info="Motion duration in seconds: [1.8s, 9.8s] (FPS = 20)."
)
with gr.Column(scale=1):
num_inference_steps = gr.Slider(
minimum=1,
maximum=4,
step=1,
value=1,
label="Inference steps",
info="Number of inference steps.",
)
cfg = gr.Slider(
minimum=1,
maximum=15,
step=0.5,
value=7.5,
label="CFG",
info="Classifier-free diffusion guidance.",
)
gen_btn = gr.Button("Generate", variant="primary")
clear = gr.Button("Clear", variant="secondary")
results = gr.Textbox(show_label=True,
label='Inference info (runtime and device)',
info='Real-time inference cannot be achieved using the free CPU. Local GPU deployment is recommended.',
interactive=False)
with gr.Column(scale=2):
examples = gr.Examples(
examples=EXAMPLES,
inputs=[text],
examples_per_page=EXAMPLES_PER_PAGE)
for i in range(NUM_ROWS):
with gr.Row():
for j in range(NUM_COLS):
video = gr.Video(autoplay=True, loop=True)
videos.append(video)
# gr.HTML(WEBSITE_bottom)
gen_btn.click(
fn=generate_and_show,
inputs=[text, motion_len, num_inference_steps, cfg],
outputs=videos + [results],
)
text.submit(
fn=generate_and_show,
inputs=[text, motion_len, num_inference_steps, cfg],
outputs=videos + [results],
)
def clear_videos():
return [None] * MAX_VIDEOS + [DEFAULT_TEXT] + [None]
clear.click(fn=clear_videos, outputs=videos + [text] + [results])
demo.launch()