import os
import time
import random
import datetime
import os.path as osp
from functools import partial
import torch
import gradio as gr
from omegaconf import OmegaConf
from mld.config import get_module_config
from mld.data.get_data import get_datasets
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
WEBSITE = """
MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model
"""
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"
]
CSS = """
.contour_video {
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
z-index: var(--layer-5);
border-radius: var(--block-radius);
background: var(--background-fill-primary);
padding: 0 var(--size-6);
max-height: var(--size-screen-h);
overflow: hidden;
}
"""
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 = 12
T2M_CFG = "./configs/motionlcm_t2m.yaml"
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
cfg = OmegaConf.load(T2M_CFG)
cfg_model = get_module_config(cfg.model, cfg.model.target)
cfg = OmegaConf.merge(cfg, cfg_model)
set_seed(1949)
name_time_str = osp.join(cfg.NAME, datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S"))
output_dir = osp.join(cfg.TEST_FOLDER, name_time_str)
vis_dir = osp.join(output_dir, 'samples')
os.makedirs(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))
lcm_key = 'denoiser.time_embedding.cond_proj.weight'
is_lcm = False
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}')
cfg.model.is_controlnet = False
datasets = get_datasets(cfg, phase="test")[0]
model = MLD(cfg, datasets)
model.to(device)
model.eval()
model.load_state_dict(state_dict)
@torch.no_grad()
def generate(text, motion_len, num_videos):
batch = {"text": [text] * num_videos, "length": [motion_len] * num_videos}
s = time.time()
joints, _ = model(batch)
runtime = round(time.time() - s, 3)
runtime_info = f'Inference {len(joints)} motions, runtime: {runtime}s, device: {device}'
path = []
for i in range(num_videos):
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=20)
path.append(video_path)
return path, runtime_info
# HTML component
def get_video_html(path, video_id, width=700, height=700):
video_html = f"""
"""
return video_html
def generate_component(generate_function, text, motion_len, num_videos):
if text == DEFAULT_TEXT or text == "" or text is None:
return [None for _ in range(MAX_VIDEOS)] + [None]
motion_len = max(36, min(int(float(motion_len) * 20), 196))
paths, info = generate_function(text, motion_len, num_videos)
htmls = [get_video_html(path, idx) for idx, path in enumerate(paths)]
htmls = htmls + [None for _ in range(max(0, MAX_VIDEOS - num_videos))]
return htmls + [info]
theme = gr.themes.Default(primary_hue="purple", secondary_hue="gray")
generate_and_show = partial(generate_component, generate)
with gr.Blocks(css=CSS, 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.Textbox(
show_label=True,
label="Motion length (in seconds, <=9.8s)",
value=5,
info="Any length exceeding 9.8s will be restricted to 9.8s.",
)
with gr.Column(scale=1):
num_videos = gr.Radio(
[1, 4, 8, 12],
label="Videos",
value=8,
info="Number of videos to generate.",
)
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):
def generate_example(text, motion_len, num_videos):
return generate_and_show(text, motion_len, num_videos)
examples = gr.Examples(
examples=[[x, None, None] for x in EXAMPLES],
inputs=[text, motion_len, num_videos],
examples_per_page=12,
run_on_click=False,
cache_examples=False,
fn=generate_example,
outputs=[],
)
for _ in range(3):
with gr.Row():
for _ in range(4):
video = gr.HTML()
videos.append(video)
# gr.HTML(WEBSITE_bottom)
# connect the examples to the output
# a bit hacky
examples.outputs = videos
def load_example(example_id):
processed_example = examples.non_none_processed_examples[example_id]
return gr.utils.resolve_singleton(processed_example)
examples.dataset.click(
load_example,
inputs=[examples.dataset],
outputs=examples.inputs_with_examples, # type: ignore
show_progress=False,
postprocess=False,
queue=False,
).then(fn=generate_example, inputs=examples.inputs, outputs=videos + [results])
gen_btn.click(
fn=generate_and_show,
inputs=[text, motion_len, num_videos],
outputs=videos + [results],
)
text.submit(
fn=generate_and_show,
inputs=[text, motion_len, num_videos],
outputs=videos + [results],
)
def clear_videos():
return [None for _ in range(MAX_VIDEOS)] + [DEFAULT_TEXT] + [None]
clear.click(fn=clear_videos, outputs=videos + [text] + [results])
demo.launch()