app.py

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 = """
<div class="embed_hidden">
<h1 style='text-align: center'> MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model </h1>

<h2 style='text-align: center'>
<a href="https://github.com/Dai-Wenxun/" target="_blank"><nobr>Wenxun Dai</nobr><sup>1</sup></a> &emsp;
<a href="https://lhchen.top/" target="_blank"><nobr>Ling-Hao Chen</nobr></a><sup>1</sup> &emsp;
<a href="https://wangjingbo1219.github.io/" target="_blank"><nobr>Jingbo Wang</nobr></a><sup>2</sup> &emsp;
<a href="https://moonsliu.github.io/" target="_blank"><nobr>Jinpeng Liu</nobr></a><sup>1</sup> &emsp;
<a href="https://daibo.info/" target="_blank"><nobr>Bo Dai</nobr></a><sup>2</sup> &emsp;
<a href="https://andytang15.github.io/" target="_blank"><nobr>Yansong Tang</nobr></a><sup>1</sup>
</h2>

<h2 style='text-align: center'>
<nobr><sup>1</sup>Tsinghua University</nobr> &emsp;
<nobr><sup>2</sup>Shanghai AI Laboratory</nobr>
</h2>

</div>
"""

WEBSITE_bottom = """
<div class="embed_hidden">
<p>
Space adapted from <a href="https://huggingface.co./spaces/Mathux/TMR" target="_blank">TMR</a> 
and <a href="https://huggingface.co./spaces/MeYourHint/MoMask" target="_blank">MoMask</a>.
</p>
</div>
"""

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"""
<video class="contour_video" width="{width}" height="{height}" preload="auto" muted playsinline onpause="this.load()"
autoplay loop disablepictureinpicture id="{video_id}">
  <source src="https://wxdai-motionlcm.hf.space/file/{path}" type="video/mp4">
  Your browser does not support the video tag.
</video>
"""
    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()