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#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import random
import shlex
import subprocess
import gradio as gr
import torch
from huggingface_hub import snapshot_download
if os.getenv('SYSTEM') == 'spaces':
subprocess.run(shlex.split('pip uninstall -y modelscope'))
subprocess.run(
shlex.split(
'pip install git+https://github.com/modelscope/modelscope.git@refs/pull/207/head'
))
from modelscope.outputs import OutputKeys
from modelscope.pipelines import pipeline
model_dir = pathlib.Path('weights')
if not model_dir.exists():
model_dir.mkdir()
snapshot_download('damo-vilab/modelscope-damo-text-to-video-synthesis',
repo_type='model',
local_dir=model_dir)
DESCRIPTION = '# [ModelScope Text to Video Synthesis](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)'
if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
pipe = pipeline('text-to-video-synthesis', model_dir.as_posix())
def generate(prompt: str, seed: int) -> str:
if seed == -1:
seed = random.randint(0, 1000000)
torch.manual_seed(seed)
return pipe({'text': prompt})[OutputKeys.OUTPUT_VIDEO]
examples = [
['An astronaut riding a horse.', 0],
['A panda eating bamboo on a rock.', 0],
['Spiderman is surfing.', 0],
]
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
prompt = gr.Text(label='Prompt', max_lines=1)
seed = gr.Slider(
label='Seed',
minimum=-1,
maximum=1000000,
step=1,
value=-1,
info='If set to -1, a different seed will be used each time.')
run_button = gr.Button('Run')
with gr.Column():
result = gr.Video(label='Result')
inputs = [prompt, seed]
gr.Examples(examples=examples,
inputs=inputs,
outputs=result,
fn=generate,
cache_examples=os.getenv('SYSTEM') == 'spaces')
prompt.submit(fn=generate, inputs=inputs, outputs=result)
run_button.click(fn=generate, inputs=inputs, outputs=result)
demo.queue(api_open=False).launch()
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