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from __future__ import annotations |
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import os |
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import pathlib |
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import random |
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import shlex |
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import subprocess |
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import gradio as gr |
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import torch |
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from huggingface_hub import snapshot_download |
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if os.getenv('SYSTEM') == 'spaces': |
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subprocess.run(shlex.split('pip uninstall -y modelscope')) |
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subprocess.run( |
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shlex.split( |
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'pip install git+https://github.com/modelscope/modelscope.git@refs/pull/207/head' |
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)) |
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from modelscope.outputs import OutputKeys |
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from modelscope.pipelines import pipeline |
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model_dir = pathlib.Path('weights') |
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if not model_dir.exists(): |
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model_dir.mkdir() |
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snapshot_download('damo-vilab/modelscope-damo-text-to-video-synthesis', |
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repo_type='model', |
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local_dir=model_dir) |
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DESCRIPTION = '# [Text-to-Video Playground](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)' |
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if (SPACE_ID := os.getenv('SPACE_ID')) is not None: |
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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>' |
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pipe = pipeline('text-to-video-synthesis', model_dir.as_posix()) |
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def generate(prompt: str, seed: int) -> str: |
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if seed == -1: |
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seed = random.randint(0, 1000000) |
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torch.manual_seed(seed) |
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return pipe({'text': prompt})[OutputKeys.OUTPUT_VIDEO] |
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examples = [ |
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['An astronaut riding a horse.', 0], |
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['A panda eating bamboo on a rock.', 0], |
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['Spiderman is surfing.', 0], |
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] |
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with gr.Blocks(css='style.css') as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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with gr.Column(): |
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prompt = gr.Text(label='Prompt', max_lines=1) |
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seed = gr.Slider( |
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label='Seed', |
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minimum=-1, |
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maximum=1000000, |
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step=25, |
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value=-1, |
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info='If set to -1, a different seed will be used each time.') |
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run_button = gr.Button('Run') |
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with gr.Column(): |
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result = gr.Video(label='Result') |
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inputs = [prompt, seed] |
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gr.Examples(examples=examples, |
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inputs=inputs, |
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outputs=result, |
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fn=generate, |
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cache_examples=os.getenv('SYSTEM') == 'spaces') |
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prompt.submit(fn=generate, inputs=inputs, outputs=result) |
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run_button.click(fn=generate, inputs=inputs, outputs=result) |
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demo.queue(api_open=False, max_size=15).launch() |
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