67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
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 gradio as gr
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import torch
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from huggingface_hub import snapshot_download
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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=1,
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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(server_name="0.0.0.0")
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