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import random | |
import torch | |
def set_env(seed=0): | |
torch.manual_seed(seed) | |
torch.set_grad_enabled(False) | |
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
if randomize_seed: | |
seed = random.randint(0, 203279) | |
return seed | |
title_markdown = (""" | |
<div style='display: flex; align-items: center; justify-content: center; text-align: center;'> | |
<img src='https://www.pnglog.com/AOuPMh.png' style='width: 400px; height: auto; margin-right: 10px;' /> | |
</div> | |
""") | |
DESCRIPTION = """ | |
# Open-Sora-Plan v1.1.0 | |
## If Open-Sora-Plan is helpful, please help to ✨ the [Github Repo](https://github.com/PKU-YuanGroup/Open-Sora-Plan) and recommend it to your friends 😊 | |
#### [Open-Sora-Plan v1.1.0](https://github.com/PKU-YuanGroup/Open-Sora-Plan) is a transformer-based text-to-video diffusion system trained on text embeddings from T5. | |
#### This demo is only trained on 3k hours videos, when creating videos, please be aware that it has the potential to generate harmful videos. For more details read our [report](). | |
#### Image generation is typically 50 steps, video generation maybe 150 steps will yield good results, but this may take 3-4 minutes. | |
#### Feel free to enjoy the examples. | |
#### English prompts ONLY; 提示词仅限英文 | |
#### | |
""" | |
# <br> | |
# <div align="center"> | |
# <div style="display:flex; gap: 0.25rem;" align="center"> | |
# <a href='https://github.com/PKU-YuanGroup/Open-Sora-Plan'><img src='https://img.shields.io/badge/Github-Code-blue'></a> | |
# <a href='https://github.com/PKU-YuanGroup/Open-Sora-Plan/stargazers'><img src='https://img.shields.io/github/stars/PKU-YuanGroup/Open-Sora-Plan.svg?style=social'></a> | |
# </div> | |
# </div> | |
# """) | |
block_css = """ | |
#buttons button { | |
min-width: min(120px,100%); | |
} | |
""" | |
with open('examples/65.txt', 'r') as f: | |
examples_txt = f.readlines() | |
examples_txt = [i.strip() for i in examples_txt] | |
examples = [[i, 50, 7.5] for i in examples_txt] | |