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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 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 app_colorization import create_demo as create_demo_colorization |
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from app_superresolution import create_demo as create_demo_superresolution |
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DESCRIPTION = '# [DDNM-HQ](https://github.com/wyhuai/DDNM/tree/main/hq_demo)' |
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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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if not torch.cuda.is_available(): |
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DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>' |
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if torch.cuda.is_available(): |
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MODEL_DIR = pathlib.Path('DDNM/hq_demo/data/pretrained') |
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if not MODEL_DIR.exists(): |
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MODEL_DIR.mkdir() |
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subprocess.run(shlex.split( |
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'wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_classifier.pt' |
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), |
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cwd=MODEL_DIR.as_posix()) |
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subprocess.run(shlex.split( |
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'wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion.pt' |
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), |
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cwd=MODEL_DIR.as_posix()) |
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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.Tabs(): |
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with gr.TabItem(label='Super-resolution'): |
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create_demo_superresolution() |
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with gr.TabItem(label='Colorization'): |
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create_demo_colorization() |
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demo.queue(api_open=False, max_size=5).launch() |
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