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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( |
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shlex.split("wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_classifier.pt"), |
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cwd=MODEL_DIR.as_posix(), |
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) |
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subprocess.run( |
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shlex.split("wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion.pt"), |
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cwd=MODEL_DIR.as_posix(), |
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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.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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