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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import pathlib | |
| import shlex | |
| import subprocess | |
| import gradio as gr | |
| # base_url = 'https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/' | |
| # names = [ | |
| # 'body_pose_model.pth', | |
| # 'dpt_hybrid-midas-501f0c75.pt', | |
| # 'hand_pose_model.pth', | |
| # 'mlsd_large_512_fp32.pth', | |
| # 'mlsd_tiny_512_fp32.pth', | |
| # 'network-bsds500.pth', | |
| # 'upernet_global_small.pth', | |
| # ] | |
| # for name in names: | |
| # command = f'wget https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/{name} -O {name}' | |
| # out_path = pathlib.Path(f'ControlNet/annotator/ckpts/{name}') | |
| # if out_path.exists(): | |
| # continue | |
| # subprocess.run(shlex.split(command), cwd='ControlNet/annotator/ckpts/') | |
| from gradio_sketch import create_demo as create_demo_sketch | |
| from gradio_pose import create_demo as create_demo_pose | |
| from gradio_seg import create_demo as create_demo_seg | |
| from model import Model | |
| MAX_IMAGES = 1 | |
| description = '''This is an unofficial demo for T2I-Adapter. [Paper](https://arxiv.org/abs/2302.08453) [GitHub](https://github.com/TencentARC/T2I-Adapter) | |
| ''' | |
| if (SPACE_ID := os.getenv('SPACE_ID')) is not None: | |
| description += f'''<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.<br/> | |
| <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"> | |
| <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
| <p/> | |
| ''' | |
| model = Model() | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.Markdown("## T2I Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models.") | |
| gr.Markdown(description) | |
| with gr.Tabs(): | |
| with gr.TabItem('Sketch'): | |
| create_demo_sketch(model.process_sketch) | |
| with gr.TabItem('Pose'): | |
| create_demo_pose(model.process_pose) | |
| with gr.TabItem('Segmentation'): | |
| create_demo_seg(model.process_seg) | |
| demo.queue(api_open=False).launch() |