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from __future__ import annotations |
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import os |
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from subprocess import getoutput |
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import gradio as gr |
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import torch |
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from app_inference import create_inference_demo |
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from app_training import create_training_demo |
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from app_upload import create_upload_demo |
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from inference import InferencePipeline |
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from trainer import Trainer |
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TITLE = '# [Tune-A-Video](https://tuneavideo.github.io/) UI' |
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ORIGINAL_SPACE_ID = 'Tune-A-Video-library/Tune-A-Video-Training-UI' |
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SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID) |
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GPU_DATA = getoutput('nvidia-smi') |
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SHARED_UI_WARNING = f'''## Attention - Training doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU. |
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<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center> |
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''' |
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if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID: |
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SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>' |
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else: |
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SETTINGS = 'Settings' |
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INVALID_GPU_WARNING = f'''## Attention - the specified GPU is invalid. Training may not work. Make sure you have selected a `T4 GPU` for this task.''' |
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CUDA_NOT_AVAILABLE_WARNING = f'''## Attention - Running on CPU. |
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<center> |
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You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces. |
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You can use "T4 small/medium" to run this demo. |
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</center> |
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''' |
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HF_TOKEN_NOT_SPECIFIED_WARNING = f'''The environment variable `HF_TOKEN` is not specified. Feel free to specify your Hugging Face token with write permission if you don't want to manually provide it for every run. |
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<center> |
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You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>. |
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You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab. |
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</center> |
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''' |
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HF_TOKEN = os.getenv('HF_TOKEN') |
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def show_warning(warning_text: str) -> gr.Blocks: |
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with gr.Blocks() as demo: |
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with gr.Box(): |
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gr.Markdown(warning_text) |
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return demo |
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pipe = InferencePipeline(HF_TOKEN) |
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trainer = Trainer(HF_TOKEN) |
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with gr.Blocks(css='style.css') as demo: |
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if SPACE_ID == ORIGINAL_SPACE_ID: |
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show_warning(SHARED_UI_WARNING) |
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elif not torch.cuda.is_available(): |
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show_warning(CUDA_NOT_AVAILABLE_WARNING) |
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elif(not "T4" in GPU_DATA): |
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show_warning(INVALID_GPU_WARNING) |
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gr.Markdown(TITLE) |
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with gr.Tabs(): |
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with gr.TabItem('Train'): |
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create_training_demo(trainer, pipe) |
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with gr.TabItem('Run'): |
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create_inference_demo(pipe, HF_TOKEN) |
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with gr.TabItem('Upload'): |
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gr.Markdown(''' |
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- You can use this tab to upload models later if you choose not to upload models in training time or if upload in training time failed. |
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''') |
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create_upload_demo(HF_TOKEN) |
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if not HF_TOKEN: |
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show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING) |
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demo.queue(max_size=1).launch(share=False) |
