app.py

#!/usr/bin/env python

from __future__ import annotations

import os

import gradio as gr
import torch

from app_inference import create_inference_demo
from app_training import create_training_demo
from app_upload import create_upload_demo
from inference import InferencePipeline
from trainer import Trainer

TITLE = '# [Tune-A-Video](https://tuneavideo.github.io/) Training UI'

ORIGINAL_SPACE_ID = 'Tune-A-Video-library/Tune-A-Video-Training-UI'
SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
SHARED_UI_WARNING = f'''# Attention - This Space doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU. (Please note that there seems to be an issue with training on the A10G GPU now. The model doesn't learn anything when trained on A10G. Training on T4 works perfectly fine and inference works fine on both.)

<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img 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>
'''

if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
else:
    SETTINGS = 'Settings'
CUDA_NOT_AVAILABLE_WARNING = f'''# Attention - Running on CPU.
<center>
You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
You can use "T4 small/medium" or "A10G small/large" to run this demo.
</center>
'''

HF_TOKEN_NOT_SPECIFIED_WARNING = f'''# Attention - The environment variable `HF_TOKEN` is not specified. Please specify your Hugging Face token with write permission as the value of it.
<center>
You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>.
You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab.
</center>
'''

HF_TOKEN = os.getenv('HF_TOKEN')


def show_warning(warning_text: str) -> gr.Blocks:
    with gr.Blocks() as demo:
        with gr.Box():
            gr.Markdown(warning_text)
    return demo


pipe = InferencePipeline(HF_TOKEN)
trainer = Trainer(HF_TOKEN)

with gr.Blocks(css='style.css') as demo:
    if os.getenv('IS_SHARED_UI'):
        show_warning(SHARED_UI_WARNING)
    if not torch.cuda.is_available():
        show_warning(CUDA_NOT_AVAILABLE_WARNING)
    if not HF_TOKEN:
        show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)

    gr.Markdown(TITLE)
    with gr.Tabs():
        with gr.TabItem('Train'):
            create_training_demo(trainer, pipe)
        with gr.TabItem('Test'):
            create_inference_demo(pipe, HF_TOKEN)
        with gr.TabItem('Upload'):
            gr.Markdown('''
            - 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.
            ''')
            create_upload_demo(HF_TOKEN)

demo.queue(max_size=1).launch(share=False)