#!/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 = '# LoRA DreamBooth Training UI' ORIGINAL_SPACE_ID = 'hysts/LoRA-DreamBooth-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.
Duplicate Space
''' if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID: SETTINGS = f'Settings' else: SETTINGS = 'Settings' CUDA_NOT_AVAILABLE_WARNING = f'''# Attention - Running on CPU.
You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces. "T4 small" is sufficient to run this demo.
''' 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() 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) 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'): create_upload_demo(HF_TOKEN) demo.queue(max_size=1).launch(share=False)