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#!/usr/bin/env python
from __future__ import annotations
import os
import gradio as gr
from constants import MODEL_LIBRARY_ORG_NAME, SAMPLE_MODEL_REPO, UploadTarget
from inference import InferencePipeline
from trainer import Trainer
def create_training_demo(trainer: Trainer,
pipe: InferencePipeline | None = None) -> gr.Blocks:
hf_token = os.getenv('HF_TOKEN')
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
with gr.Box():
gr.Markdown('Training Data')
training_video = gr.File(label='Training video')
training_prompt = gr.Textbox(
label='Training prompt',
max_lines=1,
placeholder='A man is surfing')
gr.Markdown('''
- Upload a video and write a prompt that describes the video.
''')
with gr.Box():
gr.Markdown('Output Model')
output_model_name = gr.Text(label='Name of your model',
placeholder='The surfer man',
max_lines=1)
delete_existing_model = gr.Checkbox(
label='Delete existing model of the same name',
value=False)
validation_prompt = gr.Text(label='Validation Prompt', placeholder='a dog is surfing')
gr.Markdown('''
- The validation prompt should modify your `Training Prompt` in order to validate the model
''')
with gr.Column():
with gr.Box():
gr.Markdown('Training Parameters')
with gr.Row():
base_model = gr.Text(label='Base Model',
value='CompVis/stable-diffusion-v1-4',
max_lines=1)
resolution = gr.Dropdown(choices=['512', '768'],
value='512',
label='Resolution',
visible=False)
token = gr.Text(label="Hugging Face Write Token", placeholder="", visible=False if hf_token else True)
with gr.Accordion("Advanced settings", open=False):
num_training_steps = gr.Number(
label='Number of Training Steps', value=300, precision=0)
learning_rate = gr.Number(label='Learning Rate',
value=0.000035)
gradient_accumulation = gr.Number(
label='Number of Gradient Accumulation',
value=1,
precision=0)
seed = gr.Slider(label='Seed',
minimum=0,
maximum=100000,
step=1,
randomize=True,
value=0)
fp16 = gr.Checkbox(label='FP16', value=True)
use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=False)
checkpointing_steps = gr.Number(label='Checkpointing Steps',
value=1000,
precision=0)
validation_epochs = gr.Number(label='Validation Epochs',
value=100,
precision=0)
gr.Markdown('''
- The base model must be a Stable Diffusion model compatible with [diffusers](https://github.com/huggingface/diffusers) library.
- Expected time to train a model for 300 steps: ~20 minutes with T4
- You can check the training status by pressing the "Open logs" button if you are running this on your Space.
''')
with gr.Box():
gr.Markdown('Upload Settings')
with gr.Row():
upload_to_hub = gr.Checkbox(
label='Upload model to Hub', value=True)
use_private_repo = gr.Checkbox(label='Private',
value=True)
delete_existing_repo = gr.Checkbox(
label='Delete existing repo of the same name',
value=False)
upload_to = gr.Radio(
label='Upload to',
choices=[_.value for _ in UploadTarget],
value=UploadTarget.MODEL_LIBRARY.value)
gr.Markdown(f'''
- By default, trained models will be uploaded to [Tune-A-Video Library](https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}) (see [this example model](https://huggingface.co/{SAMPLE_MODEL_REPO})).
- You can also choose "Personal Profile", in which case, the model will be uploaded to https://huggingface.co/{{your_username}}/{{model_name}}.
''')
remove_gpu_after_training = gr.Checkbox(
label='Remove GPU after training',
value=False,
interactive=bool(os.getenv('SPACE_ID')),
visible=False)
run_button = gr.Button('Start Training')
with gr.Box():
gr.Markdown('Output message')
output_message = gr.Markdown()
if pipe is not None:
run_button.click(fn=pipe.clear)
run_button.click(fn=trainer.run,
inputs=[
training_video,
training_prompt,
output_model_name,
delete_existing_model,
validation_prompt,
base_model,
resolution,
num_training_steps,
learning_rate,
gradient_accumulation,
seed,
fp16,
use_8bit_adam,
checkpointing_steps,
validation_epochs,
upload_to_hub,
use_private_repo,
delete_existing_repo,
upload_to,
remove_gpu_after_training,
token
],
outputs=output_message)
return demo
if __name__ == '__main__':
hf_token = os.getenv('HF_TOKEN')
trainer = Trainer(hf_token)
demo = create_training_demo(trainer)
demo.queue(max_size=1).launch(share=False)
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