|
|
|
|
|
from __future__ import annotations |
|
|
|
import os |
|
|
|
import gradio as gr |
|
|
|
from constants import UploadTarget |
|
from inference import InferencePipeline |
|
from trainer import Trainer |
|
|
|
|
|
def create_training_demo(trainer: Trainer, |
|
pipe: InferencePipeline | None = None) -> gr.Blocks: |
|
with gr.Blocks() as demo: |
|
with gr.Row(): |
|
with gr.Column(): |
|
with gr.Box(): |
|
gr.Markdown('Training Data') |
|
instance_images = gr.Files(label='Instance images') |
|
instance_prompt = gr.Textbox(label='Instance prompt', |
|
max_lines=1) |
|
gr.Markdown(''' |
|
- Upload images of the style you are planning on training on. |
|
- For an instance prompt, use a unique, made up word to avoid collisions. |
|
''') |
|
with gr.Box(): |
|
gr.Markdown('Output Model') |
|
output_model_name = gr.Text(label='Name of your model', |
|
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') |
|
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.SVDIFF_LIBRARY.value) |
|
gr.Markdown(''' |
|
- By default, trained models will be uploaded to [SVDiff-pytorch Library](https://huggingface.co./svdiff-library). |
|
- You can also choose "Personal Profile", in which case, the model will be uploaded to https://huggingface.co./{your_username}/{model_name}. |
|
''') |
|
|
|
with gr.Box(): |
|
gr.Markdown('Training Parameters') |
|
with gr.Row(): |
|
base_model = gr.Text( |
|
label='Base Model', |
|
value='runwayml/stable-diffusion-v1-5', |
|
max_lines=1) |
|
resolution = gr.Dropdown(choices=['512', '768'], |
|
value='512', |
|
label='Resolution') |
|
num_training_steps = gr.Number( |
|
label='Number of Training Steps', value=1000, precision=0) |
|
learning_rate = gr.Number(label='Learning Rate', value=0.005) |
|
gradient_accumulation = gr.Number( |
|
label='Number of Gradient Accumulation', |
|
value=1, |
|
precision=0) |
|
seed = gr.Slider(label='Seed', |
|
minimum=0, |
|
maximum=100000, |
|
step=1, |
|
value=0) |
|
fp16 = gr.Checkbox(label='FP16', value=False) |
|
use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True) |
|
gradient_checkpointing = gr.Checkbox(label='Use gradient checkpointing', value=True) |
|
|
|
checkpointing_steps = gr.Number(label='Checkpointing Steps', |
|
value=200, |
|
precision=0) |
|
use_wandb = gr.Checkbox(label='Use W&B', |
|
value=False, |
|
interactive=bool( |
|
os.getenv('WANDB_API_KEY'))) |
|
validation_epochs = gr.Number(label='Validation Epochs', |
|
value=200, |
|
precision=0) |
|
gr.Markdown(''' |
|
- The base model must be a model that is compatible with [diffusers](https://github.com/huggingface/diffusers) library. |
|
- It takes a few minutes to download the base model first. |
|
- It will take about 8 minutes to train for 1000 steps with a T4 GPU. |
|
- You may want to try a small number of steps first, like 1, to see if everything works fine in your environment. |
|
- You can check the training status by pressing the "Open logs" button if you are running this on your Space. |
|
- You need to set the environment variable `WANDB_API_KEY` if you'd like to use [W&B](https://wandb.ai/site). See [W&B documentation](https://docs.wandb.ai/guides/track/advanced/environment-variables). |
|
- **Note:** Due to [this issue](https://github.com/huggingface/accelerate/issues/944), currently, training will not terminate properly if you use W&B. |
|
''') |
|
|
|
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=[ |
|
instance_images, |
|
instance_prompt, |
|
output_model_name, |
|
delete_existing_model, |
|
validation_prompt, |
|
base_model, |
|
resolution, |
|
num_training_steps, |
|
learning_rate, |
|
gradient_accumulation, |
|
seed, |
|
fp16, |
|
use_8bit_adam, |
|
gradient_checkpointing, |
|
|
|
checkpointing_steps, |
|
use_wandb, |
|
validation_epochs, |
|
upload_to_hub, |
|
use_private_repo, |
|
delete_existing_repo, |
|
upload_to, |
|
remove_gpu_after_training, |
|
], |
|
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=True, debug=True) |
|
|
