Spaces:
Sleeping
Sleeping
File size: 6,084 Bytes
db1e5fb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
#!/usr/bin/env python
from __future__ import annotations
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=False)
use_private_repo = gr.Checkbox(label='Private',
value=False)
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.PERSONAL_PROFILE.value)
with gr.Box():
gr.Markdown('Training Parameters')
with gr.Row():
base_model = gr.Text(
label='Base Model',
value='stabilityai/stable-diffusion-2-1-base',
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=0.0001)
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=True)
use_8bit_adam = gr.Checkbox(label='Use 8bit Adam', value=True)
checkpointing_steps = gr.Number(label='Checkpointing Steps',
value=100,
precision=0)
use_wandb = gr.Checkbox(label='Use W&B', value=False)
validation_epochs = gr.Number(label='Validation Epochs',
value=100,
precision=0)
gr.Markdown('''
- 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 need to set the environment variable `WANDB_API_KEY` if you'd like to use W&B. 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.
''')
# TODO currently disabled
remove_gpu_after_training = gr.Checkbox(
label='Remove GPU after training', value=False, interactive=False)
run_button = gr.Button('Start Training')
with gr.Box():
gr.Markdown('Message')
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,
checkpointing_steps,
use_wandb,
validation_epochs,
upload_to_hub,
use_private_repo,
delete_existing_repo,
upload_to,
],
outputs=message)
return demo
if __name__ == '__main__':
trainer = Trainer()
demo = create_training_demo(trainer)
demo.queue(max_size=1).launch(share=False)
|