Spaces:
Runtime error
Runtime error
File size: 7,404 Bytes
e2a20af 47390c8 e2a20af |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 |
#!/usr/bin/env python
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=0.001)
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)
# enable_xformers_memory_efficient_attention = gr.Checkbox(label='Use xformers', 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.
- 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,
# enable_xformers_memory_efficient_attention,
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)
|