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import gradio as gr
import os
import subprocess
from huggingface_hub import snapshot_download
hf_token = os.environ.get("HF_TOKEN")
print(hf_token)
def set_accelerate_default_config():
try:
subprocess.run(["accelerate", "config", "default"], check=True)
print("Accelerate default config set successfully!")
except subprocess.CalledProcessError as e:
print(f"An error occurred: {e}")
def train_dreambooth_lora_sdxl(instance_data_dir):
script_filename = "train_dreambooth_lora_sdxl.py" # Assuming it's in the same folder
command = [
"accelerate",
"launch",
script_filename, # Use the local script
"--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0",
"--pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix",
f"--instance_data_dir={instance_data_dir}",
"--output_dir=lora-trained-xl-colab_2",
"--mixed_precision=fp16",
"--instance_prompt=egnestl",
"--resolution=1024",
"--train_batch_size=2",
"--gradient_accumulation_steps=2",
"--gradient_checkpointing",
"--learning_rate=1e-4",
"--lr_scheduler=constant",
"--lr_warmup_steps=0",
"--enable_xformers_memory_efficient_attention",
"--mixed_precision=fp16",
"--use_8bit_adam",
"--max_train_steps=25",
"--checkpointing_steps=717",
"--seed=0",
"--push_to_hub",
f"--hub_token={hf_token}"
]
try:
subprocess.run(command, check=True)
print("Training is finished!")
except subprocess.CalledProcessError as e:
print(f"An error occurred: {e}")
def main(dataset_url):
dataset_repo = dataset_url
# Automatically set local_dir based on the last part of dataset_repo
repo_parts = dataset_repo.split("/")
local_dir = f"./{repo_parts[-1]}" # Use the last part of the split
# Check if the directory exists and create it if necessary
if not os.path.exists(local_dir):
os.makedirs(local_dir)
gr.Info("Downloading dataset ...")
snapshot_download(
dataset_repo,
local_dir=local_dir,
repo_type="dataset",
ignore_patterns=".gitattributes",
token=hf_token
)
set_accelerate_default_config()
gr.Info("Training begins ...")
instance_data_dir = repo_parts[-1]
train_dreambooth_lora_sdxl(instance_data_dir)
return "Done"
with gr.Blocks() as demo:
with gr.Column():
dataset_id = gr.Textbox(label="Dataset ID")
train_button = gr.Button("Train !")
status = gr.Textbox(labe="Training status")
train_button.click(
fn = main,
inputs = [dataset_id],
outputs = [status]
)
demo.queue().launch() |