import gradio as gr
import torch
import os
import shutil
import requests
import subprocess
from subprocess import getoutput
from huggingface_hub import login, HfFileSystem, snapshot_download, HfApi, create_repo
is_gpu_associated = torch.cuda.is_available()
is_shared_ui = False
hf_token = 'HF TOKEN'
fs = HfFileSystem(token=hf_token)
api = HfApi()
if is_gpu_associated:
gpu_info = getoutput('nvidia-smi')
if("A10G" in gpu_info):
which_gpu = "A10G"
elif("T4" in gpu_info):
which_gpu = "T4"
else:
which_gpu = "CPU"
def check_upload_or_no(value):
if value is True:
return gr.update(visible=True)
else:
return gr.update(visible=False)
def load_images_to_dataset(images, dataset_name):
if is_shared_ui:
raise gr.Error("This Space only works in duplicated instances")
if dataset_name == "":
raise gr.Error("You forgot to name your new dataset. ")
# Create the directory if it doesn't exist
my_working_directory = f"my_working_directory_for_{dataset_name}"
if not os.path.exists(my_working_directory):
os.makedirs(my_working_directory)
# Assuming 'images' is a list of image file paths
for idx, image in enumerate(images):
# Get the base file name (without path) from the original location
image_name = os.path.basename(image.name)
# Construct the destination path in the working directory
destination_path = os.path.join(my_working_directory, image_name)
# Copy the image from the original location to the working directory
shutil.copy(image.name, destination_path)
# Print the image name and its corresponding save path
print(f"Image {idx + 1}: {image_name} copied to {destination_path}")
path_to_folder = my_working_directory
your_username = api.whoami(token=hf_token)["name"]
repo_id = f"{your_username}/{dataset_name}"
create_repo(repo_id=repo_id, repo_type="dataset", token=hf_token)
api.upload_folder(
folder_path=path_to_folder,
repo_id=repo_id,
repo_type="dataset",
token=hf_token
)
return "Done, your dataset is ready and loaded for the training step!", repo_id
def swap_hardware(hf_token, hardware="cpu-basic"):
hardware_url = f"https://huggingface.co/spaces/ClaireOzzz/train-dreambooth-lora-sdxl/hardware"
headers = { "authorization" : f"Bearer {hf_token}"}
body = {'flavor': hardware}
requests.post(hardware_url, json = body, headers=headers)
def swap_sleep_time(hf_token,sleep_time):
sleep_time_url = f"https://huggingface.co/api/spaces/ClaireOzzz/train-dreambooth-lora-sdxl/sleeptime"
headers = { "authorization" : f"Bearer {hf_token}"}
body = {'seconds':sleep_time}
requests.post(sleep_time_url,json=body,headers=headers)
def get_sleep_time(hf_token):
sleep_time_url = f"https://huggingface.co/api/spaces/ClaireOzzz/train-dreambooth-lora-sdxl"
headers = { "authorization" : f"Bearer {hf_token}"}
response = requests.get(sleep_time_url,headers=headers)
try:
gcTimeout = response.json()['runtime']['gcTimeout']
except:
gcTimeout = None
return gcTimeout
def write_to_community(title, description,hf_token):
api.create_discussion(repo_id=os.environ['ClaireOzzz/train-dreambooth-lora-sdxl'], title=title, description=description,repo_type="space", token=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(dataset_id, instance_data_dir, lora_trained_xl_folder, instance_prompt, max_train_steps, checkpoint_steps, remove_gpu):
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"--dataset_id={dataset_id}",
f"--instance_data_dir={instance_data_dir}",
f"--output_dir={lora_trained_xl_folder}",
"--mixed_precision=fp16",
f"--instance_prompt={instance_prompt}",
"--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",
f"--max_train_steps={max_train_steps}",
f"--checkpointing_steps={checkpoint_steps}",
"--seed=0",
"--push_to_hub",
f"--hub_token={hf_token}"
]
try:
subprocess.run(command, check=True)
print("Training is finished!")
if remove_gpu:
swap_hardware(hf_token, "cpu-basic")
else:
swap_sleep_time(hf_token, 300)
except subprocess.CalledProcessError as e:
print(f"An error occurred: {e}")
title="There was an error on during your training"
description=f'''
Unfortunately there was an error during training your {lora_trained_xl_folder} model.
Please check it out below. Feel free to report this issue to [SD-XL Dreambooth LoRa Training](https://huggingface.co/spaces/fffiloni/train-dreambooth-lora-sdxl):
```
{str(e)}
```
'''
if remove_gpu:
swap_hardware(hf_token, "cpu-basic")
else:
swap_sleep_time(hf_token, 300)
#write_to_community(title,description,hf_token)
def main(dataset_id,
lora_trained_xl_folder,
instance_prompt,
max_train_steps,
checkpoint_steps,
remove_gpu):
if is_shared_ui:
raise gr.Error("This Space only works in duplicated instances")
if not is_gpu_associated:
raise gr.Error("Please associate a T4 or A10G GPU for this Space")
if dataset_id == "":
raise gr.Error("You forgot to specify an image dataset")
if instance_prompt == "":
raise gr.Error("You forgot to specify a concept prompt")
if lora_trained_xl_folder == "":
raise gr.Error("You forgot to name the output folder for your model")
sleep_time = get_sleep_time(hf_token)
if sleep_time:
swap_sleep_time(hf_token, -1)
gr.Warning("If you did not check the `Remove GPU After training`, don't forget to remove the GPU attribution after you are done. ")
dataset_repo = dataset_id
# 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(dataset_id, instance_data_dir, lora_trained_xl_folder, instance_prompt, max_train_steps, checkpoint_steps, remove_gpu)
your_username = api.whoami(token=hf_token)["name"]
return f"Done, your trained model has been stored in your models library: {your_username}/{lora_trained_xl_folder}"
css="""
#col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
#upl-dataset-group {background-color: none!important;}
div#warning-ready {
background-color: #ecfdf5;
padding: 0 10px 5px;
margin: 20px 0;
}
div#warning-ready > .gr-prose > h2, div#warning-ready > .gr-prose > p {
color: #057857!important;
}
div#warning-duplicate {
background-color: #ebf5ff;
padding: 0 10px 5px;
margin: 20px 0;
}
div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
color: #0f4592!important;
}
div#warning-duplicate strong {
color: #0f4592;
}
p.actions {
display: flex;
align-items: center;
margin: 20px 0;
}
div#warning-duplicate .actions a {
display: inline-block;
margin-right: 10px;
}
div#warning-setgpu {
background-color: #fff4eb;
padding: 0 10px 5px;
margin: 20px 0;
}
div#warning-setgpu > .gr-prose > h2, div#warning-setgpu > .gr-prose > p {
color: #92220f!important;
}
div#warning-setgpu a, div#warning-setgpu b {
color: #91230f;
}
div#warning-setgpu p.actions > a {
display: inline-block;
background: #1f1f23;
border-radius: 40px;
padding: 6px 24px;
color: antiquewhite;
text-decoration: none;
font-weight: 600;
font-size: 1.2em;
}
button#load-dataset-btn{
min-height: 60px;
}
"""
def create_training_demo() -> gr.Blocks:
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
if is_shared_ui:
top_description = gr.HTML(f'''
Attention: this Space need to be duplicated to work
To make it work, duplicate the Space and run it on your own profile using a private GPU (T4-small or A10G-small).
A T4 costs US$0.60/h, so it should cost < US$1 to train most models.
# ''', elem_id="warning-setgpu")
gr.Markdown("# SD-XL Dreambooth LoRa Training UI ðŸ’")
upload_my_images = gr.Checkbox(label="Drop your training images ? (optional)", value=False)
gr.Markdown("Use this step to upload your training images and create a new dataset. If you already have a dataset stored on your HF profile, you can skip this step, and provide your dataset ID in the training `Datased ID` input below.")
with gr.Group(visible=False, elem_id="upl-dataset-group") as upload_group:
with gr.Row():
images = gr.File(file_types=["image"], label="Upload your images", file_count="multiple", interactive=True, visible=True)
with gr.Column():
new_dataset_name = gr.Textbox(label="Set new dataset name", placeholder="e.g.: my_awesome_dataset")
dataset_status = gr.Textbox(label="dataset status")
load_btn = gr.Button("Load images to new dataset", elem_id="load-dataset-btn")
gr.Markdown("## Training ")
gr.Markdown("You can use an existing image dataset, find a dataset example here: [https://huggingface.co/datasets/diffusers/dog-example](https://huggingface.co/datasets/diffusers/dog-example) ;)")
with gr.Row():
dataset_id = gr.Textbox(label="Dataset ID", info="use one of your previously uploaded image datasets on your HF profile", placeholder="diffusers/dog-example")
instance_prompt = gr.Textbox(label="Concept prompt", info="concept prompt - use a unique, made up word to avoid collisions")
with gr.Row():
model_output_folder = gr.Textbox(label="Output model folder name", placeholder="lora-trained-xl-folder")
max_train_steps = gr.Number(label="Max Training Steps", value=500, precision=0, step=10)
checkpoint_steps = gr.Number(label="Checkpoints Steps", value=100, precision=0, step=10)
remove_gpu = gr.Checkbox(label="Remove GPU After Training", value=True, info="If NOT enabled, don't forget to remove the GPU attribution after you are done.")
train_button = gr.Button("Train !")
train_status = gr.Textbox(label="Training status")
upload_my_images.change(
fn = check_upload_or_no,
inputs =[upload_my_images],
outputs = [upload_group]
)
load_btn.click(
fn = load_images_to_dataset,
inputs = [images, new_dataset_name],
outputs = [dataset_status, dataset_id]
)
train_button.click(
fn = main,
inputs = [
dataset_id,
model_output_folder,
instance_prompt,
max_train_steps,
checkpoint_steps,
remove_gpu
],
outputs = [train_status]
)
return demo
#demo.launch(debug=True, share=True)