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Delete app.py
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app.py
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@@ -1,384 +0,0 @@
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import gradio as gr
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import torch
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import os
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import shutil
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import requests
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import subprocess
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from subprocess import getoutput
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from huggingface_hub import snapshot_download, HfApi, create_repo
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api = HfApi()
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hf_token = os.environ.get("HF_TOKEN_WITH_WRITE_PERMISSION")
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is_shared_ui = True if "fffiloni/train-dreambooth-lora-sdxl" in os.environ['SPACE_ID'] else False
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is_gpu_associated = torch.cuda.is_available()
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if is_gpu_associated:
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gpu_info = getoutput('nvidia-smi')
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if("A10G" in gpu_info):
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which_gpu = "A10G"
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elif("T4" in gpu_info):
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which_gpu = "T4"
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else:
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which_gpu = "CPU"
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def check_upload_or_no(value):
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if value is True:
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return gr.update(visible=True)
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else:
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return gr.update(visible=False)
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def load_images_to_dataset(images, dataset_name):
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if is_shared_ui:
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raise gr.Error("This Space only works in duplicated instances")
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if dataset_name == "":
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raise gr.Error("You forgot to name your new dataset. ")
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# Create the directory if it doesn't exist
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my_working_directory = f"my_working_directory_for_{dataset_name}"
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if not os.path.exists(my_working_directory):
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os.makedirs(my_working_directory)
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# Assuming 'images' is a list of image file paths
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for idx, image in enumerate(images):
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# Get the base file name (without path) from the original location
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image_name = os.path.basename(image.name)
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# Construct the destination path in the working directory
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destination_path = os.path.join(my_working_directory, image_name)
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# Copy the image from the original location to the working directory
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shutil.copy(image.name, destination_path)
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# Print the image name and its corresponding save path
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print(f"Image {idx + 1}: {image_name} copied to {destination_path}")
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path_to_folder = my_working_directory
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your_username = api.whoami(token=hf_token)["name"]
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repo_id = f"{your_username}/{dataset_name}"
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create_repo(repo_id=repo_id, repo_type="dataset", private=True, token=hf_token)
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api.upload_folder(
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folder_path=path_to_folder,
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repo_id=repo_id,
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repo_type="dataset",
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token=hf_token
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)
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return "Done, your dataset is ready and loaded for the training step!", repo_id
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def swap_hardware(hf_token, hardware="cpu-basic"):
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hardware_url = f"https://huggingface.co/spaces/{os.environ['SPACE_ID']}/hardware"
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headers = { "authorization" : f"Bearer {hf_token}"}
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body = {'flavor': hardware}
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requests.post(hardware_url, json = body, headers=headers)
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def swap_sleep_time(hf_token,sleep_time):
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sleep_time_url = f"https://huggingface.co/api/spaces/{os.environ['SPACE_ID']}/sleeptime"
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headers = { "authorization" : f"Bearer {hf_token}"}
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body = {'seconds':sleep_time}
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requests.post(sleep_time_url,json=body,headers=headers)
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def get_sleep_time(hf_token):
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sleep_time_url = f"https://huggingface.co/api/spaces/{os.environ['SPACE_ID']}"
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headers = { "authorization" : f"Bearer {hf_token}"}
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response = requests.get(sleep_time_url,headers=headers)
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try:
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gcTimeout = response.json()['runtime']['gcTimeout']
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except:
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gcTimeout = None
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return gcTimeout
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def write_to_community(title, description,hf_token):
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api.create_discussion(repo_id=os.environ['SPACE_ID'], title=title, description=description,repo_type="space", token=hf_token)
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def set_accelerate_default_config():
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try:
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subprocess.run(["accelerate", "config", "default"], check=True)
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print("Accelerate default config set successfully!")
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except subprocess.CalledProcessError as e:
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print(f"An error occurred: {e}")
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def train_dreambooth_lora_sdxl(dataset_id, instance_data_dir, lora_trained_xl_folder, instance_prompt, max_train_steps, checkpoint_steps, remove_gpu):
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script_filename = "train_dreambooth_lora_sdxl.py" # Assuming it's in the same folder
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command = [
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"accelerate",
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"launch",
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script_filename, # Use the local script
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"--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0",
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"--pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix",
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f"--dataset_id={dataset_id}",
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f"--instance_data_dir={instance_data_dir}",
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f"--output_dir={lora_trained_xl_folder}",
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"--mixed_precision=fp16",
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f"--instance_prompt={instance_prompt}",
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"--resolution=1024",
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"--train_batch_size=2",
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"--gradient_accumulation_steps=2",
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"--gradient_checkpointing",
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"--learning_rate=1e-4",
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"--lr_scheduler=constant",
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"--lr_warmup_steps=0",
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"--enable_xformers_memory_efficient_attention",
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"--mixed_precision=fp16",
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"--use_8bit_adam",
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f"--max_train_steps={max_train_steps}",
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f"--checkpointing_steps={checkpoint_steps}",
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"--seed=0",
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"--push_to_hub",
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f"--hub_token={hf_token}"
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]
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try:
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subprocess.run(command, check=True)
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print("Training is finished!")
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if remove_gpu:
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swap_hardware(hf_token, "cpu-basic")
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except subprocess.CalledProcessError as e:
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print(f"An error occurred: {e}")
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title="There was an error on during your training"
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description=f'''
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Unfortunately there was an error during training your {lora_trained_xl_folder} model.
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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):
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```
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{str(e)}
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```
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'''
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if remove_gpu:
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swap_hardware(hf_token, "cpu-basic")
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#write_to_community(title,description,hf_token)
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def main(dataset_id,
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lora_trained_xl_folder,
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instance_prompt,
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max_train_steps,
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checkpoint_steps,
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remove_gpu):
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if is_shared_ui:
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raise gr.Error("This Space only works in duplicated instances")
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if not is_gpu_associated:
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raise gr.Error("Please associate a T4 or A10G GPU for this Space")
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if dataset_id == "":
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raise gr.Error("You forgot to specify an image dataset")
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if instance_prompt == "":
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raise gr.Error("You forgot to specify a concept prompt")
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if lora_trained_xl_folder == "":
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raise gr.Error("You forgot to name the output folder for your model")
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sleep_time = get_sleep_time(hf_token)
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if sleep_time:
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swap_sleep_time(hf_token, -1)
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gr.Warning("If you did not check the `Remove GPU After training`, don't forget to remove the GPU attribution after you are done. ")
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dataset_repo = dataset_id
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# Automatically set local_dir based on the last part of dataset_repo
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repo_parts = dataset_repo.split("/")
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local_dir = f"./{repo_parts[-1]}" # Use the last part of the split
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# Check if the directory exists and create it if necessary
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if not os.path.exists(local_dir):
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os.makedirs(local_dir)
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gr.Info("Downloading dataset ...")
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snapshot_download(
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dataset_repo,
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local_dir=local_dir,
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repo_type="dataset",
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ignore_patterns=".gitattributes",
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token=hf_token
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)
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set_accelerate_default_config()
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gr.Info("Training begins ...")
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instance_data_dir = repo_parts[-1]
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train_dreambooth_lora_sdxl(dataset_id, instance_data_dir, lora_trained_xl_folder, instance_prompt, max_train_steps, checkpoint_steps, remove_gpu)
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your_username = api.whoami(token=hf_token)["name"]
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return f"Done, your trained model has been stored in your models library: {your_username}/{lora_trained_xl_folder}"
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css="""
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#col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
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#upl-dataset-group {background-color: none!important;}
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div#warning-ready {
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background-color: #ecfdf5;
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padding: 0 10px 5px;
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margin: 20px 0;
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}
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div#warning-ready > .gr-prose > h2, div#warning-ready > .gr-prose > p {
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color: #057857!important;
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}
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div#warning-duplicate {
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background-color: #ebf5ff;
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padding: 0 10px 5px;
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margin: 20px 0;
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}
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div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
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color: #0f4592!important;
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}
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div#warning-duplicate strong {
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color: #0f4592;
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}
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p.actions {
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display: flex;
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align-items: center;
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margin: 20px 0;
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}
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div#warning-duplicate .actions a {
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display: inline-block;
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margin-right: 10px;
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}
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div#warning-setgpu {
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background-color: #fff4eb;
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padding: 0 10px 5px;
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margin: 20px 0;
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}
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div#warning-setgpu > .gr-prose > h2, div#warning-setgpu > .gr-prose > p {
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color: #92220f!important;
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}
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div#warning-setgpu a, div#warning-setgpu b {
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color: #91230f;
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}
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div#warning-setgpu p.actions > a {
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display: inline-block;
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background: #1f1f23;
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border-radius: 40px;
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padding: 6px 24px;
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color: antiquewhite;
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text-decoration: none;
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font-weight: 600;
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font-size: 1.2em;
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}
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button#load-dataset-btn{
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min-height: 60px;
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}
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"""
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="col-container"):
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if is_shared_ui:
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top_description = gr.HTML(f'''
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<div class="gr-prose">
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<h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
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Attention: this Space need to be duplicated to work</h2>
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<p class="main-message">
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To make it work, <strong>duplicate the Space</strong> and run it on your own profile using a <strong>private</strong> GPU (T4-small or A10G-small).<br />
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A T4 costs <strong>US$0.60/h</strong>, so it should cost < US$1 to train most models.
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</p>
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<p class="actions">
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<a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true">
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<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" />
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</a>
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to start training your own image model
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</p>
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</div>
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''', elem_id="warning-duplicate")
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else:
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if(is_gpu_associated):
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top_description = gr.HTML(f'''
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<div class="gr-prose">
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<h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
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You have successfully associated a {which_gpu} GPU to the SD-XL Training Space 🎉</h2>
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<p>
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You can now train your model! You will be billed by the minute from when you activated the GPU until when it is turned off.
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</p>
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</div>
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''', elem_id="warning-ready")
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else:
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top_description = gr.HTML(f'''
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<div class="gr-prose">
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<h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
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You have successfully duplicated the SD-XL Training Space 🎉</h2>
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<p>There's only one step left before you can train your model: <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings" style="text-decoration: underline" target="_blank">attribute a <b>T4-small or A10G-small GPU</b> to it (via the Settings tab)</a> and run the training below.
|
322 |
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You will be billed by the minute from when you activate the GPU until when it is turned off.</p>
|
323 |
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<p class="actions">
|
324 |
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<a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings">🔥 Set recommended GPU</a>
|
325 |
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</p>
|
326 |
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</div>
|
327 |
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''', elem_id="warning-setgpu")
|
328 |
-
|
329 |
-
gr.Markdown("# SD-XL Dreambooth LoRa Training UI 💭")
|
330 |
-
|
331 |
-
upload_my_images = gr.Checkbox(label="Drop your training images ? (optional)", value=False)
|
332 |
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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.")
|
333 |
-
|
334 |
-
with gr.Group(visible=False, elem_id="upl-dataset-group") as upload_group:
|
335 |
-
with gr.Row():
|
336 |
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images = gr.File(file_types=["image"], label="Upload your images", file_count="multiple", interactive=True, visible=True)
|
337 |
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with gr.Column():
|
338 |
-
new_dataset_name = gr.Textbox(label="Set new dataset name", placeholder="e.g.: my_awesome_dataset")
|
339 |
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dataset_status = gr.Textbox(label="dataset status")
|
340 |
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load_btn = gr.Button("Load images to new dataset", elem_id="load-dataset-btn")
|
341 |
-
|
342 |
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gr.Markdown("## Training ")
|
343 |
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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) ;)")
|
344 |
-
|
345 |
-
with gr.Row():
|
346 |
-
dataset_id = gr.Textbox(label="Dataset ID", info="use one of your previously uploaded image datasets on your HF profile", placeholder="diffusers/dog-example")
|
347 |
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instance_prompt = gr.Textbox(label="Concept prompt", info="concept prompt - use a unique, made up word to avoid collisions")
|
348 |
-
|
349 |
-
with gr.Row():
|
350 |
-
model_output_folder = gr.Textbox(label="Output model folder name", placeholder="lora-trained-xl-folder")
|
351 |
-
max_train_steps = gr.Number(label="Max Training Steps", value=500, precision=0, step=10)
|
352 |
-
checkpoint_steps = gr.Number(label="Checkpoints Steps", value=100, precision=0, step=10)
|
353 |
-
|
354 |
-
remove_gpu = gr.Checkbox(label="Remove GPU After Training", value=True)
|
355 |
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train_button = gr.Button("Train !")
|
356 |
-
|
357 |
-
train_status = gr.Textbox(label="Training status")
|
358 |
-
|
359 |
-
upload_my_images.change(
|
360 |
-
fn = check_upload_or_no,
|
361 |
-
inputs =[upload_my_images],
|
362 |
-
outputs = [upload_group]
|
363 |
-
)
|
364 |
-
|
365 |
-
load_btn.click(
|
366 |
-
fn = load_images_to_dataset,
|
367 |
-
inputs = [images, new_dataset_name],
|
368 |
-
outputs = [dataset_status, dataset_id]
|
369 |
-
)
|
370 |
-
|
371 |
-
train_button.click(
|
372 |
-
fn = main,
|
373 |
-
inputs = [
|
374 |
-
dataset_id,
|
375 |
-
model_output_folder,
|
376 |
-
instance_prompt,
|
377 |
-
max_train_steps,
|
378 |
-
checkpoint_steps,
|
379 |
-
remove_gpu
|
380 |
-
],
|
381 |
-
outputs = [train_status]
|
382 |
-
)
|
383 |
-
|
384 |
-
demo.queue(default_enabled=False).launch(debug=True)
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