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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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|
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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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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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for idx, image in enumerate(images): |
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image_name = os.path.basename(image.name) |
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destination_path = os.path.join(my_working_directory, image_name) |
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shutil.copy(image.name, destination_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" |
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command = [ |
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"accelerate", |
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"launch", |
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script_filename, |
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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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else: |
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swap_sleep_time(hf_token, 300) |
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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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else: |
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swap_sleep_time(hf_token, 300) |
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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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repo_parts = dataset_repo.split("/") |
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local_dir = f"./{repo_parts[-1]}" |
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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. |
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You will be billed by the minute from when you activate the GPU until when it is turned off.</p> |
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<p class="actions"> |
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<a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings">🔥 Set recommended GPU</a> |
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</p> |
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</div> |
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''', elem_id="warning-setgpu") |
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|
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gr.Markdown("# SD-XL Dreambooth LoRa Training UI 💭") |
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|
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upload_my_images = gr.Checkbox(label="Drop your training images ? (optional)", value=False) |
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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.") |
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|
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with gr.Group(visible=False, elem_id="upl-dataset-group") as upload_group: |
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with gr.Row(): |
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images = gr.File(file_types=["image"], label="Upload your images", file_count="multiple", interactive=True, visible=True) |
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with gr.Column(): |
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new_dataset_name = gr.Textbox(label="Set new dataset name", placeholder="e.g.: my_awesome_dataset") |
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dataset_status = gr.Textbox(label="dataset status") |
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load_btn = gr.Button("Load images to new dataset", elem_id="load-dataset-btn") |
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|
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gr.Markdown("## Training ") |
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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) ;)") |
|
|
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with gr.Row(): |
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dataset_id = gr.Textbox(label="Dataset ID", info="use one of your previously uploaded image datasets on your HF profile", placeholder="diffusers/dog-example") |
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instance_prompt = gr.Textbox(label="Concept prompt", info="concept prompt - use a unique, made up word to avoid collisions") |
|
|
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with gr.Row(): |
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model_output_folder = gr.Textbox(label="Output model folder name", placeholder="lora-trained-xl-folder") |
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max_train_steps = gr.Number(label="Max Training Steps", value=500, precision=0, step=10) |
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checkpoint_steps = gr.Number(label="Checkpoints Steps", value=100, precision=0, step=10) |
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|
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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.") |
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train_button = gr.Button("Train !") |
|
|
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train_status = gr.Textbox(label="Training status") |
|
|
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upload_my_images.change( |
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fn = check_upload_or_no, |
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inputs =[upload_my_images], |
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outputs = [upload_group] |
|
) |
|
|
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load_btn.click( |
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fn = load_images_to_dataset, |
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inputs = [images, new_dataset_name], |
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outputs = [dataset_status, dataset_id] |
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) |
|
|
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train_button.click( |
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fn = main, |
|
inputs = [ |
|
dataset_id, |
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model_output_folder, |
|
instance_prompt, |
|
max_train_steps, |
|
checkpoint_steps, |
|
remove_gpu |
|
], |
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outputs = [train_status] |
|
) |
|
|
|
demo.launch(debug=True) |
