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| import gradio as gr | |
| import torch | |
| import os | |
| import shutil | |
| import requests | |
| import subprocess | |
| from subprocess import getoutput | |
| import webbrowser | |
| from huggingface_hub import snapshot_download, HfApi | |
| api = HfApi() | |
| hf_token = os.environ.get("HF_TOKEN_WITH_WRITE_PERMISSION") | |
| is_shared_ui = True if "fffiloni/B-LoRa-trainer" in os.environ['SPACE_ID'] else False | |
| is_gpu_associated = torch.cuda.is_available() | |
| 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 change_training_setup(training_type): | |
| if training_type == "style" : | |
| return 1000, 500 | |
| elif training_type == "concept" : | |
| return 2000, 1000 | |
| def swap_hardware(hf_token, hardware="cpu-basic"): | |
| hardware_url = f"https://huggingface.co/spaces/{os.environ['SPACE_ID']}/hardware" | |
| headers = { "authorization" : f"Bearer {hf_token}"} | |
| body = {'flavor': hardware} | |
| requests.post(hardware_url, json = body, headers=headers) | |
| def swap_sleep_time(sleep_time): | |
| if sleep_time == "5 minutes": | |
| new_sleep_time = 300 | |
| elif sleep_time == "15 minutes": | |
| new_sleep_time = 900 | |
| elif sleep_time == "30 minutes": | |
| new_sleep_time = 1800 | |
| elif sleep_time == "1 hour": | |
| new_sleep_time = 3600 | |
| elif sleep_time == "10 hours": | |
| new_sleep_time = 36000 | |
| elif sleep_time == "24 hours": | |
| new_sleep_time = 86400 | |
| elif sleep_time == "48 hours": | |
| new_sleep_time = 172800 | |
| elif sleep_time == "72 hours": | |
| new_sleep_time = 259200 | |
| elif sleep_time == "1 week": | |
| new_sleep_time = 604800 | |
| elif sleep_time == "Don't sleep": | |
| new_sleep_time = -1 | |
| sleep_time_url = f"https://huggingface.co/api/spaces/{os.environ['SPACE_ID']}/sleeptime" | |
| headers = { "authorization" : f"Bearer {hf_token}"} | |
| body = {'seconds':new_sleep_time} | |
| requests.post(sleep_time_url,json=body,headers=headers) | |
| def get_sleep_time(): | |
| sleep_time_url = f"https://huggingface.co/api/spaces/{os.environ['SPACE_ID']}" | |
| 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 check_sleep_time(): | |
| sleep_time = get_sleep_time() | |
| if sleep_time is None : | |
| sleep_time_value = "Don't sleep" | |
| return sleep_time_value, gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| elif sleep_time >= 3600: | |
| if sleep_time == 3600: | |
| sleep_time_value = "1 hour" | |
| elif sleep_time == 36000: | |
| sleep_time_value = "10 hours" | |
| elif sleep_time == 86400: | |
| sleep_time_value = "24 hours" | |
| elif sleep_time == 172800: | |
| sleep_time_value = "48 hours" | |
| elif sleep_time == 259200: | |
| sleep_time_value = "72 hours" | |
| elif sleep_time == 604800: | |
| sleep_time_value = "1 week" | |
| return sleep_time_value, gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| else : | |
| if sleep_time == 300: | |
| sleep_time_value = "5 minutes" | |
| elif sleep_time == 900: | |
| sleep_time_value = "15 minutes" | |
| elif sleep_time == 1800: | |
| sleep_time_value = "30 minutes" | |
| return sleep_time_value, gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) | |
| def train_dreambooth_blora_sdxl(instance_data_dir, b_lora_trained_folder, instance_prompt, max_train_steps, checkpoint_steps): | |
| script_filename = "train_dreambooth_b-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", | |
| f"--instance_data_dir={instance_data_dir}", | |
| f"--output_dir={b_lora_trained_folder}", | |
| f"--instance_prompt='{instance_prompt}'", | |
| #f"--class_prompt={class_prompt}", | |
| f"--validation_prompt={instance_prompt} in {instance_prompt} style", | |
| "--num_validation_images=1", | |
| "--validation_epochs=500", | |
| "--resolution=1024", | |
| "--rank=64", | |
| "--train_batch_size=1", | |
| "--learning_rate=5e-5", | |
| "--lr_scheduler=constant", | |
| "--lr_warmup_steps=0", | |
| f"--max_train_steps={max_train_steps}", | |
| f"--checkpointing_steps={checkpoint_steps}", | |
| "--seed=0", | |
| "--gradient_checkpointing", | |
| "--use_8bit_adam", | |
| "--mixed_precision=fp16", | |
| "--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 clear_directory(directory_path): | |
| # Check if the directory exists | |
| if os.path.exists(directory_path): | |
| # Iterate over all the files and directories inside the specified directory | |
| for filename in os.listdir(directory_path): | |
| file_path = os.path.join(directory_path, filename) | |
| try: | |
| # Check if it is a file or a directory and remove accordingly | |
| if os.path.isfile(file_path) or os.path.islink(file_path): | |
| os.unlink(file_path) # Remove the file | |
| elif os.path.isdir(file_path): | |
| shutil.rmtree(file_path) # Remove the directory | |
| except Exception as e: | |
| print(f'Failed to delete {file_path}. Reason: {e}') | |
| else: | |
| print(f'The directory {directory_path} does not exist.') | |
| def get_start_info(image_path, b_lora_name, instance_prompt): | |
| 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 image_path == None: | |
| raise gr.Error("You forgot to specify an image reference") | |
| if b_lora_name == "": | |
| raise gr.Error("You forgot to specify a name for you model") | |
| if instance_prompt == "": | |
| raise gr.Error("You forgot to specify an instance prompt") | |
| your_username = api.whoami(token=hf_token)["name"] | |
| return gr.update(visible=True, value=f"https://hf.co/{your_username}/{b_lora_name}"), gr.update(visible=True) | |
| def main(started_info, image_path, b_lora_trained_folder, instance_prompt, training_type, training_steps): | |
| if started_info == None or started_info == "": | |
| raise gr.Error("Training did not start.") | |
| local_dir = "image_to_train" | |
| # Check if the directory exists and create it if necessary | |
| if not os.path.exists(local_dir): | |
| os.makedirs(local_dir) | |
| else : | |
| directory_to_clear = local_dir | |
| clear_directory(directory_to_clear) | |
| shutil.copy(image_path, local_dir) | |
| print(f"source image has been copied in {local_dir} directory") | |
| if training_type == "style": | |
| checkpoint_steps = 500 | |
| elif training_type == "concept" : | |
| checkpoint_steps = 1000 | |
| max_train_steps = training_steps | |
| train_dreambooth_blora_sdxl(local_dir, b_lora_trained_folder, instance_prompt, max_train_steps, checkpoint_steps) | |
| your_username = api.whoami(token=hf_token)["name"] | |
| #swap_hardware(hardware="cpu-basic") | |
| swap_sleep_time("5 minutes") | |
| return f"Done, your trained model has been stored in your models library: {your_username}/{b_lora_trained_folder}" | |
| css = """ | |
| #col-container {max-width: 780px; margin-left: auto; margin-right: auto;} | |
| div#warning-ready { | |
| background-color: #ecfdf5; | |
| padding: 0 16px 16px; | |
| margin: 20px 0; | |
| color: #030303!important; | |
| } | |
| div#warning-ready > .gr-prose > h2, div#warning-ready > .gr-prose > p { | |
| color: #057857!important; | |
| } | |
| div#warning-duplicate { | |
| background-color: #ebf5ff; | |
| padding: 0 16px 16px; | |
| margin: 20px 0; | |
| color: #030303!important; | |
| } | |
| 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 16px 16px; | |
| margin: 20px 0; | |
| color: #030303!important; | |
| } | |
| 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; | |
| } | |
| div#warning-setsleeptime { | |
| background-color: #fff4eb; | |
| padding: 10px 10px; | |
| margin: 0!important; | |
| color: #030303!important; | |
| } | |
| .custom-color { | |
| color: #030303 !important; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| if is_shared_ui: | |
| top_description = gr.HTML(f''' | |
| <div class="gr-prose"> | |
| <h2 class="custom-color"><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> | |
| Attention: this Space need to be duplicated to work</h2> | |
| <p class="main-message custom-color"> | |
| 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 /> | |
| A T4 costs <strong>US$0.60/h</strong>, so it should cost < US$1 to train most models. | |
| </p> | |
| <p class="actions custom-color"> | |
| <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true"> | |
| <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" /> | |
| </a> | |
| to start training your own B-LoRa model | |
| </p> | |
| </div> | |
| ''', elem_id="warning-duplicate") | |
| else: | |
| if(is_gpu_associated): | |
| top_description = gr.HTML(f''' | |
| <div class="gr-prose"> | |
| <h2 class="custom-color"><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> | |
| You have successfully associated a {which_gpu} GPU to the B-LoRa Training Space 🎉</h2> | |
| <p class="custom-color"> | |
| 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. | |
| </p> | |
| </div> | |
| ''', elem_id="warning-ready") | |
| else: | |
| top_description = gr.HTML(f''' | |
| <div class="gr-prose"> | |
| <h2 class="custom-color"><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> | |
| You have successfully duplicated the B-LoRa Training Space 🎉</h2> | |
| <p class="custom-color">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. | |
| You will be billed by the minute from when you activate the GPU until when it is turned off.</p> | |
| <p class="actions custom-color"> | |
| <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings">🔥 Set recommended GPU</a> | |
| </p> | |
| </div> | |
| ''', elem_id="warning-setgpu") | |
| gr.Markdown(""" | |
| # B-LoRa Training UI 💭 | |
| B-LoRa training method allows to perform high quality style-content mixing and even swapping the style and content between two stylized images, by implicitly decomposing a single image into its style and content representation. | |
| [Learn more about Implicit Style-Content Separation using B-LoRA](https://b-lora.github.io/B-LoRA/) | |
| """) | |
| with gr.Row(): | |
| image = gr.Image(label="Image Reference", sources=["upload"], type="filepath") | |
| with gr.Column(): | |
| sleep_time_message = gr.HTML(''' | |
| <div class="gr-prose"> | |
| <p>First of all, please make sure your space's sleep time value is set on long enough, so it do not fall asleep during training. </p> | |
| <p>Set it to <strong>"Don't sleep"</strong> or <strong>more than 1 hour</strong> to be safe.</p> | |
| <p>Don't worry, after training is finished, sleep time will be back to 5 minutes.</p> | |
| </div> | |
| ''', elem_id="warning-setsleeptime") | |
| with gr.Group(): | |
| current_sleep_time = gr.Dropdown( | |
| label="current space sleep time", | |
| choices = [ | |
| "Don't sleep", "5 minutes", "15 minutes", "30 minutes", "1 hour", "10 hours", "24 hours", "48 hours", "72 hours", "1 week" | |
| ], | |
| filterable=False | |
| ) | |
| training_type = gr.Radio(label="Training type", choices=["style", "concept"], value="style", visible=False) | |
| b_lora_name = gr.Textbox(label="Name your B-LoRa model", placeholder="b_lora_trained_folder", visible=False) | |
| with gr.Row(): | |
| instance_prompt = gr.Textbox(label="Create instance prompt", info="recommended standard B-LoRa is 'A [v]' format", placeholder="A [v42]", visible=False) | |
| #class_prompt = gr.Textbox(label="Specify class prompt", placeholder="style | person | dog ", visible=False) | |
| training_steps = gr.Number(label="Training steps", value=1000, interactive=False, visible=False) | |
| checkpoint_step = gr.Number(label="checkpoint step", visible=False, value=500) | |
| train_btn = gr.Button("Train B-LoRa", visible=False) | |
| with gr.Row(): | |
| started_info = gr.Textbox( | |
| label="Training has started", | |
| info="You can open this space's logs to monitor logs training; once training is finished, your model will be available here:", | |
| visible=False | |
| ) | |
| status = gr.Textbox(label="status", visible=False) | |
| current_sleep_time.change( | |
| fn = swap_sleep_time, | |
| inputs = current_sleep_time, | |
| outputs = None, | |
| show_api = False | |
| ) | |
| demo.load( | |
| fn = check_sleep_time, | |
| inputs = None, | |
| outputs = [current_sleep_time, sleep_time_message, b_lora_name, instance_prompt, training_type, training_steps, train_btn], | |
| show_api = False | |
| ) | |
| training_type.change( | |
| fn = change_training_setup, | |
| inputs = [training_type], | |
| outputs = [training_steps, checkpoint_step], | |
| show_api = False | |
| ) | |
| train_btn.click( | |
| fn = get_start_info, | |
| inputs = [image, b_lora_name, instance_prompt], | |
| outputs = [started_info, status], | |
| show_api = False | |
| ).then( | |
| fn = main, | |
| inputs = [started_info, image, b_lora_name, instance_prompt, training_type, training_steps], | |
| outputs = [status], | |
| show_api = False | |
| ) | |
| demo.launch(show_api=False, debug=True, show_error=True) |