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 = '' 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; } """ theme = gr.themes.Soft( primary_hue="teal", secondary_hue="gray", ).set( body_text_color_dark='*neutral_800', background_fill_primary_dark='*neutral_50', background_fill_secondary_dark='*neutral_50', border_color_accent_dark='*primary_300', border_color_primary_dark='*neutral_200', color_accent_soft_dark='*neutral_50', link_text_color_dark='*secondary_600', link_text_color_active_dark='*secondary_600', link_text_color_hover_dark='*secondary_700', link_text_color_visited_dark='*secondary_500', code_background_fill_dark='*neutral_100', shadow_spread_dark='6px', block_background_fill_dark='white', block_label_background_fill_dark='*primary_100', block_label_text_color_dark='*primary_500', block_title_text_color_dark='*primary_500', checkbox_background_color_dark='*background_fill_primary', checkbox_background_color_selected_dark='*primary_600', checkbox_border_color_dark='*neutral_100', checkbox_border_color_focus_dark='*primary_500', checkbox_border_color_hover_dark='*neutral_300', checkbox_border_color_selected_dark='*primary_600', checkbox_label_background_fill_selected_dark='*primary_500', checkbox_label_text_color_selected_dark='white', error_background_fill_dark='#fef2f2', error_border_color_dark='#b91c1c', error_text_color_dark='#b91c1c', error_icon_color_dark='#b91c1c', input_background_fill_dark='white', input_background_fill_focus_dark='*secondary_500', input_border_color_dark='*neutral_50', input_border_color_focus_dark='*secondary_300', input_placeholder_color_dark='*neutral_400', slider_color_dark='*primary_500', stat_background_fill_dark='*primary_300', table_border_color_dark='*neutral_300', table_even_background_fill_dark='white', table_odd_background_fill_dark='*neutral_50', button_primary_background_fill_dark='*primary_500', button_primary_background_fill_hover_dark='*primary_400', button_primary_border_color_dark='*primary_00', button_secondary_background_fill_dark='whiite', button_secondary_background_fill_hover_dark='*neutral_100', button_secondary_border_color_dark='*neutral_200', button_secondary_text_color_dark='*neutral_800' ) def create_training_demo() -> gr.Blocks: with gr.Blocks(theme=theme, 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.

to start training your own image model

''', elem_id="warning-duplicate") # else: # if(is_gpu_associated): # top_description = gr.HTML(f''' #
#

# You have successfully associated a {which_gpu} GPU to the SD-XL Training Space 🎉

#

# 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. #

#
# ''', elem_id="warning-ready") # else: # top_description = gr.HTML(f''' #
#

# You have successfully duplicated the SD-XL Training Space 🎉

#

There's only one step left before you can train your model: attribute a T4-small or A10G-small GPU to it (via the Settings tab) and run the training below. # You will be billed by the minute from when you activate the GPU until when it is turned off.

#

# 🔥   Set recommended GPU #

#
# ''', elem_id="warning-setgpu") gr.Markdown("# SD-XL Dreambooth LoRa Training UI 💭") 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)