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| import gradio as gr | |
| import torch | |
| import os | |
| import shutil | |
| import requests | |
| import subprocess | |
| from subprocess import getoutput | |
| from huggingface_hub import snapshot_download, HfApi, create_repo | |
| api = HfApi() | |
| hf_token = os.environ.get("HF_TOKEN_WITH_WRITE_PERMISSION") | |
| is_shared_ui = True if "fffiloni/train-dreambooth-lora-sdxl" 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 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", private=True, 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/{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(hf_token,sleep_time): | |
| sleep_time_url = f"https://huggingface.co/api/spaces/{os.environ['SPACE_ID']}/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/{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 write_to_community(title, description,hf_token): | |
| api.create_discussion(repo_id=os.environ['SPACE_ID'], 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; | |
| } | |
| """ | |
| 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><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"> | |
| 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"> | |
| <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 image model | |
| </p> | |
| </div> | |
| ''', elem_id="warning-duplicate") | |
| else: | |
| if(is_gpu_associated): | |
| top_description = gr.HTML(f''' | |
| <div class="gr-prose"> | |
| <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> | |
| You have successfully associated a {which_gpu} GPU to the SD-XL Training Space 🎉</h2> | |
| <p> | |
| 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><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 SD-XL Training Space 🎉</h2> | |
| <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. | |
| You will be billed by the minute from when you activate the GPU until when it is turned off.</p> | |
| <p class="actions"> | |
| <a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}/settings">🔥 Set recommended GPU</a> | |
| </p> | |
| </div> | |
| ''', 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] | |
| ) | |
| demo.launch(debug=True) |