import gradio as gr from PIL import Image import requests import subprocess from transformers import Blip2Processor, Blip2ForConditionalGeneration from huggingface_hub import snapshot_download, HfApi import torch import uuid import os import shutil import json import random from slugify import slugify import argparse import importlib import sys from pathlib import Path import spaces import zipfile MAX_IMAGES = 100 training_script_url = "https://raw.githubusercontent.com/huggingface/diffusers/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py" subprocess.run(['wget', '-N', training_script_url]) orchestrator_script_url = "https://huggingface.co/datasets/multimodalart/lora-ease-helper/raw/main/script.py" subprocess.run(['wget', '-N', orchestrator_script_url]) device = "cuda" if torch.cuda.is_available() else "cpu" FACES_DATASET_PATH = snapshot_download(repo_id="multimodalart/faces-prior-preservation", repo_type="dataset") #Delete .gitattributes to process things properly Path(FACES_DATASET_PATH, '.gitattributes').unlink(missing_ok=True) processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained( "Salesforce/blip2-opt-2.7b", device_map={"": 0}, torch_dtype=torch.float16 ) training_option_settings = { "face": { "rank": 32, "lr_scheduler": "constant", "with_prior_preservation": True, "class_prompt": "a photo of a person", "train_steps_multiplier": 75, "file_count": 150, "dataset_path": FACES_DATASET_PATH }, "style": { "rank": 32, "lr_scheduler": "constant", "with_prior_preservation": False, "class_prompt": "", "train_steps_multiplier": 75 }, "character": { "rank": 32, "lr_scheduler": "constant", "with_prior_preservation": False, "class_prompt": "", "train_steps_multiplier": 180 }, "object": { "rank": 16, "lr_scheduler": "constant", "with_prior_preservation": False, "class_prompt": "", "train_steps_multiplier": 50 }, "custom": { "rank": 32, "lr_scheduler": "constant", "with_prior_preservation": False, "class_prompt": "", "train_steps_multiplier": 150 } } num_images_settings = { #>24 images, 1 repeat; 10 MAX_IMAGES: raise gr.Error( f"Error: for now, only {MAX_IMAGES} or less images are allowed for training" ) # Update for the captioning_area for _ in range(3): updates.append(gr.update(visible=True)) # Update visibility and image for each captioning row and image for i in range(1, MAX_IMAGES + 1): # Determine if the current row and image should be visible visible = i <= len(uploaded_images) # Update visibility of the captioning row updates.append(gr.update(visible=visible)) # Update for image component - display image if available, otherwise hide image_value = uploaded_images[i - 1] if visible else None updates.append(gr.update(value=image_value, visible=visible)) text_value = option if visible else None updates.append(gr.update(value=text_value, visible=visible)) return updates def check_removed_and_restart(images): visible = bool(images) return [gr.update(visible=visible) for _ in range(3)] def make_options_visible(option): if (option == "object") or (option == "face"): sentence = "A photo of TOK" elif option == "style": sentence = "in the style of TOK" elif option == "character": sentence = "A TOK character" elif option == "custom": sentence = "TOK" return ( gr.update(value=sentence, visible=True), gr.update(visible=True), ) def change_defaults(option, images): settings = training_option_settings.get(option, training_option_settings["custom"]) num_images = len(images) # Calculate max_train_steps train_steps_multiplier = settings["train_steps_multiplier"] max_train_steps = max(num_images * train_steps_multiplier, num_images_settings["train_steps_min"]) max_train_steps = min(max_train_steps, num_images_settings["train_steps_max"]) # Determine repeats based on number of images repeats = next(repeats for num, repeats in num_images_settings["repeats"] if num_images > num) random_files = [] if settings["with_prior_preservation"]: directory = settings["dataset_path"] file_count = settings["file_count"] files = [os.path.join(directory, file) for file in os.listdir(directory) if os.path.isfile(os.path.join(directory, file))] random_files = random.sample(files, min(len(files), file_count)) return max_train_steps, repeats, settings["lr_scheduler"], settings["rank"], settings["with_prior_preservation"], settings["class_prompt"], random_files def create_dataset(*inputs): print("Creating dataset") images = inputs[0] destination_folder = str(uuid.uuid4()) if not os.path.exists(destination_folder): os.makedirs(destination_folder) jsonl_file_path = os.path.join(destination_folder, 'metadata.jsonl') with open(jsonl_file_path, 'a') as jsonl_file: for index, image in enumerate(images): new_image_path = shutil.copy(image, destination_folder) original_caption = inputs[index + 1] file_name = os.path.basename(new_image_path) data = {"file_name": file_name, "prompt": original_caption} jsonl_file.write(json.dumps(data) + "\n") return destination_folder def start_training( lora_name, training_option, concept_sentence, optimizer, use_snr_gamma, snr_gamma, mixed_precision, learning_rate, train_batch_size, max_train_steps, lora_rank, repeats, with_prior_preservation, class_prompt, class_images, num_class_images, train_text_encoder_ti, train_text_encoder_ti_frac, num_new_tokens_per_abstraction, train_text_encoder, train_text_encoder_frac, text_encoder_learning_rate, seed, resolution, num_train_epochs, checkpointing_steps, prior_loss_weight, gradient_accumulation_steps, gradient_checkpointing, enable_xformers_memory_efficient_attention, adam_beta1, adam_beta2, use_prodigy_beta3, prodigy_beta3, prodigy_decouple, adam_weight_decay, use_adam_weight_decay_text_encoder, adam_weight_decay_text_encoder, adam_epsilon, prodigy_use_bias_correction, prodigy_safeguard_warmup, max_grad_norm, scale_lr, lr_num_cycles, lr_scheduler, lr_power, lr_warmup_steps, dataloader_num_workers, local_rank, dataset_folder, token, progress = gr.Progress(track_tqdm=True) ): if not lora_name: raise gr.Error("You forgot to insert your LoRA name! This name has to be unique.") print("Started training") slugged_lora_name = slugify(lora_name) spacerunner_folder = str(uuid.uuid4()) commands = [ "pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0", "pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix", f"instance_prompt={concept_sentence}", f"dataset_name=./{dataset_folder}", "caption_column=prompt", f"output_dir={slugged_lora_name}", f"mixed_precision={mixed_precision}", f"resolution={int(resolution)}", f"train_batch_size={int(train_batch_size)}", f"repeats={int(repeats)}", f"gradient_accumulation_steps={int(gradient_accumulation_steps)}", f"learning_rate={learning_rate}", f"text_encoder_lr={text_encoder_learning_rate}", f"adam_beta1={adam_beta1}", f"adam_beta2={adam_beta2}", f"optimizer={'adamW' if optimizer == '8bitadam' else optimizer}", f"train_text_encoder_ti_frac={train_text_encoder_ti_frac}", f"lr_scheduler={lr_scheduler}", f"lr_warmup_steps={int(lr_warmup_steps)}", f"rank={int(lora_rank)}", f"max_train_steps={int(max_train_steps)}", f"checkpointing_steps={int(checkpointing_steps)}", f"seed={int(seed)}", f"prior_loss_weight={prior_loss_weight}", f"num_new_tokens_per_abstraction={int(num_new_tokens_per_abstraction)}", f"num_train_epochs={int(num_train_epochs)}", f"adam_weight_decay={adam_weight_decay}", f"adam_epsilon={adam_epsilon}", f"prodigy_decouple={prodigy_decouple}", f"prodigy_use_bias_correction={prodigy_use_bias_correction}", f"prodigy_safeguard_warmup={prodigy_safeguard_warmup}", f"max_grad_norm={max_grad_norm}", f"lr_num_cycles={int(lr_num_cycles)}", f"lr_power={lr_power}", f"dataloader_num_workers={int(dataloader_num_workers)}", f"local_rank={int(local_rank)}", "cache_latents", #"push_to_hub", ] # Adding optional flags if optimizer == "8bitadam": commands.append("use_8bit_adam") if gradient_checkpointing: commands.append("gradient_checkpointing") if train_text_encoder_ti: commands.append("train_text_encoder_ti") elif train_text_encoder: commands.append("train_text_encoder") commands.append(f"train_text_encoder_frac={train_text_encoder_frac}") if enable_xformers_memory_efficient_attention: commands.append("enable_xformers_memory_efficient_attention") if use_snr_gamma: commands.append(f"snr_gamma={snr_gamma}") if scale_lr: commands.append("scale_lr") if with_prior_preservation: commands.append("with_prior_preservation") commands.append(f"class_prompt={class_prompt}") commands.append(f"num_class_images={int(num_class_images)}") if class_images: class_folder = str(uuid.uuid4()) zip_path = os.path.join(spacerunner_folder, class_folder, "class_images.zip") if not os.path.exists(os.path.join(spacerunner_folder, class_folder)): os.makedirs(os.path.join(spacerunner_folder, class_folder)) with zipfile.ZipFile(zip_path, 'w') as zipf: for image in class_images: zipf.write(image, os.path.basename(image)) commands.append(f"class_data_dir={class_folder}") if use_prodigy_beta3: commands.append(f"prodigy_beta3={prodigy_beta3}") if use_adam_weight_decay_text_encoder: commands.append(f"adam_weight_decay_text_encoder={adam_weight_decay_text_encoder}") print(commands) # Joining the commands with ';' separator for spacerunner format spacerunner_args = ';'.join(commands) if not os.path.exists(spacerunner_folder): os.makedirs(spacerunner_folder) shutil.copy("train_dreambooth_lora_sdxl_advanced.py", f"{spacerunner_folder}/trainer.py") shutil.copy("script.py", f"{spacerunner_folder}/script.py") shutil.copytree(dataset_folder, f"{spacerunner_folder}/{dataset_folder}") requirements='''peft==0.7.1 -huggingface_hub torch git+https://github.com/huggingface/diffusers@518171600d3eb82fc4f4c84b81dd7564b02728dc transformers==4.36.2 accelerate==0.25.0 safetensors==0.4.1 prodigyopt==1.0 hf-transfer==0.1.4 git+https://github.com/huggingface/datasets.git@3f149204a2a5948287adcade5e90707aa5207a92 git+https://github.com/huggingface/huggingface_hub.git@8d052492fe0059c606c1a48d7a914b15b64a834d''' file_path = f'{spacerunner_folder}/requirements.txt' with open(file_path, 'w') as file: file.write(requirements) # The subprocess call for autotrain spacerunner api = HfApi(token=token) username = api.whoami()["name"] subprocess_command = ["autotrain", "spacerunner", "--project-name", slugged_lora_name, "--script-path", spacerunner_folder, "--username", username, "--token", token, "--backend", "spaces-a10gs", "--env",f"HF_TOKEN={token};HF_HUB_ENABLE_HF_TRANSFER=1", "--args", spacerunner_args] outcome = subprocess.run(subprocess_command) if(outcome.returncode == 0): return f"""# Your training has started. ## - Training Status: {username}/autotrain-{slugged_lora_name} (in the logs tab) ## - Model page: {username}/{slugged_lora_name} (will be available when training finishes)""" else: raise gr.Error("Something went wrong. Make sure the name of your LoRA is unique and try again") def calculate_price(iterations, with_prior_preservation): if(with_prior_preservation): seconds_per_iteration = 3.50 else: seconds_per_iteration = 2.00 total_seconds = (iterations * seconds_per_iteration) + 210 cost_per_second = 1.05/60/60 cost = round(cost_per_second * total_seconds, 2) return f'''To train this LoRA, we will duplicate the space and hook an A10G GPU under the hood. ## Estimated to cost < US$ {str(cost)} for {round(int(total_seconds)/60, 2)} minutes with your current train settings ({int(iterations)} iterations at {seconds_per_iteration}s/it) #### ↓ to continue, grab you write token [here](https://huggingface.co/settings/tokens) and enter it below ↓''' def start_training_og( lora_name, training_option, concept_sentence, optimizer, use_snr_gamma, snr_gamma, mixed_precision, learning_rate, train_batch_size, max_train_steps, lora_rank, repeats, with_prior_preservation, class_prompt, class_images, num_class_images, train_text_encoder_ti, train_text_encoder_ti_frac, num_new_tokens_per_abstraction, train_text_encoder, train_text_encoder_frac, text_encoder_learning_rate, seed, resolution, num_train_epochs, checkpointing_steps, prior_loss_weight, gradient_accumulation_steps, gradient_checkpointing, enable_xformers_memory_efficient_attention, adam_beta1, adam_beta2, prodigy_beta3, prodigy_decouple, adam_weight_decay, adam_weight_decay_text_encoder, adam_epsilon, prodigy_use_bias_correction, prodigy_safeguard_warmup, max_grad_norm, scale_lr, lr_num_cycles, lr_scheduler, lr_power, lr_warmup_steps, dataloader_num_workers, local_rank, dataset_folder, progress = gr.Progress(track_tqdm=True) ): slugged_lora_name = slugify(lora_name) commands = ["--pretrained_model_name_or_path=stabilityai/stable-diffusion-xl-base-1.0", "--pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix", f"--instance_prompt={concept_sentence}", f"--dataset_name=./{dataset_folder}", "--caption_column=prompt", f"--output_dir={slugged_lora_name}", f"--mixed_precision={mixed_precision}", f"--resolution={int(resolution)}", f"--train_batch_size={int(train_batch_size)}", f"--repeats={int(repeats)}", f"--gradient_accumulation_steps={int(gradient_accumulation_steps)}", f"--learning_rate={learning_rate}", f"--text_encoder_lr={text_encoder_learning_rate}", f"--adam_beta1={adam_beta1}", f"--adam_beta2={adam_beta2}", f"--optimizer={'adamW' if optimizer == '8bitadam' else optimizer}", f"--train_text_encoder_ti_frac={train_text_encoder_ti_frac}", f"--lr_scheduler={lr_scheduler}", f"--lr_warmup_steps={int(lr_warmup_steps)}", f"--rank={int(lora_rank)}", f"--max_train_steps={int(max_train_steps)}", f"--checkpointing_steps={int(checkpointing_steps)}", f"--seed={int(seed)}", f"--prior_loss_weight={prior_loss_weight}", f"--num_new_tokens_per_abstraction={int(num_new_tokens_per_abstraction)}", f"--num_train_epochs={int(num_train_epochs)}", f"--prodigy_beta3={prodigy_beta3}", f"--adam_weight_decay={adam_weight_decay}", f"--adam_weight_decay_text_encoder={adam_weight_decay_text_encoder}", f"--adam_epsilon={adam_epsilon}", f"--prodigy_decouple={prodigy_decouple}", f"--prodigy_use_bias_correction={prodigy_use_bias_correction}", f"--prodigy_safeguard_warmup={prodigy_safeguard_warmup}", f"--max_grad_norm={max_grad_norm}", f"--lr_num_cycles={int(lr_num_cycles)}", f"--lr_power={lr_power}", f"--dataloader_num_workers={int(dataloader_num_workers)}", f"--local_rank={int(local_rank)}", "--cache_latents" ] if optimizer == "8bitadam": commands.append("--use_8bit_adam") if gradient_checkpointing: commands.append("--gradient_checkpointing") if train_text_encoder_ti: commands.append("--train_text_encoder_ti") elif train_text_encoder: commands.append("--train_text_encoder") commands.append(f"--train_text_encoder_frac={train_text_encoder_frac}") if enable_xformers_memory_efficient_attention: commands.append("--enable_xformers_memory_efficient_attention") if use_snr_gamma: commands.append(f"--snr_gamma={snr_gamma}") if scale_lr: commands.append("--scale_lr") if with_prior_preservation: commands.append(f"--with_prior_preservation") commands.append(f"--class_prompt={class_prompt}") commands.append(f"--num_class_images={int(num_class_images)}") if(class_images): class_folder = str(uuid.uuid4()) if not os.path.exists(class_folder): os.makedirs(class_folder) for image in class_images: shutil.copy(image, class_folder) commands.append(f"--class_data_dir={class_folder}") from train_dreambooth_lora_sdxl_advanced import main as train_main, parse_args as parse_train_args args = parse_train_args(commands) train_main(args) return "ok!" @spaces.GPU() def run_captioning(*inputs): model.to("cuda") images = inputs[0] training_option = inputs[-1] final_captions = [""] * MAX_IMAGES for index, image in enumerate(images): original_caption = inputs[index + 1] pil_image = Image.open(image) blip_inputs = processor(images=pil_image, return_tensors="pt").to(device, torch.float16) generated_ids = model.generate(**blip_inputs) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() if training_option == "style": final_caption = generated_text + " " + original_caption else: final_caption = original_caption + " " + generated_text final_captions[index] = final_caption yield final_captions def check_token(token): try: api = HfApi(token=token) user_data = api.whoami() except Exception as e: gr.Warning("Invalid user token. Make sure to get your Hugging Face token from the settings page") return gr.update(visible=False), gr.update(visible=False) else: if (user_data['auth']['accessToken']['role'] != "write"): gr.Warning("Ops, you've uploaded a Read token. You need to use a Write token!") else: if user_data['canPay']: return gr.update(visible=False), gr.update(visible=True) else: return gr.update(visible=True), gr.update(visible=False) return gr.update(visible=False), gr.update(visible=False) def check_if_tok(sentence, textual_inversion): if "TOK" not in sentence and textual_inversion: gr.Warning("⚠️ You've removed the special token TOK from your concept sentence. This will degrade performance as this special token is needed for textual inversion. Use TOK to describe what you are training.") css = '''.gr-group{background-color: transparent;box-shadow: var(--block-shadow)} .gr-group .hide-container{padding: 1em; background: var(--block-background-fill) !important} .gr-group img{object-fit: cover} #main_title{text-align:center} #main_title h1 {font-size: 2.25rem} #main_title h3, #main_title p{margin-top: 0;font-size: 1.25em} #training_cost h2{margin-top: 10px;padding: 0.5em;border: 1px solid var(--block-border-color);font-size: 1.25em} #training_cost h4{margin-top: 1.25em;margin-bottom: 0} #training_cost small{font-weight: normal} .accordion {color: var(--body-text-color)} .main_unlogged{opacity: 0.5;pointer-events: none} .login_logout{width: 100% !important} #login {font-size: 0px;width: 100% !important;margin: 0 auto} #login:after {content: 'Authorize this app to train your model';visibility: visible;display: block;font-size: var(--button-large-text-size)} ''' theme = gr.themes.Monochrome( text_size=gr.themes.Size(lg="18px", md="15px", sm="13px", xl="22px", xs="12px", xxl="24px", xxs="9px"), font=[gr.themes.GoogleFont('Source Sans Pro'), 'ui-sans-serif', 'system-ui', 'sans-serif'], ) #def swap_opacity(token: gr.OAuthToken | None): # if token is None: # return gr.update(elem_classes=["main_unlogged"], elem_id="login") # else: # return gr.update(elem_classes=["main_logged"]) with gr.Blocks(css=css, theme=theme) as demo: dataset_folder = gr.State() gr.Markdown('''# LoRA Ease 🧞‍♂️ ### Train a high quality SDXL LoRA in a breeze ༄ with state-of-the-art techniques Dreambooth with Pivotal Tuning, Prodigy and more! Use the trained LoRAs with diffusers, AUTO1111, Comfy. [blog about the training script](#), [Colab Pro](#), [run locally or in a cloud](#)''', elem_id="main_title") #gr.LoginButton(elem_classes=["login_logout"]) with gr.Column(elem_classes=["main_logged"]) as main_ui: lora_name = gr.Textbox(label="The name of your LoRA", info="This has to be a unique name", placeholder="e.g.: Persian Miniature Painting style, Cat Toy") training_option = gr.Radio( label="What are you training?", choices=["object", "style", "character", "face", "custom"] ) concept_sentence = gr.Textbox( label="Concept sentence", info="Sentence to be used in all images for captioning. TOK is a special mandatory token, used to teach the model your concept.", placeholder="e.g.: A photo of TOK, in the style of TOK", visible=False, interactive=True, ) with gr.Group(visible=False) as image_upload: with gr.Row(): images = gr.File( file_types=["image"], label="Upload your images", file_count="multiple", interactive=True, visible=True, scale=1, ) with gr.Column(scale=3, visible=False) as captioning_area: with gr.Column(): gr.Markdown( """# Custom captioning To improve the quality of your outputs, you can add a custom caption for each image, describing exactly what is taking place in each of them. Including TOK is mandatory. You can leave things as is if you don't want to include captioning. """ ) do_captioning = gr.Button("Add AI captions with BLIP-2") output_components = [captioning_area] caption_list = [] for i in range(1, MAX_IMAGES + 1): locals()[f"captioning_row_{i}"] = gr.Row(visible=False) with locals()[f"captioning_row_{i}"]: locals()[f"image_{i}"] = gr.Image( width=111, height=111, min_width=111, interactive=False, scale=2, show_label=False, show_share_button=False, show_download_button=False ) locals()[f"caption_{i}"] = gr.Textbox( label=f"Caption {i}", scale=15, interactive=True ) output_components.append(locals()[f"captioning_row_{i}"]) output_components.append(locals()[f"image_{i}"]) output_components.append(locals()[f"caption_{i}"]) caption_list.append(locals()[f"caption_{i}"]) with gr.Accordion(open=False, label="Advanced options", visible=False, elem_classes=['accordion']) as advanced: with gr.Row(): with gr.Column(): optimizer = gr.Dropdown( label="Optimizer", info="Prodigy is an auto-optimizer and works good by default. If you prefer to set your own learning rates, change it to AdamW. If you don't have enough VRAM to train with AdamW, pick 8-bit Adam.", choices=[ ("Prodigy", "prodigy"), ("AdamW", "adamW"), ("8-bit Adam", "8bitadam"), ], value="prodigy", interactive=True, ) use_snr_gamma = gr.Checkbox(label="Use SNR Gamma") snr_gamma = gr.Number( label="snr_gamma", info="SNR weighting gamma to re-balance the loss", value=5.000, step=0.1, visible=False, ) mixed_precision = gr.Dropdown( label="Mixed Precision", choices=["no", "fp16", "bf16"], value="bf16", ) learning_rate = gr.Number( label="UNet Learning rate", minimum=0.0, maximum=10.0, step=0.0000001, value=1.0, # For prodigy you start high and it will optimize down ) max_train_steps = gr.Number( label="Max train steps", minimum=1, maximum=50000, value=1000 ) lora_rank = gr.Number( label="LoRA Rank", info="Rank for the Low Rank Adaptation (LoRA), a higher rank produces a larger LoRA", value=8, step=2, minimum=2, maximum=1024, ) repeats = gr.Number( label="Repeats", info="How many times to repeat the training data.", value=1, minimum=1, maximum=200, ) with gr.Column(): with_prior_preservation = gr.Checkbox( label="Prior preservation loss", info="Prior preservation helps to ground the model to things that are similar to your concept. Good for faces.", value=False, ) with gr.Column(visible=False) as prior_preservation_params: with gr.Tab("prompt"): class_prompt = gr.Textbox( label="Class Prompt", info="The prompt that will be used to generate your class images", ) with gr.Tab("images"): class_images = gr.File( file_types=["image"], label="Upload your images", file_count="multiple", ) num_class_images = gr.Number( label="Number of class images, if there are less images uploaded then the number you put here, additional images will be sampled with Class Prompt", value=20, ) train_text_encoder_ti = gr.Checkbox( label="Do textual inversion", value=True, info="Will train a textual inversion embedding together with the LoRA. Increases quality significantly. If untoggled, you can remove the special TOK token from the prompts.", ) with gr.Group(visible=True) as pivotal_tuning_params: train_text_encoder_ti_frac = gr.Number( label="Pivot Textual Inversion", info="% of epochs to train textual inversion for", value=0.5, step=0.1, ) num_new_tokens_per_abstraction = gr.Number( label="Tokens to train", info="Number of tokens to train in the textual inversion", value=2, minimum=1, maximum=1024, interactive=True, ) with gr.Group(visible=False) as text_encoder_train_params: train_text_encoder = gr.Checkbox( label="Train Text Encoder", value=True ) train_text_encoder_frac = gr.Number( label="Pivot Text Encoder", info="% of epochs to train the text encoder for", value=0.8, step=0.1, ) text_encoder_learning_rate = gr.Number( label="Text encoder learning rate", minimum=0.0, maximum=10.0, step=0.0000001, value=1.0, ) seed = gr.Number(label="Seed", value=42) resolution = gr.Number( label="Resolution", info="Only square sizes are supported for now, the value will be width and height", value=1024, ) with gr.Accordion(open=False, label="Even more advanced options", elem_classes=['accordion']): with gr.Row(): with gr.Column(): gradient_accumulation_steps = gr.Number( info="If you change this setting, the pricing calculation will be wrong", label="gradient_accumulation_steps", value=1 ) train_batch_size = gr.Number( info="If you change this setting, the pricing calculation will be wrong", label="Train batch size", value=2 ) num_train_epochs = gr.Number( info="If you change this setting, the pricing calculation will be wrong", label="num_train_epochs", value=1 ) checkpointing_steps = gr.Number( info="How many steps to save intermediate checkpoints", label="checkpointing_steps", value=5000 ) prior_loss_weight = gr.Number( label="prior_loss_weight", value=1 ) gradient_checkpointing = gr.Checkbox( label="gradient_checkpointing", info="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass", value=True, ) adam_beta1 = gr.Number( label="adam_beta1", value=0.9, minimum=0, maximum=1, step=0.01 ) adam_beta2 = gr.Number( label="adam_beta2", minimum=0, maximum=1, step=0.01, value=0.999 ) use_prodigy_beta3 = gr.Checkbox( label="Use Prodigy Beta 3?" ) prodigy_beta3 = gr.Number( label="Prodigy Beta 3", value=None, step=0.01, minimum=0, maximum=1, ) prodigy_decouple = gr.Checkbox( label="Prodigy Decouple", value=True ) adam_weight_decay = gr.Number( label="Adam Weight Decay", value=1e-04, step=0.00001, minimum=0, maximum=1, ) use_adam_weight_decay_text_encoder = gr.Checkbox( label="Use Adam Weight Decay Text Encoder" ) adam_weight_decay_text_encoder = gr.Number( label="Adam Weight Decay Text Encoder", value=None, step=0.00001, minimum=0, maximum=1, ) adam_epsilon = gr.Number( label="Adam Epsilon", value=1e-08, step=0.00000001, minimum=0, maximum=1, ) prodigy_use_bias_correction = gr.Checkbox( label="Prodigy Use Bias Correction", value=True ) prodigy_safeguard_warmup = gr.Checkbox( label="Prodigy Safeguard Warmup", value=True ) max_grad_norm = gr.Number( label="Max Grad Norm", value=1.0, minimum=0.1, maximum=10, step=0.1, ) enable_xformers_memory_efficient_attention = gr.Checkbox( label="enable_xformers_memory_efficient_attention" ) with gr.Column(): scale_lr = gr.Checkbox( label="Scale learning rate", info="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size", ) lr_num_cycles = gr.Number( label="lr_num_cycles", value=1 ) lr_scheduler = gr.Dropdown( label="lr_scheduler", choices=[ "linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup", ], value="constant", ) lr_power = gr.Number( label="lr_power", value=1.0, minimum=0.1, maximum=10 ) lr_warmup_steps = gr.Number( label="lr_warmup_steps", value=0 ) dataloader_num_workers = gr.Number( label="Dataloader num workers", value=0, minimum=0, maximum=64 ) local_rank = gr.Number( label="local_rank", value=-1 ) with gr.Column(visible=False) as cost_estimation: with gr.Group(elem_id="cost_box"): training_cost_estimate = gr.Markdown(elem_id="training_cost") token = gr.Textbox(label="Your Hugging Face write token", info="A Hugging Face write token you can obtain on the settings page", type="password", placeholder="hf_OhHiThIsIsNoTaReALToKeNGOoDTry") with gr.Group(visible=False) as no_payment_method: with gr.Row(): gr.HTML("

Your Hugging Face account doesn't have a payment method set up. Set one up here and come back here to train your LoRA

") payment_setup = gr.Button("I have set up a payment method") start = gr.Button("Start training", visible=False, interactive=True) progress_area = gr.Markdown("") #gr.LogoutButton(elem_classes=["login_logout"]) output_components.insert(1, advanced) output_components.insert(1, cost_estimation) gr.on( triggers=[ token.change, payment_setup.click ], fn=check_token, inputs=token, outputs=[no_payment_method, start], concurrency_limit=50, ) concept_sentence.change( check_if_tok, inputs=[concept_sentence, train_text_encoder_ti], concurrency_limit=50, ) use_snr_gamma.change( lambda x: gr.update(visible=x), inputs=use_snr_gamma, outputs=snr_gamma, queue=False, ) with_prior_preservation.change( lambda x: gr.update(visible=x), inputs=with_prior_preservation, outputs=prior_preservation_params, queue=False, ) train_text_encoder_ti.change( lambda x: gr.update(visible=x), inputs=train_text_encoder_ti, outputs=pivotal_tuning_params, queue=False, ).then( lambda x: gr.update(visible=(not x)), inputs=train_text_encoder_ti, outputs=text_encoder_train_params, queue=False, ).then( lambda x: gr.Warning("As you have disabled Pivotal Tuning, you can remove TOK from your prompts and try to find a unique token for them") if not x else None, inputs=train_text_encoder_ti, concurrency_limit=50, ) train_text_encoder.change( lambda x: [gr.update(visible=x), gr.update(visible=x)], inputs=train_text_encoder, outputs=[train_text_encoder_frac, text_encoder_learning_rate], queue=False, ) class_images.change( lambda x: gr.update(value=len(x)), inputs=class_images, outputs=num_class_images, queue=False ) images.upload( load_captioning, inputs=[images, concept_sentence], outputs=output_components, queue=False ).then( change_defaults, inputs=[training_option, images], outputs=[max_train_steps, repeats, lr_scheduler, lora_rank, with_prior_preservation, class_prompt, class_images], queue=False ) images.change( check_removed_and_restart, inputs=[images], outputs=[captioning_area, advanced, cost_estimation], queue=False ) training_option.change( make_options_visible, inputs=training_option, outputs=[concept_sentence, image_upload], queue=False ) max_train_steps.change( calculate_price, inputs=[max_train_steps, with_prior_preservation], outputs=[training_cost_estimate], queue=False ) start.click( fn=create_dataset, inputs=[images] + caption_list, outputs=dataset_folder, queue=False ).then( fn=start_training, inputs=[ lora_name, training_option, concept_sentence, optimizer, use_snr_gamma, snr_gamma, mixed_precision, learning_rate, train_batch_size, max_train_steps, lora_rank, repeats, with_prior_preservation, class_prompt, class_images, num_class_images, train_text_encoder_ti, train_text_encoder_ti_frac, num_new_tokens_per_abstraction, train_text_encoder, train_text_encoder_frac, text_encoder_learning_rate, seed, resolution, num_train_epochs, checkpointing_steps, prior_loss_weight, gradient_accumulation_steps, gradient_checkpointing, enable_xformers_memory_efficient_attention, adam_beta1, adam_beta2, use_prodigy_beta3, prodigy_beta3, prodigy_decouple, adam_weight_decay, use_adam_weight_decay_text_encoder, adam_weight_decay_text_encoder, adam_epsilon, prodigy_use_bias_correction, prodigy_safeguard_warmup, max_grad_norm, scale_lr, lr_num_cycles, lr_scheduler, lr_power, lr_warmup_steps, dataloader_num_workers, local_rank, dataset_folder, token ], outputs = progress_area, queue=False ) do_captioning.click( fn=run_captioning, inputs=[images] + caption_list + [training_option], outputs=caption_list ) #demo.load(fn=swap_opacity, outputs=[main_ui], queue=False, concurrency_limit=50) if __name__ == "__main__": demo.queue() demo.launch(share=True)