import argparse import itertools import math import os import random from pathlib import Path import intel_extension_for_pytorch as ipex import numpy as np import PIL import torch import torch.nn.functional as F import torch.utils.checkpoint from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import create_repo, upload_folder # TODO: remove and import from diffusers.utils when the new version of diffusers is released from packaging import version from PIL import Image from torch.utils.data import Dataset from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel from diffusers.optimization import get_scheduler from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker from diffusers.utils import check_min_version if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"): PIL_INTERPOLATION = { "linear": PIL.Image.Resampling.BILINEAR, "bilinear": PIL.Image.Resampling.BILINEAR, "bicubic": PIL.Image.Resampling.BICUBIC, "lanczos": PIL.Image.Resampling.LANCZOS, "nearest": PIL.Image.Resampling.NEAREST, } else: PIL_INTERPOLATION = { "linear": PIL.Image.LINEAR, "bilinear": PIL.Image.BILINEAR, "bicubic": PIL.Image.BICUBIC, "lanczos": PIL.Image.LANCZOS, "nearest": PIL.Image.NEAREST, } # ------------------------------------------------------------------------------ # Will error if the minimal version of diffusers is not installed. Remove at your own risks. check_min_version("0.13.0.dev0") logger = get_logger(__name__) def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path): logger.info("Saving embeddings") learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id] learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()} torch.save(learned_embeds_dict, save_path) def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--save_steps", type=int, default=500, help="Save learned_embeds.bin every X updates steps.", ) parser.add_argument( "--only_save_embeds", action="store_true", default=False, help="Save only the embeddings for the new concept.", ) parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data." ) parser.add_argument( "--placeholder_token", type=str, default=None, required=True, help="A token to use as a placeholder for the concept.", ) parser.add_argument( "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word." ) parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'") parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.") parser.add_argument( "--output_dir", type=str, default="text-inversion-model", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--resolution", type=int, default=512, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution." ) parser.add_argument( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) parser.add_argument("--num_train_epochs", type=int, default=100) parser.add_argument( "--max_train_steps", type=int, default=5000, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=True, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." ), ) parser.add_argument( "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." "and an Nvidia Ampere GPU." ), ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank if args.train_data_dir is None: raise ValueError("You must specify a train data directory.") return args imagenet_templates_small = [ "a photo of a {}", "a rendering of a {}", "a cropped photo of the {}", "the photo of a {}", "a photo of a clean {}", "a photo of a dirty {}", "a dark photo of the {}", "a photo of my {}", "a photo of the cool {}", "a close-up photo of a {}", "a bright photo of the {}", "a cropped photo of a {}", "a photo of the {}", "a good photo of the {}", "a photo of one {}", "a close-up photo of the {}", "a rendition of the {}", "a photo of the clean {}", "a rendition of a {}", "a photo of a nice {}", "a good photo of a {}", "a photo of the nice {}", "a photo of the small {}", "a photo of the weird {}", "a photo of the large {}", "a photo of a cool {}", "a photo of a small {}", ] imagenet_style_templates_small = [ "a painting in the style of {}", "a rendering in the style of {}", "a cropped painting in the style of {}", "the painting in the style of {}", "a clean painting in the style of {}", "a dirty painting in the style of {}", "a dark painting in the style of {}", "a picture in the style of {}", "a cool painting in the style of {}", "a close-up painting in the style of {}", "a bright painting in the style of {}", "a cropped painting in the style of {}", "a good painting in the style of {}", "a close-up painting in the style of {}", "a rendition in the style of {}", "a nice painting in the style of {}", "a small painting in the style of {}", "a weird painting in the style of {}", "a large painting in the style of {}", ] class TextualInversionDataset(Dataset): def __init__( self, data_root, tokenizer, learnable_property="object", # [object, style] size=512, repeats=100, interpolation="bicubic", flip_p=0.5, set="train", placeholder_token="*", center_crop=False, ): self.data_root = data_root self.tokenizer = tokenizer self.learnable_property = learnable_property self.size = size self.placeholder_token = placeholder_token self.center_crop = center_crop self.flip_p = flip_p self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)] self.num_images = len(self.image_paths) self._length = self.num_images if set == "train": self._length = self.num_images * repeats self.interpolation = { "linear": PIL_INTERPOLATION["linear"], "bilinear": PIL_INTERPOLATION["bilinear"], "bicubic": PIL_INTERPOLATION["bicubic"], "lanczos": PIL_INTERPOLATION["lanczos"], }[interpolation] self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p) def __len__(self): return self._length def __getitem__(self, i): example = {} image = Image.open(self.image_paths[i % self.num_images]) if not image.mode == "RGB": image = image.convert("RGB") placeholder_string = self.placeholder_token text = random.choice(self.templates).format(placeholder_string) example["input_ids"] = self.tokenizer( text, padding="max_length", truncation=True, max_length=self.tokenizer.model_max_length, return_tensors="pt", ).input_ids[0] # default to score-sde preprocessing img = np.array(image).astype(np.uint8) if self.center_crop: crop = min(img.shape[0], img.shape[1]) ( h, w, ) = ( img.shape[0], img.shape[1], ) img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2] image = Image.fromarray(img) image = image.resize((self.size, self.size), resample=self.interpolation) image = self.flip_transform(image) image = np.array(image).astype(np.uint8) image = (image / 127.5 - 1.0).astype(np.float32) example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1) return example def freeze_params(params): for param in params: param.requires_grad = False def main(): args = parse_args() logging_dir = os.path.join(args.output_dir, args.logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with="tensorboard", logging_dir=logging_dir, ) # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token ).repo_id # Load the tokenizer and add the placeholder token as a additional special token if args.tokenizer_name: tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) elif args.pretrained_model_name_or_path: tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer") # Add the placeholder token in tokenizer num_added_tokens = tokenizer.add_tokens(args.placeholder_token) if num_added_tokens == 0: raise ValueError( f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different" " `placeholder_token` that is not already in the tokenizer." ) # Convert the initializer_token, placeholder_token to ids token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False) # Check if initializer_token is a single token or a sequence of tokens if len(token_ids) > 1: raise ValueError("The initializer token must be a single token.") initializer_token_id = token_ids[0] placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) # Load models and create wrapper for stable diffusion text_encoder = CLIPTextModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, ) vae = AutoencoderKL.from_pretrained( args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, ) unet = UNet2DConditionModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, ) # Resize the token embeddings as we are adding new special tokens to the tokenizer text_encoder.resize_token_embeddings(len(tokenizer)) # Initialise the newly added placeholder token with the embeddings of the initializer token token_embeds = text_encoder.get_input_embeddings().weight.data token_embeds[placeholder_token_id] = token_embeds[initializer_token_id] # Freeze vae and unet freeze_params(vae.parameters()) freeze_params(unet.parameters()) # Freeze all parameters except for the token embeddings in text encoder params_to_freeze = itertools.chain( text_encoder.text_model.encoder.parameters(), text_encoder.text_model.final_layer_norm.parameters(), text_encoder.text_model.embeddings.position_embedding.parameters(), ) freeze_params(params_to_freeze) if args.scale_lr: args.learning_rate = ( args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Initialize the optimizer optimizer = torch.optim.AdamW( text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") train_dataset = TextualInversionDataset( data_root=args.train_data_dir, tokenizer=tokenizer, size=args.resolution, placeholder_token=args.placeholder_token, repeats=args.repeats, learnable_property=args.learnable_property, center_crop=args.center_crop, set="train", ) train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( text_encoder, optimizer, train_dataloader, lr_scheduler ) # Move vae and unet to device vae.to(accelerator.device) unet.to(accelerator.device) # Keep vae and unet in eval model as we don't train these vae.eval() unet.eval() unet = ipex.optimize(unet, dtype=torch.bfloat16, inplace=True) vae = ipex.optimize(vae, dtype=torch.bfloat16, inplace=True) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: accelerator.init_trackers("textual_inversion", config=vars(args)) # Train! total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) progress_bar.set_description("Steps") global_step = 0 text_encoder.train() text_encoder, optimizer = ipex.optimize(text_encoder, optimizer=optimizer, dtype=torch.bfloat16) for epoch in range(args.num_train_epochs): for step, batch in enumerate(train_dataloader): with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16): with accelerator.accumulate(text_encoder): # Convert images to latent space latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach() latents = latents * vae.config.scaling_factor # Sample noise that we'll add to the latents noise = torch.randn(latents.shape).to(latents.device) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint( 0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device ).long() # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # Get the text embedding for conditioning encoder_hidden_states = text_encoder(batch["input_ids"])[0] # Predict the noise residual model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") loss = F.mse_loss(model_pred, target, reduction="none").mean([1, 2, 3]).mean() accelerator.backward(loss) # Zero out the gradients for all token embeddings except the newly added # embeddings for the concept, as we only want to optimize the concept embeddings if accelerator.num_processes > 1: grads = text_encoder.module.get_input_embeddings().weight.grad else: grads = text_encoder.get_input_embeddings().weight.grad # Get the index for tokens that we want to zero the grads for index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) global_step += 1 if global_step % args.save_steps == 0: save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin") save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path) logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) accelerator.log(logs, step=global_step) if global_step >= args.max_train_steps: break accelerator.wait_for_everyone() # Create the pipeline using using the trained modules and save it. if accelerator.is_main_process: if args.push_to_hub and args.only_save_embeds: logger.warn("Enabling full model saving because --push_to_hub=True was specified.") save_full_model = True else: save_full_model = not args.only_save_embeds if save_full_model: pipeline = StableDiffusionPipeline( text_encoder=accelerator.unwrap_model(text_encoder), vae=vae, unet=unet, tokenizer=tokenizer, scheduler=PNDMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler"), safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker"), feature_extractor=CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32"), ) pipeline.save_pretrained(args.output_dir) # Save the newly trained embeddings save_path = os.path.join(args.output_dir, "learned_embeds.bin") save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path) if args.push_to_hub: upload_folder( repo_id=repo_id, folder_path=args.output_dir, commit_message="End of training", ignore_patterns=["step_*", "epoch_*"], ) accelerator.end_training() if __name__ == "__main__": main()