Spaces:
Runtime error
Runtime error
| # ported from https://github.com/huggingface/diffusers/tree/a6fb9407fd45e76ccd47d13f08f0dd835967d620/examples/research_projects/dreambooth_inpaint | |
| import argparse | |
| import hashlib | |
| import itertools | |
| import math | |
| import os | |
| import random | |
| from pathlib import Path | |
| import numpy as np | |
| 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 ProjectConfiguration, set_seed | |
| from huggingface_hub import create_repo, upload_folder | |
| from PIL import Image, ImageDraw | |
| from torch.utils.data import Dataset | |
| from torchvision import transforms | |
| from tqdm.auto import tqdm | |
| from transformers import CLIPTextModel, CLIPTokenizer | |
| from diffusers import ( | |
| AutoencoderKL, | |
| DDPMScheduler, | |
| StableDiffusionInpaintPipeline, | |
| StableDiffusionPipeline, | |
| UNet2DConditionModel, | |
| ) | |
| from diffusers.optimization import get_scheduler | |
| from diffusers.utils import check_min_version | |
| # 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 prepare_mask_and_masked_image(image, mask): | |
| image = np.array(image.convert("RGB")) | |
| image = image[None].transpose(0, 3, 1, 2) | |
| image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 | |
| mask = np.array(mask.convert("L")) | |
| mask = mask.astype(np.float32) / 255.0 | |
| mask = mask[None, None] | |
| mask[mask < 0.5] = 0 | |
| mask[mask >= 0.5] = 1 | |
| mask = torch.from_numpy(mask) | |
| masked_image = image * (mask < 0.5) | |
| return mask, masked_image | |
| # generate random masks | |
| def random_mask(im_shape, ratio=1, mask_full_image=False): | |
| mask = Image.new("L", im_shape, 0) | |
| draw = ImageDraw.Draw(mask) | |
| size = (random.randint(0, int(im_shape[0] * ratio)), random.randint(0, int(im_shape[1] * ratio))) | |
| # use this to always mask the whole image | |
| if mask_full_image: | |
| size = (int(im_shape[0] * ratio), int(im_shape[1] * ratio)) | |
| limits = (im_shape[0] - size[0] // 2, im_shape[1] - size[1] // 2) | |
| center = (random.randint(size[0] // 2, limits[0]), random.randint(size[1] // 2, limits[1])) | |
| draw_type = random.randint(0, 1) | |
| if draw_type == 0 or mask_full_image: | |
| draw.rectangle( | |
| (center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2), | |
| fill=255, | |
| ) | |
| else: | |
| draw.ellipse( | |
| (center[0] - size[0] // 2, center[1] - size[1] // 2, center[0] + size[0] // 2, center[1] + size[1] // 2), | |
| fill=255, | |
| ) | |
| return mask | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description="Simple example of a training script.") | |
| 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( | |
| "--tokenizer_name", | |
| type=str, | |
| default=None, | |
| help="Pretrained tokenizer name or path if not the same as model_name", | |
| ) | |
| parser.add_argument( | |
| "--instance_data_dir", | |
| type=str, | |
| default=None, | |
| required=True, | |
| help="A folder containing the training data of instance images.", | |
| ) | |
| parser.add_argument( | |
| "--class_data_dir", | |
| type=str, | |
| default=None, | |
| required=False, | |
| help="A folder containing the training data of class images.", | |
| ) | |
| parser.add_argument( | |
| "--instance_prompt", | |
| type=str, | |
| default=None, | |
| help="The prompt with identifier specifying the instance", | |
| ) | |
| parser.add_argument( | |
| "--class_prompt", | |
| type=str, | |
| default=None, | |
| help="The prompt to specify images in the same class as provided instance images.", | |
| ) | |
| parser.add_argument( | |
| "--with_prior_preservation", | |
| default=False, | |
| action="store_true", | |
| help="Flag to add prior preservation loss.", | |
| ) | |
| parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.") | |
| parser.add_argument( | |
| "--num_class_images", | |
| type=int, | |
| default=100, | |
| help=( | |
| "Minimal class images for prior preservation loss. If not have enough images, additional images will be" | |
| " sampled with class_prompt." | |
| ), | |
| ) | |
| 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", | |
| default=False, | |
| action="store_true", | |
| help=( | |
| "Whether to center crop the input images to the resolution. If not set, the images will be randomly" | |
| " cropped. The images will be resized to the resolution first before cropping." | |
| ), | |
| ) | |
| parser.add_argument("--train_text_encoder", action="store_true", help="Whether to train the text encoder") | |
| parser.add_argument( | |
| "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader." | |
| ) | |
| parser.add_argument( | |
| "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images." | |
| ) | |
| parser.add_argument("--num_train_epochs", type=int, default=1) | |
| parser.add_argument( | |
| "--max_train_steps", | |
| type=int, | |
| default=None, | |
| 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( | |
| "--gradient_checkpointing", | |
| action="store_true", | |
| help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", | |
| ) | |
| parser.add_argument( | |
| "--learning_rate", | |
| type=float, | |
| default=5e-6, | |
| help="Initial learning rate (after the potential warmup period) to use.", | |
| ) | |
| parser.add_argument( | |
| "--scale_lr", | |
| action="store_true", | |
| default=False, | |
| 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( | |
| "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." | |
| ) | |
| 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("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") | |
| 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") | |
| parser.add_argument( | |
| "--checkpointing_steps", | |
| type=int, | |
| default=500, | |
| help=( | |
| "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final" | |
| " checkpoints in case they are better than the last checkpoint and are suitable for resuming training" | |
| " using `--resume_from_checkpoint`." | |
| ), | |
| ) | |
| parser.add_argument( | |
| "--checkpoints_total_limit", | |
| type=int, | |
| default=None, | |
| help=( | |
| "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`." | |
| " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state" | |
| " for more docs" | |
| ), | |
| ) | |
| parser.add_argument( | |
| "--resume_from_checkpoint", | |
| type=str, | |
| default=None, | |
| help=( | |
| "Whether training should be resumed from a previous checkpoint. Use a path saved by" | |
| ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' | |
| ), | |
| ) | |
| 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.instance_data_dir is None: | |
| raise ValueError("You must specify a train data directory.") | |
| if args.with_prior_preservation: | |
| if args.class_data_dir is None: | |
| raise ValueError("You must specify a data directory for class images.") | |
| if args.class_prompt is None: | |
| raise ValueError("You must specify prompt for class images.") | |
| return args | |
| class DreamBoothDataset(Dataset): | |
| """ | |
| A dataset to prepare the instance and class images with the prompts for fine-tuning the model. | |
| It pre-processes the images and the tokenizes prompts. | |
| """ | |
| def __init__( | |
| self, | |
| instance_data_root, | |
| instance_prompt, | |
| tokenizer, | |
| class_data_root=None, | |
| class_prompt=None, | |
| size=512, | |
| center_crop=False, | |
| ): | |
| self.size = size | |
| self.center_crop = center_crop | |
| self.tokenizer = tokenizer | |
| self.instance_data_root = Path(instance_data_root) | |
| if not self.instance_data_root.exists(): | |
| raise ValueError("Instance images root doesn't exists.") | |
| self.instance_images_path = list(Path(instance_data_root).iterdir()) | |
| self.num_instance_images = len(self.instance_images_path) | |
| self.instance_prompt = instance_prompt | |
| self._length = self.num_instance_images | |
| if class_data_root is not None: | |
| self.class_data_root = Path(class_data_root) | |
| self.class_data_root.mkdir(parents=True, exist_ok=True) | |
| self.class_images_path = list(self.class_data_root.iterdir()) | |
| self.num_class_images = len(self.class_images_path) | |
| self._length = max(self.num_class_images, self.num_instance_images) | |
| self.class_prompt = class_prompt | |
| else: | |
| self.class_data_root = None | |
| self.image_transforms_resize_and_crop = transforms.Compose( | |
| [ | |
| transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), | |
| transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), | |
| ] | |
| ) | |
| self.image_transforms = transforms.Compose( | |
| [ | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5], [0.5]), | |
| ] | |
| ) | |
| def __len__(self): | |
| return self._length | |
| def __getitem__(self, index): | |
| example = {} | |
| instance_image = Image.open(self.instance_images_path[index % self.num_instance_images]) | |
| if not instance_image.mode == "RGB": | |
| instance_image = instance_image.convert("RGB") | |
| instance_image = self.image_transforms_resize_and_crop(instance_image) | |
| example["PIL_images"] = instance_image | |
| example["instance_images"] = self.image_transforms(instance_image) | |
| example["instance_prompt_ids"] = self.tokenizer( | |
| self.instance_prompt, | |
| padding="do_not_pad", | |
| truncation=True, | |
| max_length=self.tokenizer.model_max_length, | |
| ).input_ids | |
| if self.class_data_root: | |
| class_image = Image.open(self.class_images_path[index % self.num_class_images]) | |
| if not class_image.mode == "RGB": | |
| class_image = class_image.convert("RGB") | |
| class_image = self.image_transforms_resize_and_crop(class_image) | |
| example["class_images"] = self.image_transforms(class_image) | |
| example["class_PIL_images"] = class_image | |
| example["class_prompt_ids"] = self.tokenizer( | |
| self.class_prompt, | |
| padding="do_not_pad", | |
| truncation=True, | |
| max_length=self.tokenizer.model_max_length, | |
| ).input_ids | |
| return example | |
| class PromptDataset(Dataset): | |
| "A simple dataset to prepare the prompts to generate class images on multiple GPUs." | |
| def __init__(self, prompt, num_samples): | |
| self.prompt = prompt | |
| self.num_samples = num_samples | |
| def __len__(self): | |
| return self.num_samples | |
| def __getitem__(self, index): | |
| example = {} | |
| example["prompt"] = self.prompt | |
| example["index"] = index | |
| return example | |
| def main(): | |
| args = parse_args() | |
| logging_dir = Path(args.output_dir, args.logging_dir) | |
| project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit) | |
| accelerator = Accelerator( | |
| gradient_accumulation_steps=args.gradient_accumulation_steps, | |
| mixed_precision=args.mixed_precision, | |
| log_with="tensorboard", | |
| logging_dir=logging_dir, | |
| project_config=project_config, | |
| ) | |
| # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate | |
| # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models. | |
| # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate. | |
| if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: | |
| raise ValueError( | |
| "Gradient accumulation is not supported when training the text encoder in distributed training. " | |
| "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." | |
| ) | |
| if args.seed is not None: | |
| set_seed(args.seed) | |
| if args.with_prior_preservation: | |
| class_images_dir = Path(args.class_data_dir) | |
| if not class_images_dir.exists(): | |
| class_images_dir.mkdir(parents=True) | |
| cur_class_images = len(list(class_images_dir.iterdir())) | |
| if cur_class_images < args.num_class_images: | |
| torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32 | |
| pipeline = StableDiffusionInpaintPipeline.from_pretrained( | |
| args.pretrained_model_name_or_path, torch_dtype=torch_dtype, safety_checker=None | |
| ) | |
| pipeline.set_progress_bar_config(disable=True) | |
| num_new_images = args.num_class_images - cur_class_images | |
| logger.info(f"Number of class images to sample: {num_new_images}.") | |
| sample_dataset = PromptDataset(args.class_prompt, num_new_images) | |
| sample_dataloader = torch.utils.data.DataLoader( | |
| sample_dataset, batch_size=args.sample_batch_size, num_workers=1 | |
| ) | |
| sample_dataloader = accelerator.prepare(sample_dataloader) | |
| pipeline.to(accelerator.device) | |
| transform_to_pil = transforms.ToPILImage() | |
| for example in tqdm( | |
| sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process | |
| ): | |
| bsz = len(example["prompt"]) | |
| fake_images = torch.rand((3, args.resolution, args.resolution)) | |
| transform_to_pil = transforms.ToPILImage() | |
| fake_pil_images = transform_to_pil(fake_images) | |
| fake_mask = random_mask((args.resolution, args.resolution), ratio=1, mask_full_image=True) | |
| images = pipeline(prompt=example["prompt"], mask_image=fake_mask, image=fake_pil_images).images | |
| for i, image in enumerate(images): | |
| hash_image = hashlib.sha1(image.tobytes()).hexdigest() | |
| image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg" | |
| image.save(image_filename) | |
| del pipeline | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| # 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 | |
| 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") | |
| # Load models and create wrapper for stable diffusion | |
| text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder") | |
| vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae") | |
| unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet") | |
| vae.requires_grad_(False) | |
| if not args.train_text_encoder: | |
| text_encoder.requires_grad_(False) | |
| if args.gradient_checkpointing: | |
| unet.enable_gradient_checkpointing() | |
| if args.train_text_encoder: | |
| text_encoder.gradient_checkpointing_enable() | |
| if args.scale_lr: | |
| args.learning_rate = ( | |
| args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes | |
| ) | |
| # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs | |
| if args.use_8bit_adam: | |
| try: | |
| import bitsandbytes as bnb | |
| except ImportError: | |
| raise ImportError( | |
| "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`." | |
| ) | |
| optimizer_class = bnb.optim.AdamW8bit | |
| else: | |
| optimizer_class = torch.optim.AdamW | |
| params_to_optimize = ( | |
| itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters() | |
| ) | |
| optimizer = optimizer_class( | |
| params_to_optimize, | |
| 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 = DreamBoothDataset( | |
| instance_data_root=args.instance_data_dir, | |
| instance_prompt=args.instance_prompt, | |
| class_data_root=args.class_data_dir if args.with_prior_preservation else None, | |
| class_prompt=args.class_prompt, | |
| tokenizer=tokenizer, | |
| size=args.resolution, | |
| center_crop=args.center_crop, | |
| ) | |
| def collate_fn(examples): | |
| input_ids = [example["instance_prompt_ids"] for example in examples] | |
| pixel_values = [example["instance_images"] for example in examples] | |
| # Concat class and instance examples for prior preservation. | |
| # We do this to avoid doing two forward passes. | |
| if args.with_prior_preservation: | |
| input_ids += [example["class_prompt_ids"] for example in examples] | |
| pixel_values += [example["class_images"] for example in examples] | |
| pior_pil = [example["class_PIL_images"] for example in examples] | |
| masks = [] | |
| masked_images = [] | |
| for example in examples: | |
| pil_image = example["PIL_images"] | |
| # generate a random mask | |
| mask = random_mask(pil_image.size, 1, False) | |
| # prepare mask and masked image | |
| mask, masked_image = prepare_mask_and_masked_image(pil_image, mask) | |
| masks.append(mask) | |
| masked_images.append(masked_image) | |
| if args.with_prior_preservation: | |
| for pil_image in pior_pil: | |
| # generate a random mask | |
| mask = random_mask(pil_image.size, 1, False) | |
| # prepare mask and masked image | |
| mask, masked_image = prepare_mask_and_masked_image(pil_image, mask) | |
| masks.append(mask) | |
| masked_images.append(masked_image) | |
| pixel_values = torch.stack(pixel_values) | |
| pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() | |
| input_ids = tokenizer.pad({"input_ids": input_ids}, padding=True, return_tensors="pt").input_ids | |
| masks = torch.stack(masks) | |
| masked_images = torch.stack(masked_images) | |
| batch = {"input_ids": input_ids, "pixel_values": pixel_values, "masks": masks, "masked_images": masked_images} | |
| return batch | |
| train_dataloader = torch.utils.data.DataLoader( | |
| train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn | |
| ) | |
| # 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, | |
| ) | |
| if args.train_text_encoder: | |
| unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
| unet, text_encoder, optimizer, train_dataloader, lr_scheduler | |
| ) | |
| else: | |
| unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
| unet, optimizer, train_dataloader, lr_scheduler | |
| ) | |
| accelerator.register_for_checkpointing(lr_scheduler) | |
| weight_dtype = torch.float32 | |
| if args.mixed_precision == "fp16": | |
| weight_dtype = torch.float16 | |
| elif args.mixed_precision == "bf16": | |
| weight_dtype = torch.bfloat16 | |
| # Move text_encode and vae to gpu. | |
| # For mixed precision training we cast the text_encoder and vae weights to half-precision | |
| # as these models are only used for inference, keeping weights in full precision is not required. | |
| vae.to(accelerator.device, dtype=weight_dtype) | |
| if not args.train_text_encoder: | |
| text_encoder.to(accelerator.device, dtype=weight_dtype) | |
| # 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("dreambooth", 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 batches each epoch = {len(train_dataloader)}") | |
| 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}") | |
| global_step = 0 | |
| first_epoch = 0 | |
| if args.resume_from_checkpoint: | |
| if args.resume_from_checkpoint != "latest": | |
| path = os.path.basename(args.resume_from_checkpoint) | |
| else: | |
| # Get the most recent checkpoint | |
| dirs = os.listdir(args.output_dir) | |
| dirs = [d for d in dirs if d.startswith("checkpoint")] | |
| dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) | |
| path = dirs[-1] if len(dirs) > 0 else None | |
| if path is None: | |
| accelerator.print( | |
| f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." | |
| ) | |
| args.resume_from_checkpoint = None | |
| else: | |
| accelerator.print(f"Resuming from checkpoint {path}") | |
| accelerator.load_state(os.path.join(args.output_dir, path)) | |
| global_step = int(path.split("-")[1]) | |
| resume_global_step = global_step * args.gradient_accumulation_steps | |
| first_epoch = global_step // num_update_steps_per_epoch | |
| resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps) | |
| # Only show the progress bar once on each machine. | |
| progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process) | |
| progress_bar.set_description("Steps") | |
| for epoch in range(first_epoch, args.num_train_epochs): | |
| unet.train() | |
| for step, batch in enumerate(train_dataloader): | |
| # Skip steps until we reach the resumed step | |
| if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: | |
| if step % args.gradient_accumulation_steps == 0: | |
| progress_bar.update(1) | |
| continue | |
| with accelerator.accumulate(unet): | |
| # Convert images to latent space | |
| latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample() | |
| latents = latents * vae.config.scaling_factor | |
| # Convert masked images to latent space | |
| masked_latents = vae.encode( | |
| batch["masked_images"].reshape(batch["pixel_values"].shape).to(dtype=weight_dtype) | |
| ).latent_dist.sample() | |
| masked_latents = masked_latents * vae.config.scaling_factor | |
| masks = batch["masks"] | |
| # resize the mask to latents shape as we concatenate the mask to the latents | |
| mask = torch.stack( | |
| [ | |
| torch.nn.functional.interpolate(mask, size=(args.resolution // 8, args.resolution // 8)) | |
| for mask in masks | |
| ] | |
| ) | |
| mask = mask.reshape(-1, 1, args.resolution // 8, args.resolution // 8) | |
| # Sample noise that we'll add to the latents | |
| noise = torch.randn_like(latents) | |
| 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) | |
| timesteps = timesteps.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) | |
| # concatenate the noised latents with the mask and the masked latents | |
| latent_model_input = torch.cat([noisy_latents, mask, masked_latents], dim=1) | |
| # Get the text embedding for conditioning | |
| encoder_hidden_states = text_encoder(batch["input_ids"])[0] | |
| # Predict the noise residual | |
| noise_pred = unet(latent_model_input, 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}") | |
| if args.with_prior_preservation: | |
| # Chunk the noise and noise_pred into two parts and compute the loss on each part separately. | |
| noise_pred, noise_pred_prior = torch.chunk(noise_pred, 2, dim=0) | |
| target, target_prior = torch.chunk(target, 2, dim=0) | |
| # Compute instance loss | |
| loss = F.mse_loss(noise_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean() | |
| # Compute prior loss | |
| prior_loss = F.mse_loss(noise_pred_prior.float(), target_prior.float(), reduction="mean") | |
| # Add the prior loss to the instance loss. | |
| loss = loss + args.prior_loss_weight * prior_loss | |
| else: | |
| loss = F.mse_loss(noise_pred.float(), target.float(), reduction="mean") | |
| accelerator.backward(loss) | |
| if accelerator.sync_gradients: | |
| params_to_clip = ( | |
| itertools.chain(unet.parameters(), text_encoder.parameters()) | |
| if args.train_text_encoder | |
| else unet.parameters() | |
| ) | |
| accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) | |
| 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.checkpointing_steps == 0: | |
| if accelerator.is_main_process: | |
| save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") | |
| accelerator.save_state(save_path) | |
| logger.info(f"Saved state to {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: | |
| pipeline = StableDiffusionPipeline.from_pretrained( | |
| args.pretrained_model_name_or_path, | |
| unet=accelerator.unwrap_model(unet), | |
| text_encoder=accelerator.unwrap_model(text_encoder), | |
| ) | |
| pipeline.save_pretrained(args.output_dir) | |
| 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() |