# ------------------------------------------ # TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering # Paper Link: https://arxiv.org/abs/2311.16465 # Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser-2 # Copyright (c) Microsoft Corporation. # ------------------------------------------ import os import cv2 import math import random import logging import argparse import numpy as np from pathlib import Path from typing import Optional from packaging import version from collections import OrderedDict from PIL import Image, ImageDraw, ImageFont from huggingface_hub import HfFolder, Repository, create_repo, whoami import torch import torch.utils.checkpoint import torch.nn.functional as F from torchvision import transforms import datasets import transformers import accelerate from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer import diffusers from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel from diffusers.optimization import get_scheduler from diffusers.training_utils import EMAModel from diffusers.utils import check_min_version, deprecate from diffusers.utils.import_utils import is_xformers_available from termcolor import colored import string alphabet = string.digits + string.ascii_lowercase + string.ascii_uppercase + string.punctuation + ' ' # len(aphabet) = 95 '''alphabet 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ ''' # Will error if the minimal version of diffusers is not installed. Remove at your own risks. check_min_version("0.15.0.dev0") logger = get_logger(__name__, log_level="INFO") def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--pretrained_model_name_or_path", type=str, default='runwayml/stable-diffusion-v1-5', 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( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--output_dir", type=str, default="sd-model-finetuned", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) 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( "--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=2 ) 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=1e-5, 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=0, 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( "--dataset_name", type=str, default='MARIO-10M', help=( "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," " or to a folder containing files that 🤗 Datasets can understand." ), ) parser.add_argument( "--use_ema", action="store_true", help="Whether to use EMA model." ) parser.add_argument( "--segmentation_mask_aug", action="store_true", help="Whether to augment the segmentation masks (inspired by https://arxiv.org/abs/2211.13227)." ) parser.add_argument( "--non_ema_revision", type=str, default=None, required=False, help=( "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or" " remote repository specified with --pretrained_model_name_or_path." ), ) parser.add_argument( "--image_column", type=str, default="image", help="The column of the dataset containing an image." ) parser.add_argument( "--caption_column", type=str, default="text", help="The column of the dataset containing a caption or a list of captions.", ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." ), ) 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=None, 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. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--report_to", type=str, default="tensorboard", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' ), ) parser.add_argument( "--local_rank", type=int, default=-1, help="For distributed training: local_rank" ) parser.add_argument( "--checkpointing_steps", type=int, default=2500, help=( "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" " training using `--resume_from_checkpoint`." ), ) parser.add_argument( "--checkpoints_total_limit", type=int, default=10, 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.' ), ) parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." ) parser.add_argument( "--noise_offset", type=float, default=0, help="The scale of noise offset." ) parser.add_argument( "--dataset_path", type=str, default='/path/to/laion-ocr-select', help="The path of dataset." ) parser.add_argument( "--train_dataset_index_file", type=str, default='/path/to/train_dataset_index.txt', help="The txt file that provides the index of training samples. The format of each line should be XXXXX_XXXXXXXXX." ) parser.add_argument( "--vis_num", type=int, default=4, help="The number of images to be visualized during training." ) parser.add_argument( "--vis_interval", type=int, default=500, help="The interval for visualization." ) parser.add_argument( "--max_length", default=77, type=int, help="Maximum length of the prompt. Can enlarge this value to adapt longer coord representation." ) args = parser.parse_args() print('***************') print(args) print('***************') 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 # default to using the same revision for the non-ema model if not specified if args.non_ema_revision is None: args.non_ema_revision = args.revision return args def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): if token is None: token = HfFolder.get_token() if organization is None: username = whoami(token)["name"] return f"{username}/{model_id}" else: return f"{organization}/{model_id}" #### check whether two boxes can be merged in the x-axis def check_merge(box1, box2): x_center1, y_center1, x_min1, y_min1, x_max1, y_max1, pred1 = box1 x_center2, y_center2, x_min2, y_min2, x_max2, y_max2, pred2 = box2 if y_center1 >= y_min2 and y_center1 <= y_max2: if y_center2 >= y_min1 and y_center2 <= y_max1: pass else: return False else: return False distance1 = x_max2 - x_min1 distance2 = (x_max2 - x_min2) + (x_max1 - x_min1) if distance2 / distance1 >= 0.8: if x_min1 < x_min2: pred = pred1 + ' ' + pred2 else: pred = pred2 + ' ' + pred1 x_min = min(x_min1, x_min2) y_min = min(y_min1, y_min2) x_max = max(x_max1, x_max2) y_max = max(y_max1, y_max2) x_center = (x_min + x_max) // 2 y_center = (y_min + y_max) // 2 return [x_center, y_center, x_min, y_min, x_max, y_max, pred] else: return False #### merge boxes for training at line-level instead of word-level def merge_boxes(boxes): results = [] while True: if len(boxes) == 0: break flag = False sample = boxes[0] boxes.remove(sample) for item in boxes: result = check_merge(sample, item) if result: boxes.remove(item) boxes.append(result) boxes = sorted(boxes, key=lambda x: x[0]) flag = True break else: pass if flag is False: results.append(sample) return results def main(): args = parse_args() if args.non_ema_revision is not None: deprecate( "non_ema_revision!=None", "0.15.0", message=( "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to" " use `--variant=non_ema` instead." ), ) logging_dir = os.path.join(args.output_dir, args.logging_dir) accelerator_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=args.report_to, project_dir=logging_dir, project_config=accelerator_project_config, ) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_warning() diffusers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() diffusers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. args.seed = random.randint(0, 1000000) if args.seed is None else args.seed print(f'{colored("[√]", "green")} Arguments are loaded.') print(args) set_seed(args.seed) print(f'{colored("[√]", "green")} Seed is set to {args.seed}.') # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id create_repo(repo_name, exist_ok=True, token=args.hub_token) repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) print(args.output_dir) # Load scheduler, tokenizer and models. noise_scheduler = DDPMScheduler.from_pretrained( args.pretrained_model_name_or_path, subfolder="scheduler" ) tokenizer = CLIPTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision ) #### additional tokens are introduced, including coordinate tokens and character tokens print('***************') print(len(tokenizer)) for i in range(520): tokenizer.add_tokens(['l' + str(i) ]) # left tokenizer.add_tokens(['t' + str(i) ]) # top tokenizer.add_tokens(['r' + str(i) ]) # right tokenizer.add_tokens(['b' + str(i) ]) # bottom for c in alphabet: tokenizer.add_tokens([f'[{c}]']) print(len(tokenizer)) print('***************') text_encoder = CLIPTextModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision ) text_encoder.resize_token_embeddings(len(tokenizer)) 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.non_ema_revision, find_unused_parameters=True ) #### text_encoder is set to the trainable state vae.requires_grad_(False) text_encoder.requires_grad_(True) if args.enable_xformers_memory_efficient_attention: if is_xformers_available(): import xformers xformers_version = version.parse(xformers.__version__) if xformers_version == version.parse("0.0.16"): logger.warn( "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." ) unet.enable_xformers_memory_efficient_attention() else: raise ValueError("xformers is not available. Make sure it is installed correctly") # `accelerate` 0.16.0 will have better support for customized saving if version.parse(accelerate.__version__) >= version.parse("0.16.0"): # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format def save_model_hook(models, weights, output_dir): if args.use_ema: ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema")) for i, model in enumerate(models): # model.save_pretrained(os.path.join(output_dir, "unet")) if i == 0: model.save_pretrained(os.path.join(output_dir, f"unet")) elif i == 1: model.save_pretrained(os.path.join(output_dir, f"text_encoder")) # make sure to pop weight so that corresponding model is not saved again weights.pop() def load_model_hook(models, input_dir): if args.use_ema: load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNet2DConditionModel) ema_unet.load_state_dict(load_model.state_dict()) ema_unet.to(accelerator.device) del load_model for i in range(len(models)): # pop models so that they are not loaded again model = models.pop() if i == 1: load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet") model.register_to_config(**load_model.config) elif i == 0: load_model = CLIPTextModel.from_pretrained(input_dir, subfolder="text_encoder") # model.register_to_config(**load_model.config) # # load diffusers style into model # load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet") # model.register_to_config(**load_model.config) model.load_state_dict(load_model.state_dict()) del load_model accelerator.register_save_state_pre_hook(save_model_hook) accelerator.register_load_state_pre_hook(load_model_hook) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() if args.scale_lr: args.learning_rate = ( args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Initialize the optimizer if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError( "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" ) optimizer_cls = bnb.optim.AdamW8bit else: optimizer_cls = torch.optim.AdamW # optimizer = optimizer_cls( # unet.parameters(), # lr=args.learning_rate, # betas=(args.adam_beta1, args.adam_beta2), # weight_decay=args.adam_weight_decay, # eps=args.adam_epsilon, # ) #### train the u-net and text encoder optimizer = optimizer_cls( [ {'params': text_encoder.parameters()}, {'params': unet.parameters()}, ], lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) from datasets import Dataset lines = open(args.train_dataset_index_file).readlines() random.shuffle(lines) train_dataset = Dataset.from_dict({"image": lines, "text": lines}) dataset = { 'train': train_dataset, } # Preprocessing the datasets. # We need to tokenize inputs and targets. column_names = dataset["train"].column_names dataset_name_mapping = { "MARIO-10M": ("image", "text"), } # 6. Get the column names for input/target. dataset_columns = dataset_name_mapping.get(args.dataset_name, None) if args.image_column is None: image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] else: image_column = args.image_column if image_column not in column_names: raise ValueError( f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" ) if args.caption_column is None: caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] else: caption_column = args.caption_column if caption_column not in column_names: raise ValueError( f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" ) #### many augmentations can not be used in the text rendering task train_transforms = transforms.Compose( [ # transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), # transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution), # transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x), transforms.ToTensor(), ] ) #### code is borrowed from textdiffuser-1: https://github.com/microsoft/unilm/tree/master/textdiffuser def generate_random_rectangles(image): # randomly generate 0~3 masks rectangles = [] box_num = random.randint(0, 3) for i in range(box_num): x = random.randint(0, image.size[0]) y = random.randint(0, image.size[1]) w = random.randint(16, 256) h = random.randint(16, 96) angle = random.randint(-45, 45) p1 = (x, y) p2 = (x + w, y) p3 = (x + w, y + h) p4 = (x, y + h) center = ((x + x + w) / 2, (y + y + h) / 2) p1 = rotate_point(p1, center, angle) p2 = rotate_point(p2, center, angle) p3 = rotate_point(p3, center, angle) p4 = rotate_point(p4, center, angle) rectangles.append((p1, p2, p3, p4)) return rectangles def rotate_point(point, center, angle): # rotation angle = math.radians(angle) x = point[0] - center[0] y = point[1] - center[1] x1 = x * math.cos(angle) - y * math.sin(angle) y1 = x * math.sin(angle) + y * math.cos(angle) x1 += center[0] y1 += center[1] return int(x1), int(y1) def box2point(box): # convert string to list box = box.split(',') box = [int(i)//(512//512) for i in box] points = [(box[0],box[1]),(box[2],box[3]),(box[4],box[5]),(box[6],box[7])] return points def get_mask(ocrs): image_mask = Image.new('L', (512,512), 0) draw_image_mask = ImageDraw.ImageDraw(image_mask) mask_ocrs = [] for ocr in ocrs: x_center, y_center, x_min, y_min, x_max, y_max, pred = ocr if random.random() < 0.5: # each box is masked with 50% probability # points = box2point(box) points = [(x_min, y_min), (x_max, y_min), (x_max, y_max), (x_min, y_max)] draw_image_mask.polygon(points, fill=1) mask_ocrs.append(ocr) blank = Image.new('RGB', (512, 512), (0, 0, 0)) rectangles = generate_random_rectangles(blank) # get additional masks (can mask non-text areas) for rectangle in rectangles: draw_image_mask.polygon(rectangle, fill=1) return image_mask, mask_ocrs def preprocess_train(examples): # preprocess the training data images = [] image_masks = [] prompts_train = [] prompts = [] prompts_nocond = [] for image in examples[image_column]: image = image.strip() first, second = image.split('_') image_path = f'{args.dataset_path}/{first}/{second}/image.jpg' ocrs = open(f'{args.dataset_path}/{first}/{second}/ocr.txt').readlines() ocrs_temp = [] for line in ocrs: line = line.strip() pred, box, prob = line.split() items = box.split(',') x1, y1, x2, y2, x3, y3, x4, y4 = int(items[0]), int(items[1]), int(items[2]), int(items[3]), int(items[4]), int(items[5]), int(items[6]), int(items[7]) x_min = min(x1, x2, x3, x4) y_min = min(y1, y2, y3, y4) x_max = max(x1, x2, x3, x4) y_max = max(y1, y2, y3, y4) x_center = (x_min + x_max) // 2 y_center = (y_min + y_max) // 2 ocrs_temp.append([x_center, y_center, x_min, y_min, x_max, y_max, pred]) ocrs_temp = sorted(ocrs_temp, key=lambda x: x[0]) ocrs_temp = merge_boxes(ocrs_temp) ocrs_temp = sorted(ocrs_temp, key=lambda x: x[1]) random.shuffle(ocrs_temp) image_mask, mask_ocrs = get_mask(ocrs_temp) image = Image.open(image_path).convert("RGB") try: caption = open(f'{args.dataset_path}/{first}/{second}/caption.txt').readlines()[0] except: caption = 'null' print('erorr of caption') caption_ids = tokenizer( caption, truncation=True, return_tensors="pt" ).input_ids[0].tolist() ocr_ids = [] for line in mask_ocrs: x_center, y_center, x_min, y_min, x_max, y_max, pred = line char_list = list(pred) ocr_ids.extend([f'l{x_min//4}', f't{y_min//4}', f'r{x_max//4}', f'b{y_max//4}']) char_list = [f'[{i}]' for i in char_list] ocr_ids.extend(char_list) ocr_ids.append(tokenizer.eos_token_id) ocr_ids.append(tokenizer.eos_token_id) ocr_ids = tokenizer.encode(ocr_ids) prompt = caption_ids + ocr_ids prompt = prompt[:args.max_length] while len(prompt) < args.max_length: prompt.append(tokenizer.pad_token_id) prompts.append(prompt) prompts_nocond.append([tokenizer.pad_token_id]*args.max_length) if random.random() < 0.1: prompts_train.append([tokenizer.pad_token_id]*args.max_length) else: prompts_train.append(prompt) image_mask_np = np.array(image_mask) image_mask_tensor = torch.from_numpy(image_mask_np) images.append(image) # segmentation_masks.append(segmentation_mask) image_masks.append(image_mask_tensor) examples["images"] = [train_transforms(image).sub_(0.5).div_(0.5) for image in images] examples["prompts_cond"] = prompts examples["prompts_nocond"] = prompts_nocond examples["prompts_train"] = prompts_train # examples["segmentation_masks"] = segmentation_masks examples["image_masks"] = image_masks return examples with accelerator.main_process_first(): if args.max_train_samples is not None: dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) # Set the training transforms train_dataset = dataset["train"].with_transform(preprocess_train) def collate_fn(examples): images = torch.stack([example["images"] for example in examples]) images = images.to(memory_format=torch.contiguous_format).float() image_masks = torch.cat([example["image_masks"].unsqueeze(0) for example in examples],0) prompts = torch.Tensor([example["prompts_train"] for example in examples]).long() prompts_cond = torch.Tensor([example["prompts_cond"] for example in examples]).long() prompts_nocond = torch.Tensor([example["prompts_nocond"] for example in examples]).long() return {"images": images, "prompts_train": prompts, "prompts_cond": prompts_cond, "prompts_nocond": prompts_nocond, "image_masks": image_masks} # DataLoaders creation: train_dataloader = torch.utils.data.DataLoader( train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size, num_workers=args.dataloader_num_workers, ) # 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, ) # Prepare everything with our `accelerator`. #### please note that "text_encoder" should be added for training unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, text_encoder, optimizer, train_dataloader, lr_scheduler ) if args.use_ema: ema_unet.to(accelerator.device) # 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. weight_dtype = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 # Move text_encode and vae to gpu and cast to weight_dtype # text_encoder.to(accelerator.device, dtype=weight_dtype) vae.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("text2image-fine-tune", 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}") global_step = 0 first_epoch = 0 # Potentially load in the weights and states from a previous save 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() text_encoder.train() train_loss = 0.0 for step, batch in enumerate(train_dataloader): with accelerator.accumulate(unet): # Convert images to latent space features = vae.encode(batch["images"].to(weight_dtype)).latent_dist.sample() features = features * vae.config.scaling_factor image_masks = batch["image_masks"] masked_images = batch["images"] * (1-image_masks).unsqueeze(1) masked_features = vae.encode(masked_images.to(weight_dtype)).latent_dist.sample() masked_features = masked_features * vae.config.scaling_factor feature_masks = F.interpolate(image_masks.unsqueeze(1), size=(64, 64), mode='nearest') # Sample noise that we'll add to the latents noise = torch.randn_like(features) if args.noise_offset: # https://www.crosslabs.org//blog/diffusion-with-offset-noise noise += args.noise_offset * torch.randn( (features.shape[0], features.shape[1], 1, 1), device=features.device ) bsz = features.shape[0] timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=features.device) timesteps = timesteps.long() noisy_latents = noise_scheduler.add_noise(features, noise, timesteps) encoder_hidden_states = text_encoder(batch["prompts_train"])[0] # 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(features, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") model_pred = unet( sample=noisy_latents, timestep=timesteps, encoder_hidden_states=encoder_hidden_states, masked_feature=masked_features, feature_mask=feature_masks, ).sample # pred_x0 = noise_scheduler.get_x0_from_noise(model_pred, timesteps, noisy_latents) mse_loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") loss = mse_loss avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() train_loss += avg_loss.item() / args.gradient_accumulation_steps # Backpropagate accelerator.backward(loss) if accelerator.sync_gradients: accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # if accelerator.is_main_process: if (step + 0) % args.vis_interval == 0: random_code = random.randint(0,10000000) scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") scheduler.set_timesteps(50) noise = torch.randn((args.vis_num, 4, 64, 64)).to("cuda") input = noise encoder_hidden_states_cond = text_encoder(batch["prompts_cond"])[0] encoder_hidden_states_nocond = text_encoder(batch["prompts_nocond"])[0] texts = batch["prompts_cond"] f = open(f'{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_prompt_{random_code}.txt', 'w+') for text in texts: sentence = tokenizer.decode(text) f.write(sentence + '\n') f.close() for t in tqdm(scheduler.timesteps): with torch.no_grad(): # classifier free guidance noise_pred_cond = unet(sample=input.half(), timestep=t, encoder_hidden_states=encoder_hidden_states_cond[:args.vis_num], masked_feature=masked_features[:args.vis_num], feature_mask=feature_masks[:args.vis_num]).sample # b, 4, 64, 64 noise_pred_uncond = unet(sample=input.half(), timestep=t, encoder_hidden_states=encoder_hidden_states_nocond[:args.vis_num], masked_feature=masked_features[:args.vis_num], feature_mask=feature_masks[:args.vis_num]).sample # b, 4, 64, 64 noisy_residual = noise_pred_uncond + 7.5 * (noise_pred_cond - noise_pred_uncond) # b, 4, 64, 64 prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample input = prev_noisy_sample # decode input = 1 / vae.config.scaling_factor * input images = vae.decode(input.half(), return_dict=False)[0] ## save predicted images width, height = 512, 512 new_image = Image.new('RGB', (4*width, 4*height)) for index, image in enumerate(images.float()): image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0) image = image.cpu().permute(0, 2, 3, 1).numpy()[0] image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB') row = index // 4 col = index % 4 new_image.paste(image, (col*width, row*height)) new_image.save(f'{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_pred_img_{random_code}.jpg') ## save original images width, height = 512, 512 new_image = Image.new('RGB', (4*width, 4*height)) for index, image in enumerate(batch["images"][:args.vis_num]): image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0) image = image.cpu().permute(0, 2, 3, 1).numpy()[0] image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB') # pred_images.append(image) row = index // 4 col = index % 4 new_image.paste(image, (col*width, row*height)) new_image.save(f'{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_orig_img_{random_code}.jpg') ## save masked original images width, height = 512, 512 new_image = Image.new('RGB', (4*width, 4*height)) for index, image in enumerate(masked_images[:args.vis_num]): image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0) image = image.cpu().permute(0, 2, 3, 1).numpy()[0] image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB') # pred_images.append(image) row = index // 4 col = index % 4 new_image.paste(image, (col*width, row*height)) new_image.save(f'{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_masked_orig_img_{random_code}.jpg') print('inference successfully') # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: if args.use_ema: ema_unet.step(unet.parameters()) progress_bar.update(1) global_step += 1 accelerator.log({"train_loss": train_loss}, step=global_step) train_loss = 0.0 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 = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], 'mse_loss': mse_loss.detach().item()} progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break # Create the pipeline using the trained modules and save it. accelerator.wait_for_everyone() if accelerator.is_main_process: unet = accelerator.unwrap_model(unet) if args.use_ema: ema_unet.copy_to(unet.parameters()) pipeline = StableDiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, revision=args.revision, ) pipeline.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) accelerator.end_training() if __name__ == "__main__": main()