unilm/textdiffuser-2/train_textdiffuser2_inpainting_full.py

# ------------------------------------------
# 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()