# ------------------------------------------ # 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 argparse import logging import math import os import random import shutil from pathlib import Path import glob import json import datasets import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from datasets import load_dataset, Dataset from huggingface_hub import create_repo, upload_folder from packaging import version from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer import diffusers from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel from diffusers.loaders import AttnProcsLayers from diffusers.models.attention_processor import LoRAAttnProcessor from diffusers.optimization import get_scheduler from diffusers.utils import check_min_version, is_wandb_available from diffusers.utils.import_utils import is_xformers_available from PIL import Image import string alphabet = string.digits + string.ascii_lowercase + string.ascii_uppercase + string.punctuation + ' ' # len(aphabet) = 95 '''alphabet 0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ ''' logger = get_logger(__name__, log_level="INFO") def save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None): img_str = "" for i, image in enumerate(images): image.save(os.path.join(repo_folder, f"image_{i}.png")) img_str += f"![img_{i}](./image_{i}.png)\n" yaml = f""" --- license: creativeml-openrail-m base_model: {base_model} tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- """ model_card = f""" # LoRA text2image fine-tuning - {repo_id} These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n {img_str} """ with open(os.path.join(repo_folder, "README.md"), "w") as f: f.write(yaml + model_card) #### 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 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( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--dataset_name", type=str, default='lambdalabs/pokemon-blip-captions', 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( "--dataset_config_name", type=str, default=None, help="The config of the Dataset, leave as None if there's only one config.", ) parser.add_argument( "--train_data_dir", type=str, default=None, help=( "A folder containing the training data. Folder contents must follow the structure described in" " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." ), ) 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( "--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-lora", 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( "--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( "--random_flip", action="store_true", help="whether to randomly flip images horizontally", ) parser.add_argument( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) parser.add_argument("--num_train_epochs", type=int, default=100) parser.add_argument( "--max_train_steps", type=int, default=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-4, #### lora is trained with higher lr help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--text_encoder_learning_rate", type=float, default=1e-5, #### the text encoder is trained with lower lr to avoid the forgetting help="Initial learning rate for the text encoder (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( "--snr_gamma", type=float, default=None, help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. " "More details here: https://arxiv.org/abs/2303.09556.", ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) 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( "--prediction_type", type=str, default=None, help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.", ) 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, # should be decreased for saving space help=("Max number of checkpoints to store."), ) 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( "--rank", type=int, default=4, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--vis_num", type=int, default=16, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--vis_interval", type=int, default=1000, help="The interval for visualization." ) #### newly added parameters parser.add_argument( "--granularity", type=int, default=128, #### limit the coord range to 0~128 will make the feature space compact help="The granularity of coordinates, ranging from 1~512." ) parser.add_argument( "--coord_mode", type=str, default='lt', choices=['lt', 'center', 'ltrb'], #### l, t, r, b stand for left, top, right, bottom help="The way to represent coordinates" ) parser.add_argument( "--drop_coord", #### not used in the experiment. model is hard to train without the coord guidance action='store_true', help="Whether to drop coord during training. Add more diversity." ) parser.add_argument( "--max_length", default=77, #### enlarge the context length of text encoder. empirically, enlarging the context length can proceed longer sequence. However, we observe that it will be hard to render general objects type=int, help="Maximum length of the composed prompt" ) parser.add_argument( "--index_file_path", type=str, default='/path/to/train_dataset_index.txt', required=True, help="The path of data index file, each line should follow the format 00123_0012300567 ...." ) parser.add_argument( "--dataset_path", type=str, default='/path/to/laion-ocr-select', required=True, help="the root of the dataset, please follow the code in textdiffuser-1" ) ###################################################################### 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 # Sanity checks if args.dataset_name is None and args.train_data_dir is None: raise ValueError("Need either a dataset name or a training folder.") return args DATASET_NAME_MAPPING = { # "lambdalabs/pokemon-blip-captions": ("image", "text"), "MARIO-10M": ("image", "text"), } def main(): args = parse_args() logging_dir = Path(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, ) if args.report_to == "wandb": if not is_wandb_available(): raise ImportError("Make sure to install wandb if you want to use it for logging during training.") import wandb # 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. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token ).repo_id # Load 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('[Size of the original tokenizer] ', 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) ]) # width tokenizer.add_tokens(['b' + str(i) ]) # height for c in alphabet: tokenizer.add_tokens([f'[{c}]']) # character-level embedding print('[Size of the modified tokenizer] ', len(tokenizer)) if args.max_length == 77: text_encoder = CLIPTextModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision ) else: #### enlarge the context length of text encoder. empirically, enlarging the context length can proceed longer sequence. However, we observe that it will be hard to render general objects text_encoder = CLIPTextModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, max_position_embeddings=args.max_length, ignore_mismatched_sizes=True ) 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.revision ) # freeze parameters of models to save more memory unet.requires_grad_(False) # unet is not trained since lora is used vae.requires_grad_(False) #### the text_encoder should be trainable to learn the newly-added tokens text_encoder.requires_grad_(True) # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision # as these weights 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 unet, vae and text_encoder to device and cast to weight_dtype #### only operate parameters unet.to(accelerator.device, dtype=weight_dtype) vae.to(accelerator.device, dtype=weight_dtype) # text_encoder.to(accelerator.device, dtype=weight_dtype) # now we will add new LoRA weights to the attention layers # It's important to realize here how many attention weights will be added and of which sizes # The sizes of the attention layers consist only of two different variables: # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`. # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`. # Let's first see how many attention processors we will have to set. # For Stable Diffusion, it should be equal to: # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12 # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2 # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18 # => 32 layers # Set correct lora layers lora_attn_procs = {} for name in unet.attn_processors.keys(): cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim if name.startswith("mid_block"): hidden_size = unet.config.block_out_channels[-1] elif name.startswith("up_blocks"): block_id = int(name[len("up_blocks.")]) hidden_size = list(reversed(unet.config.block_out_channels))[block_id] elif name.startswith("down_blocks"): block_id = int(name[len("down_blocks.")]) hidden_size = unet.config.block_out_channels[block_id] lora_attn_procs[name] = LoRAAttnProcessor( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=args.rank, ) unet.set_attn_processor(lora_attn_procs) 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") def compute_snr(timesteps): """ Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849 """ alphas_cumprod = noise_scheduler.alphas_cumprod sqrt_alphas_cumprod = alphas_cumprod**0.5 sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5 # Expand the tensors. # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026 sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float() while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape): sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None] alpha = sqrt_alphas_cumprod.expand(timesteps.shape) sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float() while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape): sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None] sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape) # Compute SNR. snr = (alpha / sigma) ** 2 return snr lora_layers = AttnProcsLayers(unet.attn_processors) # Enable TF32 for faster training on Ampere GPUs, # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices if args.allow_tf32: torch.backends.cuda.matmul.allow_tf32 = True 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( # lora_layers.parameters(), # lr=args.learning_rate, # betas=(args.adam_beta1, args.adam_beta2), # weight_decay=args.adam_weight_decay, # eps=args.adam_epsilon, # ) #### the optimizer is modified to train both the text_encoder and the lora optimizer = optimizer_cls( [ {'params': text_encoder.parameters(), 'lr': args.text_encoder_learning_rate}, # 1e-5 {'params': lora_layers.parameters(), 'lr': args.learning_rate}, # 1e-4 ], lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon ) #### load the data through the index file path lines = open(args.index_file_path).readlines() random.shuffle(lines) train_dataset = Dataset.from_dict({"image": lines, "text": lines}) # 一些line列表，这个还是好处理的 dataset = { 'train': train_dataset, } # Preprocessing the datasets. # We need to tokenize inputs and targets. column_names = dataset["train"].column_names # 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(), ] ) #### process the training data def preprocess_train(examples): images = [] prompts_train = [] prompts_cond = [] prompts_nocond = [] for image in examples[image_column]: image = image.strip() first, second = image.split('_') #### get image image_path = f'{args.dataset_path}/{first}/{second}/image.jpg' image = Image.open(image_path).convert("RGB") images.append(image) #### get caption try: #### note that few cases do not contain valid captions caption = open(f'{args.dataset_path}/{first}/{second}/caption.txt').readlines()[0] except: caption = 'null' print('erorr of caption') #### get ocr #### since the original ocr annotations are word-level, we need to merge some boxes to construct line-level ocr 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) #### augment the ocr sequence for robust training ocr_ids = [] #### concat with the prompt tokens for line in ocrs_temp: x_center, y_center, x_min, y_min, x_max, y_max, pred = line # choose coord mode if args.coord_mode == 'lt': x_left = x_min y_top = y_min x_left = x_left // (512 // args.granularity) y_top = y_top // (512 // args.granularity) x_left = np.clip(x_left, 0, args.granularity) y_top = np.clip(y_top, 0, args.granularity) ocr_ids.extend(['l'+str(x_left), 't'+str(y_top)]) elif args.coord_mode == 'center': x_center = x_center // (512 // args.granularity) y_center = y_center // (512 // args.granularity) x_center = np.clip(x_center, 0, args.granularity) y_center = np.clip(y_center, 0, args.granularity) ocr_ids.extend(['l'+str(x_center), 't'+str(y_center)]) elif args.coord_mode == 'ltrb': x_left = x_min y_top = y_min x_right = x_max y_bottom = y_max x_left = x_left // (512 // args.granularity) y_top = y_top // (512 // args.granularity) x_right = x_right // (512 // args.granularity) y_bottom = y_bottom // (512 // args.granularity) x_left = np.clip(x_left, 0, args.granularity) y_top = np.clip(y_top, 0, args.granularity) x_right = np.clip(x_right, 0, args.granularity) y_bottom = np.clip(y_bottom, 0, args.granularity) ocr_ids.extend(['l'+str(x_left), 't'+str(y_top), 'r'+str(x_right), 'b'+str(y_bottom)]) char_list = list(pred) 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) caption_ids = tokenizer( caption, truncation=True, return_tensors="pt" ).input_ids[0].tolist() prompt = caption_ids + ocr_ids prompt = prompt[:args.max_length] while len(prompt) < args.max_length: prompt.append(tokenizer.pad_token_id) prompts_cond.append(prompt) prompts_nocond.append([tokenizer.pad_token_id]*args.max_length) #### classifier-free guidance if random.random() < 0.1: prompts_train.append([tokenizer.pad_token_id]*args.max_length) else: prompts_train.append(prompt) examples["images"] = [train_transforms(image).sub_(0.5).div_(0.5) for image in images] examples["prompts_train"] = prompts_train examples["prompts_cond"] = prompts_cond examples["prompts_nocond"] = prompts_nocond 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() prompts_train = 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_train, "prompts_cond": prompts_cond, "prompts_nocond": prompts_nocond} # 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 lora_layers, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( lora_layers, text_encoder, optimizer, train_dataloader, lr_scheduler ) # 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}") #### modified. directly load ckpt from args.resume_from_checkpoint accelerator.load_state(args.resume_from_checkpoint) 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): # # 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["images"].to(dtype=weight_dtype)).latent_dist.sample() latents = latents * vae.config.scaling_factor # Sample noise that we'll add to the latents noise = torch.randn_like(latents) if args.noise_offset: # https://www.crosslabs.org//blog/diffusion-with-offset-noise noise += args.noise_offset * torch.randn( (latents.shape[0], latents.shape[1], 1, 1), device=latents.device ) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) 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) # Get the text embedding for conditioning encoder_hidden_states = text_encoder(batch["prompts_train"])[0] # Get the target for loss depending on the prediction type if args.prediction_type is not None: # set prediction_type of scheduler if defined noise_scheduler.register_to_config(prediction_type=args.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}") # Predict the noise residual and compute loss # model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample if args.snr_gamma is None: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") else: # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556. # Since we predict the noise instead of x_0, the original formulation is slightly changed. # This is discussed in Section 4.2 of the same paper. snr = compute_snr(timesteps) mse_loss_weights = ( torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr ) # We first calculate the original loss. Then we mean over the non-batch dimensions and # rebalance the sample-wise losses with their respective loss weights. # Finally, we take the mean of the rebalanced loss. loss = F.mse_loss(model_pred.float(), target.float(), reduction="none") loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights loss = loss.mean() # Gather the losses across all processes for logging (if we use distributed training). 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_(text_encoder.parameters(), args.max_grad_norm) params_to_clip = lora_layers.parameters() accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() #### visualization during training if True: # if accelerator.is_main_process: cfg = 7 if (step + 0) % args.vis_interval == 0: 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_{args.local_rank}.txt', 'w+') for text in texts: # also need to call a function to map back 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]).sample # b, 4, 64, 64 noise_pred_uncond = unet(sample=input.half(), timestep=t, encoder_hidden_states=encoder_hidden_states_nocond[:args.vis_num]).sample # b, 4, 64, 64 noisy_residual = noise_pred_uncond + cfg * (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_cfg{cfg}_{args.local_rank}.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_{args.local_rank}.jpg') 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 # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: 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: # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` if args.checkpoints_total_limit is not None: checkpoints = os.listdir(args.output_dir) checkpoints = [d for d in checkpoints if d.startswith("checkpoint")] checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1])) # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints if len(checkpoints) >= args.checkpoints_total_limit: num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1 removing_checkpoints = checkpoints[0:num_to_remove] logger.info( f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" ) logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}") for removing_checkpoint in removing_checkpoints: removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint) shutil.rmtree(removing_checkpoint) 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]} progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break # Save the lora layers accelerator.wait_for_everyone() if accelerator.is_main_process: unet = unet.to(torch.float32) unet.save_attn_procs(args.output_dir) if args.push_to_hub: save_model_card( repo_id, images=images, base_model=args.pretrained_model_name_or_path, dataset_name=args.dataset_name, repo_folder=args.output_dir, ) upload_folder( repo_id=repo_id, folder_path=args.output_dir, commit_message="End of training", ignore_patterns=["step_*", "epoch_*"], ) # Final inference # Load previous pipeline pipeline = DiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype ) pipeline = pipeline.to(accelerator.device) # load attention processors pipeline.unet.load_attn_procs(args.output_dir) accelerator.end_training() if __name__ == "__main__": main()