426 lines
16 KiB
Python
426 lines
16 KiB
Python
import argparse
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import math
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import os
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from pathlib import Path
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from typing import Optional
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import torch
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import torch.nn.functional as F
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from datasets import load_dataset
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from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import EMAModel
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from huggingface_hub import HfFolder, Repository, whoami
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from torchvision.transforms import (
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CenterCrop,
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Compose,
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InterpolationMode,
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Normalize,
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RandomHorizontalFlip,
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Resize,
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ToTensor,
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)
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from tqdm.auto import tqdm
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logger = get_logger(__name__)
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def _extract_into_tensor(arr, timesteps, broadcast_shape):
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"""
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Extract values from a 1-D numpy array for a batch of indices.
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:param arr: the 1-D numpy array.
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:param timesteps: a tensor of indices into the array to extract.
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:param broadcast_shape: a larger shape of K dimensions with the batch
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dimension equal to the length of timesteps.
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:return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
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"""
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if not isinstance(arr, torch.Tensor):
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arr = torch.from_numpy(arr)
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res = arr[timesteps].float().to(timesteps.device)
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while len(res.shape) < len(broadcast_shape):
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res = res[..., None]
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return res.expand(broadcast_shape)
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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"--dataset_name",
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type=str,
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default=None,
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help=(
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"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
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" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
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" or to a folder containing files that HF Datasets can understand."
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),
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The config of the Dataset, leave as None if there's only one config.",
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)
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parser.add_argument(
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"--train_data_dir",
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type=str,
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default=None,
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help=(
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"A folder containing the training data. Folder contents must follow the structure described in"
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" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
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" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
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),
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="ddpm-model-64",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument("--overwrite_output_dir", action="store_true")
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parser.add_argument(
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"--cache_dir",
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type=str,
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default=None,
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help="The directory where the downloaded models and datasets will be stored.",
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)
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parser.add_argument(
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"--resolution",
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type=int,
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default=64,
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help=(
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"The resolution for input images, all the images in the train/validation dataset will be resized to this"
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" resolution"
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),
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument(
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"--eval_batch_size", type=int, default=16, help="The number of images to generate for evaluation."
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)
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parser.add_argument(
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"--dataloader_num_workers",
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type=int,
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default=0,
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help=(
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"The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
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" process."
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),
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)
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parser.add_argument("--num_epochs", type=int, default=100)
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parser.add_argument("--save_images_epochs", type=int, default=10, help="How often to save images during training.")
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parser.add_argument(
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"--save_model_epochs", type=int, default=10, help="How often to save the model during training."
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=1e-4,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="cosine",
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help=(
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'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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' "constant", "constant_with_warmup"]'
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),
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)
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parser.add_argument(
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"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument("--adam_beta1", type=float, default=0.95, help="The beta1 parameter for the Adam optimizer.")
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parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument(
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"--adam_weight_decay", type=float, default=1e-6, help="Weight decay magnitude for the Adam optimizer."
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)
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parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer.")
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parser.add_argument(
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"--use_ema",
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action="store_true",
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default=True,
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help="Whether to use Exponential Moving Average for the final model weights.",
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)
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parser.add_argument("--ema_inv_gamma", type=float, default=1.0, help="The inverse gamma value for the EMA decay.")
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parser.add_argument("--ema_power", type=float, default=3 / 4, help="The power value for the EMA decay.")
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parser.add_argument("--ema_max_decay", type=float, default=0.9999, help="The maximum decay magnitude for EMA.")
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--hub_model_id",
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type=str,
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default=None,
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help="The name of the repository to keep in sync with the local `output_dir`.",
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)
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parser.add_argument(
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"--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
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)
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default="no",
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose"
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"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
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"and an Nvidia Ampere GPU."
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),
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)
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parser.add_argument(
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"--predict_mode",
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type=str,
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default="eps",
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help="What the model should predict. 'eps' to predict error, 'x0' to directly predict reconstruction",
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)
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parser.add_argument("--ddpm_num_steps", type=int, default=1000)
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parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
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args = parser.parse_args()
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env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
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if env_local_rank != -1 and env_local_rank != args.local_rank:
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args.local_rank = env_local_rank
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if args.dataset_name is None and args.train_data_dir is None:
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raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
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return args
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def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
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if token is None:
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token = HfFolder.get_token()
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if organization is None:
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username = whoami(token)["name"]
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return f"{username}/{model_id}"
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else:
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return f"{organization}/{model_id}"
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def main(args):
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logging_dir = os.path.join(args.output_dir, args.logging_dir)
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accelerator = Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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mixed_precision=args.mixed_precision,
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log_with="tensorboard",
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logging_dir=logging_dir,
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)
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model = UNet2DModel(
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sample_size=args.resolution,
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in_channels=3,
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out_channels=3,
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layers_per_block=2,
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block_out_channels=(128, 128, 256, 256, 512, 512),
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down_block_types=(
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"DownBlock2D",
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"DownBlock2D",
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"DownBlock2D",
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"DownBlock2D",
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"AttnDownBlock2D",
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"DownBlock2D",
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),
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up_block_types=(
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"UpBlock2D",
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"AttnUpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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"UpBlock2D",
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),
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)
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noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)
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optimizer = torch.optim.AdamW(
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model.parameters(),
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lr=args.learning_rate,
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betas=(args.adam_beta1, args.adam_beta2),
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weight_decay=args.adam_weight_decay,
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eps=args.adam_epsilon,
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)
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augmentations = Compose(
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[
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Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
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CenterCrop(args.resolution),
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RandomHorizontalFlip(),
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ToTensor(),
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Normalize([0.5], [0.5]),
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]
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)
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if args.dataset_name is not None:
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dataset = load_dataset(
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args.dataset_name,
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args.dataset_config_name,
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cache_dir=args.cache_dir,
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split="train",
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)
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else:
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dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")
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def transforms(examples):
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images = [augmentations(image.convert("RGB")) for image in examples["image"]]
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return {"input": images}
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logger.info(f"Dataset size: {len(dataset)}")
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dataset.set_transform(transforms)
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train_dataloader = torch.utils.data.DataLoader(
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dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
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)
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lr_scheduler = get_scheduler(
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args.lr_scheduler,
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optimizer=optimizer,
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num_warmup_steps=args.lr_warmup_steps,
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num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
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)
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model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
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model, optimizer, train_dataloader, lr_scheduler
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)
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num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
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ema_model = EMAModel(model, inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay)
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# Handle the repository creation
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if accelerator.is_main_process:
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if args.push_to_hub:
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if args.hub_model_id is None:
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repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
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else:
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repo_name = args.hub_model_id
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repo = Repository(args.output_dir, clone_from=repo_name)
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with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
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if "step_*" not in gitignore:
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gitignore.write("step_*\n")
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if "epoch_*" not in gitignore:
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gitignore.write("epoch_*\n")
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elif args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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if accelerator.is_main_process:
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run = os.path.split(__file__)[-1].split(".")[0]
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accelerator.init_trackers(run)
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global_step = 0
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for epoch in range(args.num_epochs):
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model.train()
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progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
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progress_bar.set_description(f"Epoch {epoch}")
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for step, batch in enumerate(train_dataloader):
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clean_images = batch["input"]
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# Sample noise that we'll add to the images
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noise = torch.randn(clean_images.shape).to(clean_images.device)
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bsz = clean_images.shape[0]
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# Sample a random timestep for each image
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timesteps = torch.randint(
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0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device
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).long()
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# Add noise to the clean images according to the noise magnitude at each timestep
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# (this is the forward diffusion process)
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noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
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with accelerator.accumulate(model):
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# Predict the noise residual
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model_output = model(noisy_images, timesteps).sample
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if args.predict_mode == "eps":
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loss = F.mse_loss(model_output, noise) # this could have different weights!
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elif args.predict_mode == "x0":
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alpha_t = _extract_into_tensor(
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noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)
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)
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snr_weights = alpha_t / (1 - alpha_t)
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loss = snr_weights * F.mse_loss(
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model_output, clean_images, reduction="none"
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) # use SNR weighting from distillation paper
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loss = loss.mean()
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accelerator.backward(loss)
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if accelerator.sync_gradients:
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accelerator.clip_grad_norm_(model.parameters(), 1.0)
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optimizer.step()
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lr_scheduler.step()
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if args.use_ema:
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ema_model.step(model)
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optimizer.zero_grad()
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# Checks if the accelerator has performed an optimization step behind the scenes
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if accelerator.sync_gradients:
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progress_bar.update(1)
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global_step += 1
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logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
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if args.use_ema:
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logs["ema_decay"] = ema_model.decay
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progress_bar.set_postfix(**logs)
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accelerator.log(logs, step=global_step)
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progress_bar.close()
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accelerator.wait_for_everyone()
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# Generate sample images for visual inspection
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if accelerator.is_main_process:
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if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:
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pipeline = DDPMPipeline(
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unet=accelerator.unwrap_model(ema_model.averaged_model if args.use_ema else model),
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scheduler=noise_scheduler,
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)
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generator = torch.manual_seed(0)
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# run pipeline in inference (sample random noise and denoise)
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images = pipeline(
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generator=generator,
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batch_size=args.eval_batch_size,
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output_type="numpy",
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predict_epsilon=args.predict_mode == "eps",
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).images
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# denormalize the images and save to tensorboard
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images_processed = (images * 255).round().astype("uint8")
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accelerator.trackers[0].writer.add_images(
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"test_samples", images_processed.transpose(0, 3, 1, 2), epoch
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)
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if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
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# save the model
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pipeline.save_pretrained(args.output_dir)
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if args.push_to_hub:
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repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=False)
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accelerator.wait_for_everyone()
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accelerator.end_training()
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if __name__ == "__main__":
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args = parse_args()
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main(args)
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