793 lines
32 KiB
Python
793 lines
32 KiB
Python
import argparse
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import copy
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import logging
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import math
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import os
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import random
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from pathlib import Path
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from typing import Iterable, Optional
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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import datasets
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import diffusers
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import transformers
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import set_seed
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from datasets import load_dataset
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from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
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from diffusers.optimization import get_scheduler
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from diffusers.utils import check_min_version
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from diffusers.utils.import_utils import is_xformers_available
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from huggingface_hub import HfFolder, Repository, whoami
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from torchvision import transforms
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from tqdm.auto import tqdm
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from transformers import CLIPTextModel, CLIPTokenizer
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# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
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check_min_version("0.10.0.dev0")
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logger = get_logger(__name__, log_level="INFO")
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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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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default=None,
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required=False,
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help="Revision of pretrained model identifier from huggingface.co/models.",
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)
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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 🤗 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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"--image_column", type=str, default="image", help="The column of the dataset containing an image."
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)
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parser.add_argument(
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"--caption_column",
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type=str,
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default="text",
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help="The column of the dataset containing a caption or a list of captions.",
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)
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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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="sd-model-finetuned",
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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(
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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("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--resolution",
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type=int,
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default=512,
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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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"--center_crop",
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action="store_true",
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help="Whether to center crop images before resizing to resolution (if not set, random crop will be used)",
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)
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parser.add_argument(
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"--random_flip",
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action="store_true",
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help="whether to randomly flip images horizontally",
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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("--num_train_epochs", type=int, default=100)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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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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"--gradient_checkpointing",
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action="store_true",
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help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward 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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"--scale_lr",
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action="store_true",
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default=False,
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help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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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="constant",
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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(
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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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parser.add_argument(
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"--allow_tf32",
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action="store_true",
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help=(
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"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
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" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
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),
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)
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parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
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parser.add_argument(
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"--non_ema_revision",
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type=str,
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default=None,
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required=False,
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help=(
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"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
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" remote repository specified with --pretrained_model_name_or_path."
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),
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, 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("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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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("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
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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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"--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(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
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" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
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" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
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),
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)
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parser.add_argument(
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"--report_to",
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type=str,
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default="tensorboard",
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help=(
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'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
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' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
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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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"--checkpointing_steps",
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type=int,
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default=500,
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help=(
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"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
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" training using `--resume_from_checkpoint`."
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),
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help=(
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"Whether training should be resumed from a previous checkpoint. Use a path saved by"
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' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
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),
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)
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parser.add_argument(
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"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
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)
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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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# Sanity checks
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if args.dataset_name is None and args.train_data_dir is None:
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raise ValueError("Need either a dataset name or a training folder.")
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# default to using the same revision for the non-ema model if not specified
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if args.non_ema_revision is None:
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args.non_ema_revision = args.revision
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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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dataset_name_mapping = {
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"lambdalabs/pokemon-blip-captions": ("image", "text"),
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}
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# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14
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class EMAModel:
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"""
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Exponential Moving Average of models weights
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"""
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def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999):
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parameters = list(parameters)
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self.shadow_params = [p.clone().detach() for p in parameters]
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self.collected_params = None
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self.decay = decay
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self.optimization_step = 0
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@torch.no_grad()
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def step(self, parameters):
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parameters = list(parameters)
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self.optimization_step += 1
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# Compute the decay factor for the exponential moving average.
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value = (1 + self.optimization_step) / (10 + self.optimization_step)
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one_minus_decay = 1 - min(self.decay, value)
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for s_param, param in zip(self.shadow_params, parameters):
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if param.requires_grad:
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s_param.sub_(one_minus_decay * (s_param - param))
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else:
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s_param.copy_(param)
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torch.cuda.empty_cache()
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def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
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"""
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Copy current averaged parameters into given collection of parameters.
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Args:
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parameters: Iterable of `torch.nn.Parameter`; the parameters to be
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updated with the stored moving averages. If `None`, the
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parameters with which this `ExponentialMovingAverage` was
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initialized will be used.
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"""
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parameters = list(parameters)
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for s_param, param in zip(self.shadow_params, parameters):
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param.data.copy_(s_param.data)
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def to(self, device=None, dtype=None) -> None:
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r"""Move internal buffers of the ExponentialMovingAverage to `device`.
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Args:
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device: like `device` argument to `torch.Tensor.to`
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"""
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# .to() on the tensors handles None correctly
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self.shadow_params = [
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p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
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for p in self.shadow_params
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]
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def state_dict(self) -> dict:
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r"""
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Returns the state of the ExponentialMovingAverage as a dict.
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This method is used by accelerate during checkpointing to save the ema state dict.
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"""
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# Following PyTorch conventions, references to tensors are returned:
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# "returns a reference to the state and not its copy!" -
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# https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict
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return {
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"decay": self.decay,
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"optimization_step": self.optimization_step,
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"shadow_params": self.shadow_params,
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"collected_params": self.collected_params,
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}
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def load_state_dict(self, state_dict: dict) -> None:
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r"""
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Loads the ExponentialMovingAverage state.
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This method is used by accelerate during checkpointing to save the ema state dict.
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Args:
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state_dict (dict): EMA state. Should be an object returned
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from a call to :meth:`state_dict`.
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"""
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# deepcopy, to be consistent with module API
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state_dict = copy.deepcopy(state_dict)
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self.decay = state_dict["decay"]
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if self.decay < 0.0 or self.decay > 1.0:
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raise ValueError("Decay must be between 0 and 1")
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self.optimization_step = state_dict["optimization_step"]
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if not isinstance(self.optimization_step, int):
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raise ValueError("Invalid optimization_step")
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self.shadow_params = state_dict["shadow_params"]
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if not isinstance(self.shadow_params, list):
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raise ValueError("shadow_params must be a list")
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if not all(isinstance(p, torch.Tensor) for p in self.shadow_params):
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raise ValueError("shadow_params must all be Tensors")
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self.collected_params = state_dict["collected_params"]
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if self.collected_params is not None:
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if not isinstance(self.collected_params, list):
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raise ValueError("collected_params must be a list")
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if not all(isinstance(p, torch.Tensor) for p in self.collected_params):
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raise ValueError("collected_params must all be Tensors")
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if len(self.collected_params) != len(self.shadow_params):
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raise ValueError("collected_params and shadow_params must have the same length")
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def main():
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args = parse_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=args.report_to,
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logging_dir=logging_dir,
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)
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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logger.info(accelerator.state, main_process_only=False)
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if accelerator.is_local_main_process:
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_warning()
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diffusers.utils.logging.set_verbosity_info()
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else:
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datasets.utils.logging.set_verbosity_error()
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transformers.utils.logging.set_verbosity_error()
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diffusers.utils.logging.set_verbosity_error()
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# If passed along, set the training seed now.
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if args.seed is not None:
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set_seed(args.seed)
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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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# Load scheduler, tokenizer and models.
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noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
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tokenizer = CLIPTokenizer.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
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)
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text_encoder = CLIPTextModel.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
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)
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vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
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unet = UNet2DConditionModel.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision
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)
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# Freeze vae and text_encoder
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vae.requires_grad_(False)
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text_encoder.requires_grad_(False)
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# Create EMA for the unet.
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if args.use_ema:
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ema_unet = UNet2DConditionModel.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
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)
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ema_unet = EMAModel(ema_unet.parameters())
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if args.enable_xformers_memory_efficient_attention:
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if is_xformers_available():
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unet.enable_xformers_memory_efficient_attention()
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else:
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raise ValueError("xformers is not available. Make sure it is installed correctly")
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if args.gradient_checkpointing:
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unet.enable_gradient_checkpointing()
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# 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(
|
|
unet.parameters(),
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# Get the datasets: you can either provide your own training and evaluation files (see below)
|
|
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
|
|
|
|
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
|
|
# download the dataset.
|
|
if args.dataset_name is not None:
|
|
# Downloading and loading a dataset from the hub.
|
|
dataset = load_dataset(
|
|
args.dataset_name,
|
|
args.dataset_config_name,
|
|
cache_dir=args.cache_dir,
|
|
)
|
|
else:
|
|
data_files = {}
|
|
if args.train_data_dir is not None:
|
|
data_files["train"] = os.path.join(args.train_data_dir, "**")
|
|
dataset = load_dataset(
|
|
"imagefolder",
|
|
data_files=data_files,
|
|
cache_dir=args.cache_dir,
|
|
)
|
|
# See more about loading custom images at
|
|
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
|
|
|
|
# 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)}"
|
|
)
|
|
|
|
# Preprocessing the datasets.
|
|
# We need to tokenize input captions and transform the images.
|
|
def tokenize_captions(examples, is_train=True):
|
|
captions = []
|
|
for caption in examples[caption_column]:
|
|
if isinstance(caption, str):
|
|
captions.append(caption)
|
|
elif isinstance(caption, (list, np.ndarray)):
|
|
# take a random caption if there are multiple
|
|
captions.append(random.choice(caption) if is_train else caption[0])
|
|
else:
|
|
raise ValueError(
|
|
f"Caption column `{caption_column}` should contain either strings or lists of strings."
|
|
)
|
|
inputs = tokenizer(
|
|
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
|
|
)
|
|
return inputs.input_ids
|
|
|
|
# Preprocessing the datasets.
|
|
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(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
|
|
def preprocess_train(examples):
|
|
images = [image.convert("RGB") for image in examples[image_column]]
|
|
examples["pixel_values"] = [train_transforms(image) for image in images]
|
|
examples["input_ids"] = tokenize_captions(examples)
|
|
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):
|
|
pixel_values = torch.stack([example["pixel_values"] for example in examples])
|
|
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
|
input_ids = torch.stack([example["input_ids"] for example in examples])
|
|
return {"pixel_values": pixel_values, "input_ids": input_ids}
|
|
|
|
# DataLoaders creation:
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size
|
|
)
|
|
|
|
# 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`.
|
|
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
unet, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
if args.use_ema:
|
|
accelerator.register_for_checkpointing(ema_unet)
|
|
|
|
# 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)
|
|
if args.use_ema:
|
|
ema_unet.to(accelerator.device)
|
|
|
|
# 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]
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
global_step = int(path.split("-")[1])
|
|
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
resume_step = global_step % num_update_steps_per_epoch
|
|
|
|
# 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()
|
|
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["pixel_values"].to(weight_dtype)).latent_dist.sample()
|
|
latents = latents * 0.18215
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(latents)
|
|
bsz = latents.shape[0]
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(0, noise_scheduler.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["input_ids"])[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(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
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="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_(unet.parameters(), args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
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]}
|
|
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()
|