path: root/train_lora.py

import argparse
import itertools
import math
import datetime
import logging
import json
from pathlib import Path

import torch
import torch.nn.functional as F
import torch.utils.checkpoint

from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import LoggerType, set_seed
from diffusers import AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, UNet2DConditionModel
from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup
from diffusers.training_utils import EMAModel
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from slugify import slugify

from common import load_text_embeddings
from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
from data.csv import CSVDataModule
from training.lora import LoraAttnProcessor
from training.optimization import get_one_cycle_schedule
from training.util import AverageMeter, CheckpointerBase, freeze_params, save_args
from models.clip.prompt import PromptProcessor

logger = get_logger(__name__)


torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True


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,
        help="Path to pretrained model or model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--train_data_file",
        type=str,
        default=None,
        help="A folder containing the training data."
    )
    parser.add_argument(
        "--train_data_template",
        type=str,
        default="template",
    )
    parser.add_argument(
        "--instance_identifier",
        type=str,
        default=None,
        help="A token to use as a placeholder for the concept.",
    )
    parser.add_argument(
        "--class_identifier",
        type=str,
        default=None,
        help="A token to use as a placeholder for the concept.",
    )
    parser.add_argument(
        "--placeholder_token",
        type=str,
        nargs='*',
        default=[],
        help="A token to use as a placeholder for the concept.",
    )
    parser.add_argument(
        "--initializer_token",
        type=str,
        nargs='*',
        default=[],
        help="A token to use as initializer word."
    )
    parser.add_argument(
        "--tag_dropout",
        type=float,
        default=0.1,
        help="Tag dropout probability.",
    )
    parser.add_argument(
        "--num_class_images",
        type=int,
        default=400,
        help="How many class images to generate."
    )
    parser.add_argument(
        "--repeats",
        type=int,
        default=1,
        help="How many times to repeat the training data."
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="output/lora",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument(
        "--embeddings_dir",
        type=str,
        default=None,
        help="The embeddings directory where Textual Inversion embeddings are stored.",
    )
    parser.add_argument(
        "--mode",
        type=str,
        default=None,
        help="A mode to filter the dataset.",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=None,
        help="A seed for reproducible training."
    )
    parser.add_argument(
        "--resolution",
        type=int,
        default=768,
        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",
        action="store_true",
        help="Whether to center crop images before resizing to resolution"
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
            " process."
        ),
    )
    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=2e-6,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--scale_lr",
        action="store_true",
        default=True,
        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="one_cycle",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup", "one_cycle"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_epochs",
        type=int,
        default=10,
        help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument(
        "--lr_cycles",
        type=int,
        default=None,
        help="Number of restart cycles in the lr scheduler (if supported)."
    )
    parser.add_argument(
        "--use_8bit_adam",
        action="store_true",
        default=True,
        help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    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(
        "--mixed_precision",
        type=str,
        default="no",
        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."
        ),
    )
    parser.add_argument(
        "--sample_frequency",
        type=int,
        default=1,
        help="How often to save a checkpoint and sample image",
    )
    parser.add_argument(
        "--sample_image_size",
        type=int,
        default=768,
        help="Size of sample images",
    )
    parser.add_argument(
        "--sample_batches",
        type=int,
        default=1,
        help="Number of sample batches to generate per checkpoint",
    )
    parser.add_argument(
        "--sample_batch_size",
        type=int,
        default=1,
        help="Number of samples to generate per batch",
    )
    parser.add_argument(
        "--valid_set_size",
        type=int,
        default=None,
        help="Number of images in the validation dataset."
    )
    parser.add_argument(
        "--train_batch_size",
        type=int,
        default=1,
        help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument(
        "--sample_steps",
        type=int,
        default=15,
        help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
    )
    parser.add_argument(
        "--prior_loss_weight",
        type=float,
        default=1.0,
        help="The weight of prior preservation loss."
    )
    parser.add_argument(
        "--max_grad_norm",
        default=1.0,
        type=float,
        help="Max gradient norm."
    )
    parser.add_argument(
        "--noise_timesteps",
        type=int,
        default=1000,
    )
    parser.add_argument(
        "--config",
        type=str,
        default=None,
        help="Path to a JSON configuration file containing arguments for invoking this script."
    )

    args = parser.parse_args()
    if args.config is not None:
        with open(args.config, 'rt') as f:
            args = parser.parse_args(
                namespace=argparse.Namespace(**json.load(f)["args"]))

    if args.train_data_file is None:
        raise ValueError("You must specify --train_data_file")

    if args.pretrained_model_name_or_path is None:
        raise ValueError("You must specify --pretrained_model_name_or_path")

    if args.instance_identifier is None:
        raise ValueError("You must specify --instance_identifier")

    if isinstance(args.initializer_token, str):
        args.initializer_token = [args.initializer_token]

    if isinstance(args.placeholder_token, str):
        args.placeholder_token = [args.placeholder_token]

    if len(args.placeholder_token) == 0:
        args.placeholder_token = [f"<*{i}>" for i in range(len(args.initializer_token))]

    if len(args.placeholder_token) != len(args.initializer_token):
        raise ValueError("Number of items in --placeholder_token and --initializer_token must match")

    if args.output_dir is None:
        raise ValueError("You must specify --output_dir")

    return args


class Checkpointer(CheckpointerBase):
    def __init__(
        self,
        datamodule,
        accelerator,
        vae,
        unet,
        tokenizer,
        text_encoder,
        unet_lora,
        scheduler,
        instance_identifier,
        placeholder_token,
        placeholder_token_id,
        output_dir: Path,
        sample_image_size,
        sample_batches,
        sample_batch_size,
        seed
    ):
        super().__init__(
            datamodule=datamodule,
            output_dir=output_dir,
            instance_identifier=instance_identifier,
            placeholder_token=placeholder_token,
            placeholder_token_id=placeholder_token_id,
            sample_image_size=sample_image_size,
            seed=seed or torch.random.seed(),
            sample_batches=sample_batches,
            sample_batch_size=sample_batch_size
        )

        self.accelerator = accelerator
        self.vae = vae
        self.unet = unet
        self.tokenizer = tokenizer
        self.text_encoder = text_encoder
        self.unet_lora = unet_lora
        self.scheduler = scheduler

    @torch.no_grad()
    def save_model(self):
        print("Saving model...")

        unet_lora = self.accelerator.unwrap_model(self.unet_lora)
        unet_lora.save_pretrained(self.output_dir.joinpath("model"))

        del unet_lora

        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    @torch.no_grad()
    def save_samples(self, step, num_inference_steps, guidance_scale=7.5, eta=0.0):
        # Save a sample image
        pipeline = VlpnStableDiffusion(
            text_encoder=self.text_encoder,
            vae=self.vae,
            unet=self.unet,
            tokenizer=self.tokenizer,
            scheduler=self.scheduler,
        ).to(self.accelerator.device)
        pipeline.set_progress_bar_config(dynamic_ncols=True)

        super().save_samples(pipeline, step, num_inference_steps, guidance_scale, eta)

        del pipeline
        del generator
        del stable_latents

        if torch.cuda.is_available():
            torch.cuda.empty_cache()


def main():
    args = parse_args()

    instance_identifier = args.instance_identifier

    if len(args.placeholder_token) != 0:
        instance_identifier = instance_identifier.format(args.placeholder_token[0])

    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
    basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now)
    basepath.mkdir(parents=True, exist_ok=True)

    accelerator = Accelerator(
        log_with=LoggerType.TENSORBOARD,
        logging_dir=f"{basepath}",
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision
    )

    logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG)

    args.seed = args.seed or (torch.random.seed() >> 32)
    set_seed(args.seed)

    save_args(basepath, args)

    # Load the tokenizer and add the placeholder token as a additional special token
    if args.tokenizer_name:
        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
    elif args.pretrained_model_name_or_path:
        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')

    # Load models and create wrapper for stable diffusion
    text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
    unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder='scheduler')
    checkpoint_scheduler = DPMSolverMultistepScheduler.from_pretrained(
        args.pretrained_model_name_or_path, subfolder='scheduler')

    unet_lora = LoraAttnProcessor(
        cross_attention_dim=unet.cross_attention_dim,
        inner_dim=unet.in_channels,
        r=4,
    )

    vae.enable_slicing()
    vae.set_use_memory_efficient_attention_xformers(True)
    unet.set_use_memory_efficient_attention_xformers(True)
    unet.set_attn_processor(unet_lora)

    if args.gradient_checkpointing:
        unet.enable_gradient_checkpointing()
        text_encoder.gradient_checkpointing_enable()

    # Freeze text_encoder and vae
    vae.requires_grad_(False)
    unet.requires_grad_(False)

    if args.embeddings_dir is not None:
        embeddings_dir = Path(args.embeddings_dir)
        if not embeddings_dir.exists() or not embeddings_dir.is_dir():
            raise ValueError("--embeddings_dir must point to an existing directory")
        added_tokens = load_text_embeddings(tokenizer, text_encoder, embeddings_dir)
        print(f"Added {len(added_tokens)} tokens from embeddings dir: {added_tokens}")

    if len(args.placeholder_token) != 0:
        # Convert the initializer_token, placeholder_token to ids
        initializer_token_ids = torch.stack([
            torch.tensor(tokenizer.encode(token, add_special_tokens=False)[:1])
            for token in args.initializer_token
        ])

        num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
        print(f"Added {num_added_tokens} new tokens.")

        placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)

        # Resize the token embeddings as we are adding new special tokens to the tokenizer
        text_encoder.resize_token_embeddings(len(tokenizer))

        token_embeds = text_encoder.get_input_embeddings().weight.data
        original_token_embeds = token_embeds.clone().to(accelerator.device)
        initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids)

        for (token_id, embeddings) in zip(placeholder_token_id, initializer_token_embeddings):
            token_embeds[token_id] = embeddings
    else:
        placeholder_token_id = []

    print(f"Training added text embeddings")

    freeze_params(itertools.chain(
        text_encoder.text_model.encoder.parameters(),
        text_encoder.text_model.final_layer_norm.parameters(),
        text_encoder.text_model.embeddings.position_embedding.parameters(),
    ))

    index_fixed_tokens = torch.arange(len(tokenizer))
    index_fixed_tokens = index_fixed_tokens[~torch.isin(index_fixed_tokens, torch.tensor(placeholder_token_id))]

    prompt_processor = PromptProcessor(tokenizer, text_encoder)

    if args.scale_lr:
        args.learning_rate = (
            args.learning_rate * args.gradient_accumulation_steps *
            args.train_batch_size * accelerator.num_processes
        )

    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
    if args.use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.")

        optimizer_class = bnb.optim.AdamW8bit
    else:
        optimizer_class = torch.optim.AdamW

    # Initialize the optimizer
    optimizer = optimizer_class(
        [
            {
                'params': unet_lora.parameters(),
                'lr': args.learning_rate,
            },
        ],
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )

    weight_dtype = torch.float32
    if args.mixed_precision == "fp16":
        weight_dtype = torch.float16
    elif args.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    def collate_fn(examples):
        prompts = [example["prompts"] for example in examples]
        nprompts = [example["nprompts"] for example in examples]
        input_ids = [example["instance_prompt_ids"] for example in examples]
        pixel_values = [example["instance_images"] for example in examples]

        # concat class and instance examples for prior preservation
        if args.num_class_images != 0 and "class_prompt_ids" in examples[0]:
            input_ids += [example["class_prompt_ids"] for example in examples]
            pixel_values += [example["class_images"] for example in examples]

        pixel_values = torch.stack(pixel_values)
        pixel_values = pixel_values.to(dtype=weight_dtype, memory_format=torch.contiguous_format)

        inputs = prompt_processor.unify_input_ids(input_ids)

        batch = {
            "prompts": prompts,
            "nprompts": nprompts,
            "input_ids": inputs.input_ids,
            "pixel_values": pixel_values,
            "attention_mask": inputs.attention_mask,
        }
        return batch

    datamodule = CSVDataModule(
        data_file=args.train_data_file,
        batch_size=args.train_batch_size,
        prompt_processor=prompt_processor,
        instance_identifier=instance_identifier,
        class_identifier=args.class_identifier,
        class_subdir="cls",
        num_class_images=args.num_class_images,
        size=args.resolution,
        repeats=args.repeats,
        mode=args.mode,
        dropout=args.tag_dropout,
        center_crop=args.center_crop,
        template_key=args.train_data_template,
        valid_set_size=args.valid_set_size,
        num_workers=args.dataloader_num_workers,
        collate_fn=collate_fn
    )

    datamodule.prepare_data()
    datamodule.setup()

    if args.num_class_images != 0:
        missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()]

        if len(missing_data) != 0:
            batched_data = [
                missing_data[i:i+args.sample_batch_size]
                for i in range(0, len(missing_data), args.sample_batch_size)
            ]

            pipeline = VlpnStableDiffusion(
                text_encoder=text_encoder,
                vae=vae,
                unet=unet,
                tokenizer=tokenizer,
                scheduler=checkpoint_scheduler,
            ).to(accelerator.device)
            pipeline.set_progress_bar_config(dynamic_ncols=True)

            with torch.autocast("cuda"), torch.inference_mode():
                for batch in batched_data:
                    image_name = [item.class_image_path for item in batch]
                    prompt = [item.prompt.format(identifier=args.class_identifier) for item in batch]
                    nprompt = [item.nprompt for item in batch]

                    images = pipeline(
                        prompt=prompt,
                        negative_prompt=nprompt,
                        num_inference_steps=args.sample_steps
                    ).images

                    for i, image in enumerate(images):
                        image.save(image_name[i])

            del pipeline

            if torch.cuda.is_available():
                torch.cuda.empty_cache()

    train_dataloader = datamodule.train_dataloader()
    val_dataloader = datamodule.val_dataloader()

    # 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
    num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    warmup_steps = args.lr_warmup_epochs * num_update_steps_per_epoch * args.gradient_accumulation_steps

    if args.lr_scheduler == "one_cycle":
        lr_scheduler = get_one_cycle_schedule(
            optimizer=optimizer,
            num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
        )
    elif args.lr_scheduler == "cosine_with_restarts":
        lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
            optimizer=optimizer,
            num_warmup_steps=warmup_steps,
            num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
            num_cycles=args.lr_cycles or math.ceil(math.sqrt(
                ((args.max_train_steps - warmup_steps) / num_update_steps_per_epoch))),
        )
    else:
        lr_scheduler = get_scheduler(
            args.lr_scheduler,
            optimizer=optimizer,
            num_warmup_steps=warmup_steps,
            num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
        )

    unet_lora, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare(
        unet_lora, optimizer, train_dataloader, val_dataloader, lr_scheduler
    )

    # Move text_encoder and vae to device
    vae.to(accelerator.device, dtype=weight_dtype)
    unet.to(accelerator.device, dtype=weight_dtype)
    text_encoder.to(accelerator.device, dtype=weight_dtype)

    # Keep text_encoder and vae in eval mode as we don't train these
    vae.eval()
    unet.eval()
    text_encoder.eval()

    # 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

    num_val_steps_per_epoch = len(val_dataloader)
    num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
    val_steps = num_val_steps_per_epoch * num_epochs

    # 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:
        config = vars(args).copy()
        config["initializer_token"] = " ".join(config["initializer_token"])
        config["placeholder_token"] = " ".join(config["placeholder_token"])
        accelerator.init_trackers("lora", config=config)

    # Train!
    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num Epochs = {num_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}")
    # Only show the progress bar once on each machine.

    global_step = 0

    avg_loss = AverageMeter()
    avg_acc = AverageMeter()

    avg_loss_val = AverageMeter()
    avg_acc_val = AverageMeter()

    max_acc_val = 0.0

    checkpointer = Checkpointer(
        datamodule=datamodule,
        accelerator=accelerator,
        vae=vae,
        unet=unet,
        tokenizer=tokenizer,
        text_encoder=text_encoder,
        scheduler=checkpoint_scheduler,
        unet_lora=unet_lora,
        output_dir=basepath,
        instance_identifier=instance_identifier,
        placeholder_token=args.placeholder_token,
        placeholder_token_id=placeholder_token_id,
        sample_image_size=args.sample_image_size,
        sample_batch_size=args.sample_batch_size,
        sample_batches=args.sample_batches,
        seed=args.seed
    )

    if accelerator.is_main_process:
        checkpointer.save_samples(0, args.sample_steps)

    local_progress_bar = tqdm(
        range(num_update_steps_per_epoch + num_val_steps_per_epoch),
        disable=not accelerator.is_local_main_process,
        dynamic_ncols=True
    )
    local_progress_bar.set_description("Epoch X / Y")

    global_progress_bar = tqdm(
        range(args.max_train_steps + val_steps),
        disable=not accelerator.is_local_main_process,
        dynamic_ncols=True
    )
    global_progress_bar.set_description("Total progress")

    try:
        for epoch in range(num_epochs):
            local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}")
            local_progress_bar.reset()

            unet_lora.train()

            for step, batch in enumerate(train_dataloader):
                with accelerator.accumulate(unet_lora):
                    # Convert images to latent space
                    latents = vae.encode(batch["pixel_values"]).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.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 = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"])

                    # Predict the noise residual
                    model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample

                    # 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}")

                    if args.num_class_images != 0:
                        # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                        model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
                        target, target_prior = torch.chunk(target, 2, dim=0)

                        # Compute instance loss
                        loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()

                        # Compute prior loss
                        prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")

                        # Add the prior loss to the instance loss.
                        loss = loss + args.prior_loss_weight * prior_loss
                    else:
                        loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

                    acc = (model_pred == latents).float().mean()

                    accelerator.backward(loss)

                    if accelerator.sync_gradients:
                        accelerator.clip_grad_norm_(unet_lora.parameters(), args.max_grad_norm)

                    optimizer.step()
                    if not accelerator.optimizer_step_was_skipped:
                        lr_scheduler.step()
                    optimizer.zero_grad(set_to_none=True)

                    with torch.no_grad():
                        text_encoder.get_input_embeddings(
                        ).weight[index_fixed_tokens] = original_token_embeds[index_fixed_tokens]

                    avg_loss.update(loss.detach_(), bsz)
                    avg_acc.update(acc.detach_(), bsz)

                # Checks if the accelerator has performed an optimization step behind the scenes
                if accelerator.sync_gradients:
                    local_progress_bar.update(1)
                    global_progress_bar.update(1)

                    global_step += 1

                logs = {
                    "train/loss": avg_loss.avg.item(),
                    "train/acc": avg_acc.avg.item(),
                    "train/cur_loss": loss.item(),
                    "train/cur_acc": acc.item(),
                    "lr/unet": lr_scheduler.get_last_lr()[0],
                    "lr/text": lr_scheduler.get_last_lr()[1]
                }

                accelerator.log(logs, step=global_step)

                local_progress_bar.set_postfix(**logs)

                if global_step >= args.max_train_steps:
                    break

            accelerator.wait_for_everyone()

            unet_lora.eval()

            with torch.inference_mode():
                for step, batch in enumerate(val_dataloader):
                    latents = vae.encode(batch["pixel_values"]).latent_dist.sample()
                    latents = latents * 0.18215

                    noise = torch.randn_like(latents)
                    bsz = latents.shape[0]
                    timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps,
                                              (bsz,), device=latents.device)
                    timesteps = timesteps.long()

                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

                    encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"])

                    model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample

                    # 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}")

                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

                    acc = (model_pred == latents).float().mean()

                    avg_loss_val.update(loss.detach_(), bsz)
                    avg_acc_val.update(acc.detach_(), bsz)

                    if accelerator.sync_gradients:
                        local_progress_bar.update(1)
                        global_progress_bar.update(1)

                    logs = {
                        "val/loss": avg_loss_val.avg.item(),
                        "val/acc": avg_acc_val.avg.item(),
                        "val/cur_loss": loss.item(),
                        "val/cur_acc": acc.item(),
                    }
                    local_progress_bar.set_postfix(**logs)

            accelerator.log({
                "val/loss": avg_loss_val.avg.item(),
                "val/acc": avg_acc_val.avg.item(),
            }, step=global_step)

            local_progress_bar.clear()
            global_progress_bar.clear()

            if avg_acc_val.avg.item() > max_acc_val:
                accelerator.print(
                    f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}")
                max_acc_val = avg_acc_val.avg.item()

            if accelerator.is_main_process:
                if (epoch + 1) % args.sample_frequency == 0:
                    checkpointer.save_samples(global_step, args.sample_steps)

        # Create the pipeline using using the trained modules and save it.
        if accelerator.is_main_process:
            print("Finished! Saving final checkpoint and resume state.")
            checkpointer.save_model()

            accelerator.end_training()

    except KeyboardInterrupt:
        if accelerator.is_main_process:
            print("Interrupted, saving checkpoint and resume state...")
            checkpointer.save_model()
            accelerator.end_training()
        quit()


if __name__ == "__main__":
    main()