mmm

#!/usr/bin/env python

coding=utf-8

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"""Fine-tuning script for Stable Diffusion for text2image with support for LoRA."""

import argparse import logging import math import os import random import shutil from contextlib import nullcontext from pathlib import Path

import datasets import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from datasets import load_dataset from huggingface_hub import create_repo, upload_folder from packaging import version from peft import LoraConfig from peft.utils import get_peft_model_state_dict from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer

import diffusers from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, StableDiffusionPipeline, UNet2DConditionModel from diffusers.optimization import get_scheduler from diffusers.training_utils import cast_training_params, compute_snr from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, is_wandb_available from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.torch_utils import is_compiled_module

if is_wandb_available(): import wandb

Will error if the minimal version of diffusers is not installed. Remove at your own risks.

check_min_version("0.34.0.dev0")

logger = get_logger(name, log_level="INFO")

def save_model_card( repo_id: str, images: list = None, base_model: str = None, dataset_name: str = None, repo_folder: str = None, ): img_str = "" if images is not None: for i, image in enumerate(images): image.save(os.path.join(repo_folder, f"image_{i}.png")) img_str += f"\n"

model_description = f"""

LoRA text2image fine-tuning - {repo_id}

These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n {img_str} """

model_card = load_or_create_model_card(
    repo_id_or_path=repo_id,
    from_training=True,
    license="creativeml-openrail-m",
    base_model=base_model,
    model_description=model_description,
    inference=True,
)

tags = [
    "stable-diffusion",
    "stable-diffusion-diffusers",
    "text-to-image",
    "diffusers",
    "diffusers-training",
    "lora",
]
model_card = populate_model_card(model_card, tags=tags)

model_card.save(os.path.join(repo_folder, "README.md"))

def log_validation( pipeline, args, accelerator, epoch, is_final_validation=False, ): logger.info( f"Running validation... \n Generating {args.num_validation_images} images with prompt:" f" {args.validation_prompt}." ) pipeline = pipeline.to(accelerator.device) pipeline.set_progress_bar_config(disable=True) generator = torch.Generator(device=accelerator.device) if args.seed is not None: generator = generator.manual_seed(args.seed) images = [] if torch.backends.mps.is_available(): autocast_ctx = nullcontext() else: autocast_ctx = torch.autocast(accelerator.device.type)

with autocast_ctx:
    for _ in range(args.num_validation_images):
        images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])

for tracker in accelerator.trackers:
    phase_name = "test" if is_final_validation else "validation"
    if tracker.name == "tensorboard":
        np_images = np.stack([np.asarray(img) for img in images])
        tracker.writer.add_images(phase_name, np_images, epoch, dataformats="NHWC")
    if tracker.name == "wandb":
        tracker.log(
            {
                phase_name: [
                    wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
                ]
            }
        )
return images

def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--variant", type=str, default=None, help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", ) parser.add_argument( "--dataset_name", type=str, default=None, help=( "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," " or to a folder containing files that 🤗 Datasets can understand." ), ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The config of the Dataset, leave as None if there's only one config.", ) parser.add_argument( "--train_data_dir", type=str, default=None, help=( "A folder containing the training data. Folder contents must follow the structure described in" " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a metadata.jsonl file" " must exist to provide the captions for the images. Ignored if dataset_name is specified." ), ) parser.add_argument( "--image_column", type=str, default="image", help="The column of the dataset containing an image." ) parser.add_argument( "--caption_column", type=str, default="text", help="The column of the dataset containing a caption or a list of captions.", ) parser.add_argument( "--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference." ) parser.add_argument( "--num_validation_images", type=int, default=4, help="Number of images that should be generated during validation with validation_prompt.", ) parser.add_argument( "--validation_epochs", type=int, default=1, help=( "Run fine-tuning validation every X epochs. The validation process consists of running the prompt" " args.validation_prompt multiple times: args.num_validation_images." ), ) parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--output_dir", type=str, default="sd-model-finetuned-lora", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--resolution", type=int, default=512, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--center_crop", default=False, action="store_true", help=( "Whether to center crop the input images to the resolution. If not set, the images will be randomly" " cropped. The images will be resized to the resolution first before cropping." ), ) parser.add_argument( "--random_flip", action="store_true", help="whether to randomly flip images horizontally", ) parser.add_argument( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) parser.add_argument("--num_train_epochs", type=int, default=100) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument( "--snr_gamma", type=float, default=None, help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. " "More details here: https://arxiv.org/abs/2303.09556.", ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." ), ) parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") parser.add_argument( "--prediction_type", type=str, default=None, help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave None. If left to None the default prediction type of the scheduler: noise_scheduler.config.prediction_type is chosen.", ) parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local output_dir.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "TensorBoard log directory. Will default to" " output_dir/runs/CURRENT_DATETIME_HOSTNAME." ), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the accelerate.launch command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--report_to", type=str, default="tensorboard", help=( 'The integration to report the results and logs to. Supported platforms are "tensorboard"' ' (default), "wandb" and "comet_ml". Use "all" to report to all integrations.' ), ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument( "--checkpointing_steps", type=int, default=500, help=( "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" " training using --resume_from_checkpoint." ), ) parser.add_argument( "--checkpoints_total_limit", type=int, default=None, help=("Max number of checkpoints to store."), ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help=( "Whether training should be resumed from a previous checkpoint. Use a path saved by" ' --checkpointing_steps, or "latest" to automatically select the last available checkpoint.' ), ) parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." ) parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.") parser.add_argument( "--rank", type=int, default=4, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--image_interpolation_mode", type=str, default="lanczos", choices=[ f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("") and not f.endswith("") ], help="The image interpolation method to use for resizing images.", )

args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
    args.local_rank = env_local_rank

# Sanity checks
if args.dataset_name is None and args.train_data_dir is None:
    raise ValueError("Need either a dataset name or a training folder.")

return args

DATASET_NAME_MAPPING = { "lambdalabs/naruto-blip-captions": ("image", "text"), }

def main(): args = parse_args() if args.report_to == "wandb" and args.hub_token is not None: raise ValueError( "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token." " Please use huggingface-cli login to authenticate with the Hub." )

logging_dir = Path(args.output_dir, args.logging_dir)

accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)

accelerator = Accelerator(
    gradient_accumulation_steps=args.gradient_accumulation_steps,
    mixed_precision=args.mixed_precision,
    log_with=args.report_to,
    project_config=accelerator_project_config,
)

# Disable AMP for MPS.
if torch.backends.mps.is_available():
    accelerator.native_amp = False

# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_warning()
    diffusers.utils.logging.set_verbosity_info()
else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()
    diffusers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
    set_seed(args.seed)

# Handle the repository creation
if accelerator.is_main_process:
    if args.output_dir is not None:
        os.makedirs(args.output_dir, exist_ok=True)

    if args.push_to_hub:
        repo_id = create_repo(
            repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
        ).repo_id
# Load scheduler, tokenizer and models.
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(
    args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
text_encoder = CLIPTextModel.from_pretrained(
    args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
)
vae = AutoencoderKL.from_pretrained(
    args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
)
unet = UNet2DConditionModel.from_pretrained(
    args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
# freeze parameters of models to save more memory
unet.requires_grad_(False)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)

# For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
    weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
    weight_dtype = torch.bfloat16

unet_lora_config = LoraConfig(
    r=args.rank,
    lora_alpha=args.rank,
    init_lora_weights="gaussian",
    target_modules=["to_k", "to_q", "to_v", "to_out.0"],
)

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

# Add adapter and make sure the trainable params are in float32.
unet.add_adapter(unet_lora_config)
if args.mixed_precision == "fp16":
    # only upcast trainable parameters (LoRA) into fp32
    cast_training_params(unet, dtype=torch.float32)

if args.enable_xformers_memory_efficient_attention:
    if is_xformers_available():
        import xformers

        xformers_version = version.parse(xformers.__version__)
        if xformers_version == version.parse("0.0.16"):
            logger.warning(
                "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
            )
        unet.enable_xformers_memory_efficient_attention()
    else:
        raise ValueError("xformers is not available. Make sure it is installed correctly")

lora_layers = filter(lambda p: p.requires_grad, unet.parameters())

if args.gradient_checkpointing:
    unet.enable_gradient_checkpointing()

# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
    torch.backends.cuda.matmul.allow_tf32 = True

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

# Initialize the optimizer
if args.use_8bit_adam:
    try:
        import bitsandbytes as bnb
    except ImportError:
        raise ImportError(
            "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
        )

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

optimizer = optimizer_cls(
    lora_layers,
    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,
        data_dir=args.train_data_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

# Get the specified interpolation method from the args
interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)

# Raise an error if the interpolation method is invalid
if interpolation is None:
    raise ValueError(f"Unsupported interpolation mode {args.image_interpolation_mode}.")

# Data preprocessing transformations
train_transforms = transforms.Compose(
    [
        transforms.Resize(args.resolution, interpolation=interpolation),  # Use dynamic interpolation method
        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 unwrap_model(model):
    model = accelerator.unwrap_model(model)
    model = model._orig_mod if is_compiled_module(model) else model
    return model

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,
    num_workers=args.dataloader_num_workers,
)

# Scheduler and math around the number of training steps.
# Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
num_warmup_steps_for_scheduler = args.lr_warmup_steps * accelerator.num_processes
if args.max_train_steps is None:
    len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)
    num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)
    num_training_steps_for_scheduler = (
        args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes
    )
else:
    num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes

lr_scheduler = get_scheduler(
    args.lr_scheduler,
    optimizer=optimizer,
    num_warmup_steps=num_warmup_steps_for_scheduler,
    num_training_steps=num_training_steps_for_scheduler,
)

# Prepare everything with our `accelerator`.
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
    unet, optimizer, train_dataloader, lr_scheduler
)

# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
    args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    if num_training_steps_for_scheduler != args.max_train_steps * accelerator.num_processes:
        logger.warning(
            f"The length of the 'train_dataloader' after 'accelerator.prepare' ({len(train_dataloader)}) does not match "
            f"the expected length ({len_train_dataloader_after_sharding}) when the learning rate scheduler was created. "
            f"This inconsistency may result in the learning rate scheduler not functioning properly."
        )
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
    accelerator.init_trackers("text2image-fine-tune", config=vars(args))

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

logger.info("***** Running training *****")
logger.info(f"  Num examples = {len(train_dataset)}")
logger.info(f"  Num Epochs = {args.num_train_epochs}")
logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f"  Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0

# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
    if args.resume_from_checkpoint != "latest":
        path = os.path.basename(args.resume_from_checkpoint)
    else:
        # Get the most recent checkpoint
        dirs = os.listdir(args.output_dir)
        dirs = [d for d in dirs if d.startswith("checkpoint")]
        dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
        path = dirs[-1] if len(dirs) > 0 else None

    if path is None:
        accelerator.print(
            f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
        )
        args.resume_from_checkpoint = None
        initial_global_step = 0
    else:
        accelerator.print(f"Resuming from checkpoint {path}")
        accelerator.load_state(os.path.join(args.output_dir, path))
        global_step = int(path.split("-")[1])

        initial_global_step = global_step
        first_epoch = global_step // num_update_steps_per_epoch
else:
    initial_global_step = 0

progress_bar = tqdm(
    range(0, args.max_train_steps),
    initial=initial_global_step,
    desc="Steps",
    # Only show the progress bar once on each machine.
    disable=not accelerator.is_local_main_process,
)

for epoch in range(first_epoch, args.num_train_epochs):
    unet.train()
    train_loss = 0.0
    for step, batch in enumerate(train_dataloader):
        with accelerator.accumulate(unet):
            # Convert images to latent space
            latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
            latents = latents * vae.config.scaling_factor

            # Sample noise that we'll add to the latents
            noise = torch.randn_like(latents)
            if args.noise_offset:
                # https://www.crosslabs.org//blog/diffusion-with-offset-noise
                noise += args.noise_offset * torch.randn(
                    (latents.shape[0], latents.shape[1], 1, 1), device=latents.device
                )

            bsz = latents.shape[0]
            # Sample a random timestep for each image
            timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
            timesteps = timesteps.long()

            # Add noise to the latents according to the noise magnitude at each timestep
            # (this is the forward diffusion process)
            noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)

            # Get the text embedding for conditioning
            encoder_hidden_states = text_encoder(batch["input_ids"], return_dict=False)[0]

            # Get the target for loss depending on the prediction type
            if args.prediction_type is not None:
                # set prediction_type of scheduler if defined
                noise_scheduler.register_to_config(prediction_type=args.prediction_type)

            if noise_scheduler.config.prediction_type == "epsilon":
                target = noise
            elif noise_scheduler.config.prediction_type == "v_prediction":
                target = noise_scheduler.get_velocity(latents, noise, timesteps)
            else:
                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")

            # Predict the noise residual and compute loss
            model_pred = unet(noisy_latents, timesteps, encoder_hidden_states, return_dict=False)[0]

            if args.snr_gamma is None:
                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
            else:
                # Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
                # Since we predict the noise instead of x_0, the original formulation is slightly changed.
                # This is discussed in Section 4.2 of the same paper.
                snr = compute_snr(noise_scheduler, timesteps)
                mse_loss_weights = torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(
                    dim=1
                )[0]
                if noise_scheduler.config.prediction_type == "epsilon":
                    mse_loss_weights = mse_loss_weights / snr
                elif noise_scheduler.config.prediction_type == "v_prediction":
                    mse_loss_weights = mse_loss_weights / (snr + 1)

                loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
                loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
                loss = loss.mean()

            # Gather the losses across all processes for logging (if we use distributed training).
            avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
            train_loss += avg_loss.item() / args.gradient_accumulation_steps

            # Backpropagate
            accelerator.backward(loss)
            if accelerator.sync_gradients:
                params_to_clip = lora_layers
                accelerator.clip_grad_norm_(params_to_clip, 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:
            progress_bar.update(1)
            global_step += 1
            accelerator.log({"train_loss": train_loss}, step=global_step)
            train_loss = 0.0

            if global_step % args.checkpointing_steps == 0:
                if accelerator.is_main_process:
                    # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
                    if args.checkpoints_total_limit is not None:
                        checkpoints = os.listdir(args.output_dir)
                        checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
                        checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))

                        # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
                        if len(checkpoints) >= args.checkpoints_total_limit:
                            num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
                            removing_checkpoints = checkpoints[0:num_to_remove]

                            logger.info(
                                f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
                            )
                            logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")

                            for removing_checkpoint in removing_checkpoints:
                                removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
                                shutil.rmtree(removing_checkpoint)

                    save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
                    accelerator.save_state(save_path)

                    unwrapped_unet = unwrap_model(unet)
                    unet_lora_state_dict = convert_state_dict_to_diffusers(
                        get_peft_model_state_dict(unwrapped_unet)
                    )

                    StableDiffusionPipeline.save_lora_weights(
                        save_directory=save_path,
                        unet_lora_layers=unet_lora_state_dict,
                        safe_serialization=True,
                    )

                    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

    if accelerator.is_main_process:
        if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
            # create pipeline
            pipeline = DiffusionPipeline.from_pretrained(
                args.pretrained_model_name_or_path,
                unet=unwrap_model(unet),
                revision=args.revision,
                variant=args.variant,
                torch_dtype=weight_dtype,
            )
            images = log_validation(pipeline, args, accelerator, epoch)

            del pipeline
            torch.cuda.empty_cache()

# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
    unet = unet.to(torch.float32)

    unwrapped_unet = unwrap_model(unet)
    unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unwrapped_unet))
    StableDiffusionPipeline.save_lora_weights(
        save_directory=args.output_dir,
        unet_lora_layers=unet_lora_state_dict,
        safe_serialization=True,
    )

    # Final inference
    # Load previous pipeline
    if args.validation_prompt is not None:
        pipeline = DiffusionPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
            revision=args.revision,
            variant=args.variant,
            torch_dtype=weight_dtype,
        )

        # load attention processors
        pipeline.load_lora_weights(args.output_dir)

        # run inference
        images = log_validation(pipeline, args, accelerator, epoch, is_final_validation=True)

    if args.push_to_hub:
        save_model_card(
            repo_id,
            images=images,
            base_model=args.pretrained_model_name_or_path,
            dataset_name=args.dataset_name,
            repo_folder=args.output_dir,
        )
        upload_folder(
            repo_id=repo_id,
            folder_path=args.output_dir,
            commit_message="End of training",
            ignore_patterns=["step_*", "epoch_*"],
        )

accelerator.end_training()

if name == "main": main()