train.py

#!/usr/bin/env python
# coding=utf-8

import argparse
import contextlib
import gc
import logging
import math
import os
import sys
import random
import shutil
import importlib
from pathlib import Path

import accelerate
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torch.utils.data
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from datasets import Image as ImageFeature, load_dataset
from huggingface_hub import create_repo, upload_folder, get_token
from packaging import version
from PIL import Image
from peft import LoraConfig
from peft.utils import get_peft_model_state_dict, set_peft_model_state_dict
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig

import diffusers
from diffusers import (
    AutoencoderKL,
    DDPMScheduler,
    UniPCMultistepScheduler,
)
from diffusers.optimization import get_scheduler
from diffusers.training_utils import cast_training_params
from diffusers.utils import check_min_version, convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft, 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
from diffusers.loaders import LoraLoaderMixin

from model import UNet2DConditionModelEx
from pipeline import StableDiffusionControlLoraV3Pipeline


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("0.29.0")

logger = get_logger(__name__)


def image_grid(imgs, rows, cols):
    assert len(imgs) == rows * cols

    w, h = imgs[0].size
    grid = Image.new("RGB", size=(cols * w, rows * h))

    for i, img in enumerate(imgs):
        grid.paste(img, box=(i % cols * w, i // cols * h))
    return grid


def log_validation(
    vae, text_encoder, tokenizer, unet, args, accelerator, weight_dtype, step, is_final_validation=False
):
    logger.info("Running validation... ")

    if not is_final_validation:
        unet = accelerator.unwrap_model(unet)
    else:
        unet: UNet2DConditionModelEx = UNet2DConditionModelEx.from_pretrained(
            args.pretrained_model_name_or_path,
            subfolder="unet",
            torch_dtype=weight_dtype,)
        unet = unet.add_extra_conditions([args.lora_adapter_name])

    pipeline = StableDiffusionControlLoraV3Pipeline.from_pretrained(
        args.pretrained_model_name_or_path,
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=unet,
        safety_checker=None,
        revision=args.revision,
        variant=args.variant,
        torch_dtype=weight_dtype,
    )
    
    if is_final_validation:
        # load attention processors
        pipeline.load_lora_weights(args.output_dir, adapter_name=args.lora_adapter_name)
    
    pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)
    pipeline = pipeline.to(accelerator.device)
    pipeline.set_progress_bar_config(disable=True)

    if args.enable_xformers_memory_efficient_attention:
        pipeline.enable_xformers_memory_efficient_attention()

    if args.seed is None:
        generator = None
    else:
        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)

    if len(args.validation_image) == len(args.validation_prompt):
        validation_images = args.validation_image
        validation_prompts = args.validation_prompt
    elif len(args.validation_image) == 1:
        validation_images = args.validation_image * len(args.validation_prompt)
        validation_prompts = args.validation_prompt
    elif len(args.validation_prompt) == 1:
        validation_images = args.validation_image
        validation_prompts = args.validation_prompt * len(args.validation_image)
    else:
        raise ValueError(
            "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`"
        )

    image_logs = []
    inference_ctx = contextlib.nullcontext() if is_final_validation else torch.autocast("cuda")

    for validation_prompt, validation_image in zip(validation_prompts, validation_images):
        validation_image = Image.open(validation_image).convert("RGB")

        images = []

        for _ in range(args.num_validation_images):
            with inference_ctx:
                image = pipeline(
                    validation_prompt, validation_image, num_inference_steps=20, generator=generator
                ).images[0]

            images.append(image)

        image_logs.append(
            {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt}
        )

    tracker_key = "test" if is_final_validation else "validation"
    for tracker in accelerator.trackers:
        if tracker.name == "tensorboard":
            for log in image_logs:
                images = log["images"]
                validation_prompt = log["validation_prompt"]
                validation_image = log["validation_image"]

                formatted_images = []

                formatted_images.append(np.asarray(validation_image))

                for image in images:
                    formatted_images.append(np.asarray(image))

                formatted_images = np.stack(formatted_images)

                tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
        elif tracker.name == "wandb":
            formatted_images = []

            for log in image_logs:
                images = log["images"]
                validation_prompt = log["validation_prompt"]
                validation_image = log["validation_image"]

                formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning"))

                for image in images:
                    image = wandb.Image(image, caption=validation_prompt)
                    formatted_images.append(image)

            tracker.log({tracker_key: formatted_images})
        else:
            logger.warning(f"image logging not implemented for {tracker.name}")

        del pipeline
        gc.collect()
        torch.cuda.empty_cache()

        return image_logs


def import_model_class_from_model_name_or_path(
    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
    text_encoder_config = PretrainedConfig.from_pretrained(
        pretrained_model_name_or_path, subfolder=subfolder, revision=revision
    )
    model_class = text_encoder_config.architectures[0]

    if model_class == "CLIPTextModel":
        from transformers import CLIPTextModel

        return CLIPTextModel
    elif model_class == "CLIPTextModelWithProjection":
        from transformers import CLIPTextModelWithProjection

        return CLIPTextModelWithProjection
    else:
        raise ValueError(f"{model_class} is not supported.")


def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
    img_str = ""
    if image_logs is not None:
        img_str = "You can find some example images below.\n\n"
        for i, log in enumerate(image_logs):
            images = log["images"]
            validation_prompt = log["validation_prompt"]
            validation_image = log["validation_image"]
            validation_image.save(os.path.join(repo_folder, "image_control.png"))
            img_str += f"prompt: {validation_prompt}\n"
            images = [validation_image] + images
            image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
            img_str += f"![images_{i})](./images_{i}.png)\n"

    model_description = f"""
# sd-control-lora-v3-{repo_id.split("sd-control-lora-v3-")[-1] if "sd-control-lora-v3-" in repo_id else repo_id}

These are control-lora-v3 weights trained on {base_model} with new type of conditioning.
{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",
        "controlnet",
        "control-lora-v3",
        "diffusers-training",
    ]
    model_card = populate_model_card(model_card, tags=tags)

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


def get_pissa_init(unet: UNet2DConditionModelEx):
    return { k: v.clone().float() for k, v in unet.named_parameters() if ".lora_" in k }


def convert_pissa_to_lora(unet: UNet2DConditionModelEx, pissa_init: dict):
    for name, module in unet.named_modules():
        if ".lora_A." in name and isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
            module.weight.data = torch.cat(
                [module.weight.data, pissa_init[name + '.weight']], dim=0
            )
            if isinstance(module, torch.nn.Linear):
                module.out_features *= 2
            elif isinstance(module, torch.nn.Conv2d):
                module.out_channels *= 2
        if ".lora_B." in name and isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
            module.weight.data = torch.cat(
                [module.weight.data, -pissa_init[name + '.weight']], dim=1
            )
            if isinstance(module, torch.nn.Linear):
                module.in_features *= 2
            elif isinstance(module, torch.nn.Conv2d):
                module.in_channels *= 2
    return unet


def convert_lora_to_pissa(unet: UNet2DConditionModelEx):
    for name, module in unet.named_modules():
        if ".lora_A." in name and isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
            module.weight.data = module.weight.data[:module.weight.data.shape[0]//2]
            if isinstance(module, torch.nn.Linear):
                module.out_features //= 2
            elif isinstance(module, torch.nn.Conv2d):
                module.out_channels //= 2
        if ".lora_B." in name and isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
            module.weight.data = module.weight.data[:,:module.weight.data.shape[1]//2]
            if isinstance(module, torch.nn.Linear):
                module.in_features //= 2
            elif isinstance(module, torch.nn.Conv2d):
                module.in_channels //= 2
    return unet


def parse_args(input_args=None):
    parser = argparse.ArgumentParser(description="Simple example of a ControlNet 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(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="control_lora_v3-model",
        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(
        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument("--num_train_epochs", type=int, default=1)
    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(
        "--checkpointing_steps",
        type=int,
        default=500,
        help=(
            "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
            "instructions."
        ),
    )
    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(
        "--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=5e-5,
        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(
        "--lr_num_cycles",
        type=int,
        default=1,
        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
    )
    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
    parser.add_argument(
        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=4,
        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(
        "--hub_model_id",
        type=str,
        default=None,
        help="The name of the repository to keep in sync with the local `output_dir`.",
    )
    parser.add_argument(
        "--logging_dir",
        type=str,
        default="logs",
        help=(
            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
        ),
    )
    parser.add_argument(
        "--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(
        "--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(
        "--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(
        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
    )
    parser.add_argument(
        "--rank",
        type=int,
        default=4,
        help=("The dimension of the LoRA update matrices."),
    )
    parser.add_argument(
        "--loraplus_lr_ratio",
        type=float,
        default=1.0,
        help=("The loraplus learning rate ratio lr_B / lr_A."),
    )
    parser.add_argument(
        "--lora_adapter_name",
        type=str,
        default="default",
        help=("The name of the LORA adapter to add.."),
    )
    parser.add_argument(
        "--init_lora_weights",
        type=str,
        default="gaussian",
        choices=["gaussian", "pissa", *[f"pissa_niter_{i}" for i in range(1,16+1)]],
        help=("How to initialize the weights of the adapter layers, one of"
              " ['gaussian', 'pissa', 'pissa_niter_[number of iters]']."
              " default: gaussian"),
    )
    parser.add_argument(
        "--half_or_full_lora",
        type=str,
        default="half_skip_attn",
        choices=["half", "full", "half_skip_attn", "full_skip_attn"],
        help=("Half or full of the unet linear and convolutional layers are fine-tuned using LoRA."),
    )
    parser.add_argument(
        "--extra_lora_rank_modules",
        type=str,
        default=[],
        nargs="+",
        help=(
            "The layers need different LoRA ranks."
        ),
    )
    parser.add_argument(
        "--extra_lora_ranks",
        type=int,
        default=[],
        nargs="+",
        help=(
            "The LoRA ranks of the extra layers are set."
        ),
    )
    parser.add_argument(
        "--set_grads_to_none",
        action="store_true",
        help=(
            "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
            " behaviors, so disable this argument if it causes any problems. More info:"
            " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
        ),
    )
    parser.add_argument(
        "--dataset_script_path",
        type=str,
        default=None,
        help=(
            "The script path where the custom torch.utils.data.Dataset subclass is located."
        ),
    )
    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 the target image."
    )
    parser.add_argument(
        "--conditioning_image_column",
        type=str,
        default="conditioning_image",
        help="The column of the dataset containing the control_lora_v3 conditioning image.",
    )
    parser.add_argument(
        "--caption_column",
        type=str,
        default="text",
        help="The column of the dataset containing a caption or a list of captions.",
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--proportion_empty_prompts",
        type=float,
        default=0,
        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
    )
    parser.add_argument(
        "--validation_prompt",
        type=str,
        default=None,
        nargs="+",
        help=(
            "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`."
            " Provide either a matching number of `--validation_image`s, a single `--validation_image`"
            " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s."
        ),
    )
    parser.add_argument(
        "--validation_image",
        type=str,
        default=None,
        nargs="+",
        help=(
            "A set of paths to the control_lora_v3 conditioning image be evaluated every `--validation_steps`"
            " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a"
            " a single `--validation_prompt` to be used with all `--validation_image`s, or a single"
            " `--validation_image` that will be used with all `--validation_prompt`s."
        ),
    )
    parser.add_argument(
        "--num_validation_images",
        type=int,
        default=4,
        help="Number of images to be generated for each `--validation_image`, `--validation_prompt` pair",
    )
    parser.add_argument(
        "--validation_steps",
        type=int,
        default=100,
        help=(
            "Run validation every X steps. Validation consists of running the prompt"
            " `args.validation_prompt` multiple times: `args.num_validation_images`"
            " and logging the images."
        ),
    )
    parser.add_argument(
        "--tracker_project_name",
        type=str,
        default="train_control_lora_v3",
        help=(
            "The `project_name` argument passed to Accelerator.init_trackers for"
            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
        ),
    )

    if input_args is not None:
        args = parser.parse_args(input_args)
    else:
        args = parser.parse_args()

    if args.dataset_name is None and args.train_data_dir is None and args.dataset_script_path is None:
        raise ValueError("Specify either `--dataset_name` or `--train_data_dir` or `dataset_script_path`")

    _data_count = 0
    _data_count += 0 if args.dataset_name is None else 1
    _data_count += 0 if args.train_data_dir is None else 1
    _data_count += 0 if args.dataset_script_path is None else 1
    if _data_count > 1:
        raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir` or `dataset_script_path`")

    if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")

    if args.validation_prompt is not None and args.validation_image is None:
        raise ValueError("`--validation_image` must be set if `--validation_prompt` is set")

    if args.validation_prompt is None and args.validation_image is not None:
        raise ValueError("`--validation_prompt` must be set if `--validation_image` is set")

    if (
        args.validation_image is not None
        and args.validation_prompt is not None
        and len(args.validation_image) != 1
        and len(args.validation_prompt) != 1
        and len(args.validation_image) != len(args.validation_prompt)
    ):
        raise ValueError(
            "Must provide either 1 `--validation_image`, 1 `--validation_prompt`,"
            " or the same number of `--validation_prompt`s and `--validation_image`s"
        )

    if args.resolution % 8 != 0:
        raise ValueError(
            "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the control_lora_v3 encoder."
        )

    return args


def _is_py_script(data_path):
    return data_path[-3:] == ".py"


def _import_module_from_path(module_name, file_path):
    spec = importlib.util.spec_from_file_location(module_name, file_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    return module


def _get_data_class_from_module(module):
    names = dir(module)
    for name in names:
        attr = getattr(module, name)
        try:
            if issubclass(attr, torch.utils.data.Dataset):
                return attr
        except TypeError:
            # attr may not be a class
            pass
    raise ValueError("Cannot find a subclass of torch.utils.data.Dataset")


class TrainDataset(torch.utils.data.Dataset):
    def __init__(self, args, tokenizer, accelerator):
        self.args = args
        self.tokenizer = tokenizer
        
        # 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).
        
        if args.dataset_script_path is not None and _is_py_script(args.dataset_script_path):
            self.dataset = _get_data_class_from_module(
                _import_module_from_path(
                    "custom_dataset", 
                    args.dataset_script_path)
            )(args=args, tokenizer=tokenizer)
            return
        
        # 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:
            if args.train_data_dir is not None:
                dataset = load_dataset(
                    args.train_data_dir,
                    cache_dir=args.cache_dir,
                )
            # See more about loading custom images at
            # https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script

        # Preprocessing the datasets.
        # We need to tokenize inputs and targets.
        column_names = dataset["train"].column_names

        # 6. Get the column names for input/target.
        if args.image_column is None:
            self.image_column = column_names[0]
            logger.info(f"image column defaulting to {self.image_column}")
        else:
            self.image_column = args.image_column
            if self.image_column not in column_names:
                raise ValueError(
                    f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
                )

        if args.caption_column is None:
            self.caption_column = column_names[1]
            logger.info(f"caption column defaulting to {self.caption_column}")
        else:
            self.caption_column = args.caption_column
            if self.caption_column not in column_names:
                raise ValueError(
                    f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
                )

        if args.conditioning_image_column is None:
            self.conditioning_image_column = column_names[2]
            logger.info(f"conditioning image column defaulting to {self.conditioning_image_column}")
        else:
            self.conditioning_image_column = args.conditioning_image_column
            if self.conditioning_image_column not in column_names:
                raise ValueError(
                    f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
                )


        self.image_transforms = transforms.Compose(
            [
                transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
                transforms.CenterCrop(args.resolution),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

        self.conditioning_image_transforms = transforms.Compose(
            [
                transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
                transforms.CenterCrop(args.resolution),
                transforms.ToTensor(),
                transforms.Normalize([0.5], [0.5]),
            ]
        )

        # 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"]
        # train_dataset = train_dataset.cast_column("image", ImageFeature())
        # train_dataset = train_dataset.cast_column("guide", ImageFeature())
        train_dataset = train_dataset.with_transform(self.preprocess_train)

        self.dataset = train_dataset

    def tokenize_captions(self, examples, is_train=True):
        captions = []
        for caption in examples[self.caption_column]:
            if random.random() < self.args.proportion_empty_prompts:
                captions.append("")
            elif 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 `{self.caption_column}` should contain either strings or lists of strings."
                )
        inputs = self.tokenizer(
            captions, max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
        )
        return inputs.input_ids
    
    def preprocess_train(self, examples):
        image_0 = examples[self.image_column][0]
        if isinstance(image_0, str):
            if sys.platform != 'win32' and any([image_0.startswith(chr(i) + ':') for i in range(65, 123)]): # "A: ~ z:"
                examples[self.image_column] = [
                    f'/mnt/{image[0].lower()}/{image[2:]}' 
                    for image in examples[self.image_column]
                ]
            examples[self.image_column] = [
                Image.open(image.replace("\\", "/")) 
                for image in examples[self.image_column]
            ]
        images = [(image).convert("RGB") for image in examples[self.image_column]]
        images = [self.image_transforms(image) for image in images]

        cond_0 = examples[self.conditioning_image_column][0]
        if isinstance(cond_0, str):
            if sys.platform != 'win32' and any([cond_0.startswith(chr(i) + ':') for i in range(65, 123)]): # "A: ~ z:"
                examples[self.conditioning_image_column] = [
                    f'/mnt/{image[0].lower()}/{image[2:]}' 
                    for image in examples[self.conditioning_image_column]
                ]
            examples[self.conditioning_image_column] = [
                Image.open(image.replace("\\", "/")) 
                for image in examples[self.conditioning_image_column]
            ]
        conditioning_images = [image.convert("RGB") for image in examples[self.conditioning_image_column]]
        conditioning_images = [self.conditioning_image_transforms(image) for image in conditioning_images]

        examples["pixel_values"] = images
        examples["conditioning_pixel_values"] = conditioning_images
        examples["input_ids"] = self.tokenize_captions(examples)

        return examples
        
    def __len__(self):
        return len(self.dataset)
    
    def __getitem__(self, idx):
        return self.dataset[idx]


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()

    conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples])
    conditioning_pixel_values = conditioning_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,
        "conditioning_pixel_values": conditioning_pixel_values,
        "input_ids": input_ids,
    }


def main(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."
        )
    hub_token = args.hub_token or get_token()

    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:
        transformers.utils.logging.set_verbosity_warning()
        diffusers.utils.logging.set_verbosity_info()
    else:
        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=hub_token
            ).repo_id

    # Load the tokenizer
    if args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
    elif args.pretrained_model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            args.pretrained_model_name_or_path,
            subfolder="tokenizer",
            revision=args.revision,
            use_fast=False,
        )

    # import correct text encoder class
    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)

    # Load scheduler and models
    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
    text_encoder = text_encoder_cls.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
    )
    vae = AutoencoderKL.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
    )
    
    logger.info("Converting the default unet to control-lora-v3 compatible unet")
    unet: UNet2DConditionModelEx = UNet2DConditionModelEx.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
    )
    unet = unet.add_extra_conditions([args.lora_adapter_name])

    # Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files)
    def unwrap_model(model):
        model = accelerator.unwrap_model(model)
        model = model._orig_mod if is_compiled_module(model) else model
        return model

    vae.requires_grad_(False)
    unet.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

    # Freeze the control_lora_v3 main parameters before adding adapters
    for param in unet.parameters():
        param.requires_grad_(False)

    if args.half_or_full_lora == "half":
        lora_target_modules = unet.default_half_lora_target_modules
    elif args.half_or_full_lora == "full":
        lora_target_modules = unet.default_full_lora_target_modules
    elif args.half_or_full_lora == "half_skip_attn":
        lora_target_modules = unet.default_half_skip_attn_lora_target_modules
    elif args.half_or_full_lora == "full_skip_attn":
        lora_target_modules = unet.default_full_skip_attn_lora_target_modules
    lora_config = LoraConfig(
        r=args.rank,
        lora_alpha=args.rank,
        init_lora_weights=args.init_lora_weights,
        target_modules=lora_target_modules,
        rank_pattern={
            extra_lora_rank_module: extra_lora_rank 
            for extra_lora_rank_module, extra_lora_rank in zip(args.extra_lora_rank_modules, args.extra_lora_ranks)},
        alpha_pattern={
            extra_lora_rank_module: extra_lora_rank
            for extra_lora_rank_module, extra_lora_rank in zip(args.extra_lora_rank_modules, args.extra_lora_ranks)},
    )
    
    # 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(lora_config, adapter_name=args.lora_adapter_name)
    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_A_layers = [p for n, p in unet.named_parameters() if ".lora_A." in n and p.requires_grad]
    lora_B_layers = [p for n, p in unet.named_parameters() if ".lora_B." in n and p.requires_grad]
    if "pissa" in args.init_lora_weights:
        pissa_init = get_pissa_init(unet)
    
    # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
    def save_model_hook(models, weights, output_dir):
        if accelerator.is_main_process:
            # Just the unet attn processor layers
            unet_lora_layers_to_save = None
            
            for model in models:
                unwrapped_model = unwrap_model(model)
                if isinstance(unwrapped_model, type(unwrap_model(unet))):
                    if "pissa" in args.init_lora_weights:
                        unwrapped_model = convert_pissa_to_lora(unwrapped_model, pissa_init)
                    unet_lora_layers_to_save = convert_state_dict_to_diffusers(
                        get_peft_model_state_dict(unwrapped_model, adapter_name=args.lora_adapter_name)
                    )
                    if "pissa" in args.init_lora_weights:
                        unwrapped_model = convert_lora_to_pissa(unwrapped_model)
                else:
                    raise ValueError(f"unexpected save model: {model.__class__}")

                # make sure to pop weight so that corresponding model is not saved again
                if weights:
                    weights.pop()
            
            StableDiffusionControlLoraV3Pipeline.save_lora_weights(
                output_dir,
                unet_lora_layers=unet_lora_layers_to_save,
                safe_serialization=True
            )

    def load_model_hook(models, input_dir):
        unet_ = None

        while len(models) > 0:
            model = models.pop()

            if isinstance(model, type(unwrap_model(unet))):
                unet_ = model
            else:
                raise ValueError(f"unexpected save model: {model.__class__}")

        lora_state_dict, _ = LoraLoaderMixin.lora_state_dict(input_dir)
        unet_state_dict = {f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")}
        unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
        if "pissa" in args.init_lora_weights:
            unet_state_dict.update({ k: v[:v.shape[0]//2].clone() for k, v in unet_state_dict.items() if ".lora_A." in k })
            unet_state_dict.update({ k: v[:,:v.shape[1]//2].clone() for k, v in unet_state_dict.items() if ".lora_B." in k })
        incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name=args.lora_adapter_name)
        if incompatible_keys is not None:
            # check only for unexpected keys
            unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
            if unexpected_keys:
                logger.warning(
                    f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
                    f" {unexpected_keys}. "
                )

        # Make sure the trainable params are in float32. This is again needed since the base models
        # are in `weight_dtype`. More details:
        # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
        if args.mixed_precision == "fp16":
            models = [unet_]
            cast_training_params(models, dtype=torch.float32)
            
    accelerator.register_save_state_pre_hook(save_model_hook)
    accelerator.register_load_state_pre_hook(load_model_hook)
    
    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
        )

    # 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

    # Optimizer creation
    optimizer = optimizer_class(
        [
            {
                "params": lora_A_layers,
                "weight_decay": args.adam_weight_decay,
                "lr": args.learning_rate,
            },
            {
                "params": lora_B_layers,
                "weight_decay": args.adam_weight_decay,
                "lr": args.learning_rate * args.loraplus_lr_ratio,
            },
        ],
        betas=(args.adam_beta1, args.adam_beta2),
        eps=args.adam_epsilon,
    )

    train_dataset = TrainDataset(args, tokenizer, accelerator)

    train_dataloader = torch.utils.data.DataLoader(
        train_dataset,
        shuffle=True,
        collate_fn=collate_fn,
        batch_size=args.train_batch_size,
        num_workers=args.dataloader_num_workers,
    )

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
        num_training_steps=args.max_train_steps * accelerator.num_processes,
        num_cycles=args.lr_num_cycles,
        power=args.lr_power,
    )

    # 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 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:
        tracker_config = dict(vars(args))

        # tensorboard cannot handle list types for config
        tracker_config.pop("validation_prompt")
        tracker_config.pop("validation_image")
        tracker_config.pop("extra_lora_rank_modules")
        tracker_config.pop("extra_lora_ranks")

        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)

    # 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 batches each epoch = {len(train_dataloader)}")
    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,
    )

    image_logs = None
    for epoch in range(first_epoch, args.num_train_epochs):
        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)
                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]

                control_lora_v3_image = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
                control_latents = vae.encode(control_lora_v3_image).latent_dist.sample()
                control_latents = control_latents * vae.config.scaling_factor

                # Predict the noise residual
                model_pred = unet(
                    noisy_latents,
                    timesteps,
                    encoder_hidden_states=encoder_hidden_states,
                    extra_conditions=control_latents,
                    return_dict=False,
                )[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}")
                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    params_to_clip = lora_A_layers + lora_B_layers
                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad(set_to_none=args.set_grads_to_none)

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1

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

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

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

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

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

                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
                        image_logs = log_validation(
                            vae,
                            text_encoder,
                            tokenizer,
                            unet,
                            args,
                            accelerator,
                            weight_dtype,
                            global_step,
                        )

            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)

            if global_step >= args.max_train_steps:
                break

    # Create the pipeline using using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        unet = unwrap_model(unet)
        if "pissa" in args.init_lora_weights:
            unet = convert_pissa_to_lora(unet, pissa_init)
        unet_lora_state_dict = convert_state_dict_to_diffusers(
            get_peft_model_state_dict(unet, adapter_name=args.lora_adapter_name)
        )
        if "pissa" in args.init_lora_weights:
            unet = convert_lora_to_pissa(unet)
        StableDiffusionControlLoraV3Pipeline.save_lora_weights(
            save_directory=args.output_dir,
            unet_lora_layers=unet_lora_state_dict,
            safe_serialization=True,
        )
        
        del unet
        unet = None
        gc.collect()
        torch.cuda.empty_cache()
        
        # Run a final round of validation.
        image_logs = None
        if args.validation_prompt is not None:
            image_logs = log_validation(
                vae=vae,
                text_encoder=text_encoder,
                tokenizer=tokenizer,
                unet=unet,
                args=args,
                accelerator=accelerator,
                weight_dtype=weight_dtype,
                step=global_step,
                is_final_validation=True,
            )

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

    accelerator.end_training()


if __name__ == "__main__":
    args = parse_args()
    main(args)