#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

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

import accelerate
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 DistributedType, ProjectConfiguration, set_seed
from datasets import load_dataset
from huggingface_hub import create_repo, upload_folder
from packaging import version
from PIL import Image
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import (
    AutoTokenizer,
    CLIPTextModel,
    T5EncoderModel,
)

import diffusers
from diffusers import (
    AutoencoderKL,
    FlowMatchEulerDiscreteScheduler,
    FluxTransformer2DModel,
)
from diffusers.models.controlnets.controlnet_flux import FluxControlNetModel
from diffusers.optimization import get_scheduler
from diffusers.pipelines.flux.pipeline_flux_controlnet import FluxControlNetPipeline
from diffusers.training_utils import compute_density_for_timestep_sampling, free_memory
from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.import_utils import is_torch_npu_available, 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("0.35.0.dev0")

logger = get_logger(__name__)
if is_torch_npu_available():
    torch.npu.config.allow_internal_format = False


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

    if not is_final_validation:
        flux_controlnet = accelerator.unwrap_model(flux_controlnet)
        pipeline = FluxControlNetPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
            controlnet=flux_controlnet,
            transformer=flux_transformer,
            torch_dtype=torch.bfloat16,
        )
    else:
        flux_controlnet = FluxControlNetModel.from_pretrained(
            args.output_dir, torch_dtype=torch.bfloat16, variant=args.save_weight_dtype
        )
        pipeline = FluxControlNetPipeline.from_pretrained(
            args.pretrained_model_name_or_path,
            controlnet=flux_controlnet,
            transformer=flux_transformer,
            torch_dtype=torch.bfloat16,
        )

    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 = []
    if is_final_validation or torch.backends.mps.is_available():
        autocast_ctx = nullcontext()
    else:
        autocast_ctx = torch.autocast(accelerator.device.type)

    for validation_prompt, validation_image in zip(validation_prompts, validation_images):
        from diffusers.utils import load_image

        validation_image = load_image(validation_image)
        # maybe need to inference on 1024 to get a good image
        validation_image = validation_image.resize((args.resolution, args.resolution))

        images = []

        # pre calculate  prompt embeds, pooled prompt embeds, text ids because t5 does not support autocast
        prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(
            validation_prompt, prompt_2=validation_prompt
        )
        for _ in range(args.num_validation_images):
            with autocast_ctx:
                # need to fix in pipeline_flux_controlnet
                image = pipeline(
                    prompt_embeds=prompt_embeds,
                    pooled_prompt_embeds=pooled_prompt_embeds,
                    control_image=validation_image,
                    num_inference_steps=28,
                    controlnet_conditioning_scale=1,
                    guidance_scale=3.5,
                    generator=generator,
                ).images[0]
            image = image.resize((args.resolution, args.resolution))
            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 = [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
    free_memory()
    return image_logs


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
            make_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"""
# controlnet-{repo_id}

These are controlnet weights trained on {base_model} with new type of conditioning.
{img_str}

## License

Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)
"""

    model_card = load_or_create_model_card(
        repo_id_or_path=repo_id,
        from_training=True,
        license="other",
        base_model=base_model,
        model_description=model_description,
        inference=True,
    )

    tags = [
        "flux",
        "flux-diffusers",
        "text-to-image",
        "diffusers",
        "controlnet",
        "diffusers-training",
    ]
    model_card = populate_model_card(model_card, tags=tags)

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


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(
        "--pretrained_vae_model_name_or_path",
        type=str,
        default=None,
        help="Path to an improved VAE to stabilize training. For more details check out: https://github.com/huggingface/diffusers/pull/4038.",
    )
    parser.add_argument(
        "--controlnet_model_name_or_path",
        type=str,
        default=None,
        help="Path to pretrained controlnet model or model identifier from huggingface.co/models."
        " If not specified controlnet weights are initialized from unet.",
    )
    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(
        "--revision",
        type=str,
        default=None,
        required=False,
        help="Revision of pretrained model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--tokenizer_name",
        type=str,
        default=None,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="controlnet-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(
        "--crops_coords_top_left_h",
        type=int,
        default=0,
        help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
    )
    parser.add_argument(
        "--crops_coords_top_left_w",
        type=int,
        default=0,
        help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
    )
    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-6,
        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(
        "--use_adafactor",
        action="store_true",
        help=(
            "Adafactor is a stochastic optimization method based on Adam that reduces memory usage while retaining"
            "the empirical benefits of adaptivity. This is achieved through maintaining a factored representation "
            "of the squared gradient accumulator across training steps."
        ),
    )
    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(
        "--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(
        "--enable_npu_flash_attention", action="store_true", help="Whether or not to use npu flash attention."
    )
    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_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(
        "--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 controlnet 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 controlnet 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_double_layers",
        type=int,
        default=4,
        help="Number of double layers in the controlnet (default: 4).",
    )
    parser.add_argument(
        "--num_single_layers",
        type=int,
        default=4,
        help="Number of single layers in the controlnet (default: 4).",
    )
    parser.add_argument(
        "--num_validation_images",
        type=int,
        default=2,
        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="flux_train_controlnet",
        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"
        ),
    )
    parser.add_argument(
        "--jsonl_for_train",
        type=str,
        default=None,
        help="Path to the jsonl file containing the training data.",
    )

    parser.add_argument(
        "--guidance_scale",
        type=float,
        default=3.5,
        help="the guidance scale used for transformer.",
    )

    parser.add_argument(
        "--save_weight_dtype",
        type=str,
        default="fp32",
        choices=[
            "fp16",
            "bf16",
            "fp32",
        ],
        help=("Preserve precision type according to selected weight"),
    )

    parser.add_argument(
        "--weighting_scheme",
        type=str,
        default="logit_normal",
        choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
        help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'),
    )
    parser.add_argument(
        "--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."
    )
    parser.add_argument(
        "--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme."
    )
    parser.add_argument(
        "--mode_scale",
        type=float,
        default=1.29,
        help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
    )
    parser.add_argument(
        "--enable_model_cpu_offload",
        action="store_true",
        help="Enable model cpu offload and save memory.",
    )
    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.",
    )

    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.jsonl_for_train is None:
        raise ValueError("Specify either `--dataset_name` or `--jsonl_for_train`")

    if args.dataset_name is not None and args.jsonl_for_train is not None:
        raise ValueError("Specify only one of `--dataset_name` or `--jsonl_for_train`")

    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 controlnet encoder."
        )

    return args


def get_train_dataset(args, accelerator):
    dataset = None
    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,
        )
    if args.jsonl_for_train is not None:
        # load from json
        dataset = load_dataset("json", data_files=args.jsonl_for_train, cache_dir=args.cache_dir)
        dataset = dataset.flatten_indices()
    # 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:
        image_column = column_names[0]
        logger.info(f"image column defaulting to {image_column}")
    else:
        image_column = args.image_column
        if 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:
        caption_column = column_names[1]
        logger.info(f"caption column defaulting to {caption_column}")
    else:
        caption_column = args.caption_column
        if 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:
        conditioning_image_column = column_names[2]
        logger.info(f"conditioning image column defaulting to {conditioning_image_column}")
    else:
        conditioning_image_column = args.conditioning_image_column
        if 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)}"
            )

    with accelerator.main_process_first():
        train_dataset = dataset["train"].shuffle(seed=args.seed)
        if args.max_train_samples is not None:
            train_dataset = train_dataset.select(range(args.max_train_samples))
    return train_dataset


def prepare_train_dataset(dataset, accelerator):
    interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
    if interpolation is None:
        raise ValueError(f"Unsupported interpolation mode {interpolation=}.")

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

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

    def preprocess_train(examples):
        images = [
            (image.convert("RGB") if not isinstance(image, str) else Image.open(image).convert("RGB"))
            for image in examples[args.image_column]
        ]
        images = [image_transforms(image) for image in images]

        conditioning_images = [
            (image.convert("RGB") if not isinstance(image, str) else Image.open(image).convert("RGB"))
            for image in examples[args.conditioning_image_column]
        ]
        conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images]
        examples["pixel_values"] = images
        examples["conditioning_pixel_values"] = conditioning_images

        return examples

    with accelerator.main_process_first():
        dataset = dataset.with_transform(preprocess_train)

    return dataset


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()

    prompt_ids = torch.stack([torch.tensor(example["prompt_embeds"]) for example in examples])

    pooled_prompt_embeds = torch.stack([torch.tensor(example["pooled_prompt_embeds"]) for example in examples])
    text_ids = torch.stack([torch.tensor(example["text_ids"]) for example in examples])

    return {
        "pixel_values": pixel_values,
        "conditioning_pixel_values": conditioning_pixel_values,
        "prompt_ids": prompt_ids,
        "unet_added_conditions": {"pooled_prompt_embeds": pooled_prompt_embeds, "time_ids": text_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."
        )

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

    if torch.backends.mps.is_available() and args.mixed_precision == "bf16":
        # due to pytorch#99272, MPS does not yet support bfloat16.
        raise ValueError(
            "Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
        )

    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=str(logging_out_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. A technique for accelerating machine learning computations on iOS and macOS devices.
    if torch.backends.mps.is_available():
        print("MPS is enabled. Disabling AMP.")
        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",
        # DEBUG, INFO, WARNING, ERROR, CRITICAL
        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=args.hub_token
            ).repo_id

    # Load the tokenizers
    # load clip tokenizer
    tokenizer_one = AutoTokenizer.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="tokenizer",
        revision=args.revision,
    )
    # load t5 tokenizer
    tokenizer_two = AutoTokenizer.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="tokenizer_2",
        revision=args.revision,
    )
    # load clip text encoder
    text_encoder_one = CLIPTextModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
    )
    # load t5 text encoder
    text_encoder_two = T5EncoderModel.from_pretrained(
        args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
    )

    vae = AutoencoderKL.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="vae",
        revision=args.revision,
        variant=args.variant,
    )
    flux_transformer = FluxTransformer2DModel.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="transformer",
        revision=args.revision,
        variant=args.variant,
    )
    if args.controlnet_model_name_or_path:
        logger.info("Loading existing controlnet weights")
        flux_controlnet = FluxControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
    else:
        logger.info("Initializing controlnet weights from transformer")
        # we can define the num_layers, num_single_layers,
        flux_controlnet = FluxControlNetModel.from_transformer(
            flux_transformer,
            attention_head_dim=flux_transformer.config["attention_head_dim"],
            num_attention_heads=flux_transformer.config["num_attention_heads"],
            num_layers=args.num_double_layers,
            num_single_layers=args.num_single_layers,
        )
    logger.info("all models loaded successfully")

    noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
        args.pretrained_model_name_or_path,
        subfolder="scheduler",
    )
    noise_scheduler_copy = copy.deepcopy(noise_scheduler)
    vae.requires_grad_(False)
    flux_transformer.requires_grad_(False)
    text_encoder_one.requires_grad_(False)
    text_encoder_two.requires_grad_(False)
    flux_controlnet.train()

    # use some pipeline function
    flux_controlnet_pipeline = FluxControlNetPipeline(
        scheduler=noise_scheduler,
        vae=vae,
        text_encoder=text_encoder_one,
        tokenizer=tokenizer_one,
        text_encoder_2=text_encoder_two,
        tokenizer_2=tokenizer_two,
        transformer=flux_transformer,
        controlnet=flux_controlnet,
    )
    if args.enable_model_cpu_offload:
        flux_controlnet_pipeline.enable_model_cpu_offload()
    else:
        flux_controlnet_pipeline.to(accelerator.device)

    def unwrap_model(model):
        model = accelerator.unwrap_model(model)
        model = model._orig_mod if is_compiled_module(model) else model
        return model

    # `accelerate` 0.16.0 will have better support for customized saving
    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
        # 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:
                i = len(weights) - 1

                while len(weights) > 0:
                    weights.pop()
                    model = models[i]

                    sub_dir = "flux_controlnet"
                    model.save_pretrained(os.path.join(output_dir, sub_dir))

                    i -= 1

        def load_model_hook(models, input_dir):
            while len(models) > 0:
                # pop models so that they are not loaded again
                model = models.pop()

                # load diffusers style into model
                load_model = FluxControlNetModel.from_pretrained(input_dir, subfolder="flux_controlnet")
                model.register_to_config(**load_model.config)

                model.load_state_dict(load_model.state_dict())
                del load_model

        accelerator.register_save_state_pre_hook(save_model_hook)
        accelerator.register_load_state_pre_hook(load_model_hook)

    if args.enable_npu_flash_attention:
        if is_torch_npu_available():
            logger.info("npu flash attention enabled.")
            flux_transformer.enable_npu_flash_attention()
        else:
            raise ValueError("npu flash attention requires torch_npu extensions and is supported only on npu devices.")

    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."
                )
            flux_transformer.enable_xformers_memory_efficient_attention()
            flux_controlnet.enable_xformers_memory_efficient_attention()
        else:
            raise ValueError("xformers is not available. Make sure it is installed correctly")

    if args.gradient_checkpointing:
        flux_transformer.enable_gradient_checkpointing()
        flux_controlnet.enable_gradient_checkpointing()

    # Check that all trainable models are in full precision
    low_precision_error_string = (
        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
        " doing mixed precision training, copy of the weights should still be float32."
    )

    if unwrap_model(flux_controlnet).dtype != torch.float32:
        raise ValueError(
            f"Controlnet loaded as datatype {unwrap_model(flux_controlnet).dtype}. {low_precision_error_string}"
        )

    # 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
    params_to_optimize = flux_controlnet.parameters()
    # use adafactor optimizer to save gpu memory
    if args.use_adafactor:
        from transformers import Adafactor

        optimizer = Adafactor(
            params_to_optimize,
            lr=args.learning_rate,
            scale_parameter=False,
            relative_step=False,
            # warmup_init=True,
            weight_decay=args.adam_weight_decay,
        )
    else:
        optimizer = optimizer_class(
            params_to_optimize,
            lr=args.learning_rate,
            betas=(args.adam_beta1, args.adam_beta2),
            weight_decay=args.adam_weight_decay,
            eps=args.adam_epsilon,
        )

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

    vae.to(accelerator.device, dtype=weight_dtype)
    flux_transformer.to(accelerator.device, dtype=weight_dtype)

    def compute_embeddings(batch, proportion_empty_prompts, flux_controlnet_pipeline, weight_dtype, is_train=True):
        prompt_batch = batch[args.caption_column]
        captions = []
        for caption in prompt_batch:
            if random.random() < 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])
        prompt_batch = captions
        prompt_embeds, pooled_prompt_embeds, text_ids = flux_controlnet_pipeline.encode_prompt(
            prompt_batch, prompt_2=prompt_batch
        )
        prompt_embeds = prompt_embeds.to(dtype=weight_dtype)
        pooled_prompt_embeds = pooled_prompt_embeds.to(dtype=weight_dtype)
        text_ids = text_ids.to(dtype=weight_dtype)

        # text_ids [512,3] to [bs,512,3]
        text_ids = text_ids.unsqueeze(0).expand(prompt_embeds.shape[0], -1, -1)
        return {"prompt_embeds": prompt_embeds, "pooled_prompt_embeds": pooled_prompt_embeds, "text_ids": text_ids}

    train_dataset = get_train_dataset(args, accelerator)
    compute_embeddings_fn = functools.partial(
        compute_embeddings,
        flux_controlnet_pipeline=flux_controlnet_pipeline,
        proportion_empty_prompts=args.proportion_empty_prompts,
        weight_dtype=weight_dtype,
    )
    with accelerator.main_process_first():
        from datasets.fingerprint import Hasher

        # fingerprint used by the cache for the other processes to load the result
        # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
        new_fingerprint = Hasher.hash(args)
        train_dataset = train_dataset.map(
            compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint, batch_size=50
        )

    text_encoder_one.to("cpu")
    text_encoder_two.to("cpu")
    free_memory()

    # Then get the training dataset ready to be passed to the dataloader.
    train_dataset = prepare_train_dataset(train_dataset, 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.
    # Check the PR https://github.com/huggingface/diffusers/pull/8312 for detailed explanation.
    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=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`.
    flux_controlnet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        flux_controlnet, 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:
        tracker_config = dict(vars(args))

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

        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,
    )

    def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
        sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype)
        schedule_timesteps = noise_scheduler_copy.timesteps.to(accelerator.device)
        timesteps = timesteps.to(accelerator.device)
        step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]

        sigma = sigmas[step_indices].flatten()
        while len(sigma.shape) < n_dim:
            sigma = sigma.unsqueeze(-1)
        return sigma

    image_logs = None
    for epoch in range(first_epoch, args.num_train_epochs):
        for step, batch in enumerate(train_dataloader):
            with accelerator.accumulate(flux_controlnet):
                # Convert images to latent space
                # vae encode
                pixel_values = batch["pixel_values"].to(dtype=weight_dtype)
                pixel_latents_tmp = vae.encode(pixel_values).latent_dist.sample()
                pixel_latents_tmp = (pixel_latents_tmp - vae.config.shift_factor) * vae.config.scaling_factor
                pixel_latents = FluxControlNetPipeline._pack_latents(
                    pixel_latents_tmp,
                    pixel_values.shape[0],
                    pixel_latents_tmp.shape[1],
                    pixel_latents_tmp.shape[2],
                    pixel_latents_tmp.shape[3],
                )

                control_values = batch["conditioning_pixel_values"].to(dtype=weight_dtype)
                control_latents = vae.encode(control_values).latent_dist.sample()
                control_latents = (control_latents - vae.config.shift_factor) * vae.config.scaling_factor
                control_image = FluxControlNetPipeline._pack_latents(
                    control_latents,
                    control_values.shape[0],
                    control_latents.shape[1],
                    control_latents.shape[2],
                    control_latents.shape[3],
                )

                latent_image_ids = FluxControlNetPipeline._prepare_latent_image_ids(
                    batch_size=pixel_latents_tmp.shape[0],
                    height=pixel_latents_tmp.shape[2] // 2,
                    width=pixel_latents_tmp.shape[3] // 2,
                    device=pixel_values.device,
                    dtype=pixel_values.dtype,
                )

                bsz = pixel_latents.shape[0]
                noise = torch.randn_like(pixel_latents).to(accelerator.device).to(dtype=weight_dtype)
                # Sample a random timestep for each image
                # for weighting schemes where we sample timesteps non-uniformly
                u = compute_density_for_timestep_sampling(
                    weighting_scheme=args.weighting_scheme,
                    batch_size=bsz,
                    logit_mean=args.logit_mean,
                    logit_std=args.logit_std,
                    mode_scale=args.mode_scale,
                )
                indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()
                timesteps = noise_scheduler_copy.timesteps[indices].to(device=pixel_latents.device)

                # Add noise according to flow matching.
                sigmas = get_sigmas(timesteps, n_dim=pixel_latents.ndim, dtype=pixel_latents.dtype)
                noisy_model_input = (1.0 - sigmas) * pixel_latents + sigmas * noise

                # handle guidance
                if flux_transformer.config.guidance_embeds:
                    guidance_vec = torch.full(
                        (noisy_model_input.shape[0],),
                        args.guidance_scale,
                        device=noisy_model_input.device,
                        dtype=weight_dtype,
                    )
                else:
                    guidance_vec = None

                controlnet_block_samples, controlnet_single_block_samples = flux_controlnet(
                    hidden_states=noisy_model_input,
                    controlnet_cond=control_image,
                    timestep=timesteps / 1000,
                    guidance=guidance_vec,
                    pooled_projections=batch["unet_added_conditions"]["pooled_prompt_embeds"].to(dtype=weight_dtype),
                    encoder_hidden_states=batch["prompt_ids"].to(dtype=weight_dtype),
                    txt_ids=batch["unet_added_conditions"]["time_ids"][0].to(dtype=weight_dtype),
                    img_ids=latent_image_ids,
                    return_dict=False,
                )

                noise_pred = flux_transformer(
                    hidden_states=noisy_model_input,
                    timestep=timesteps / 1000,
                    guidance=guidance_vec,
                    pooled_projections=batch["unet_added_conditions"]["pooled_prompt_embeds"].to(dtype=weight_dtype),
                    encoder_hidden_states=batch["prompt_ids"].to(dtype=weight_dtype),
                    controlnet_block_samples=[sample.to(dtype=weight_dtype) for sample in controlnet_block_samples]
                    if controlnet_block_samples is not None
                    else None,
                    controlnet_single_block_samples=[
                        sample.to(dtype=weight_dtype) for sample in controlnet_single_block_samples
                    ]
                    if controlnet_single_block_samples is not None
                    else None,
                    txt_ids=batch["unet_added_conditions"]["time_ids"][0].to(dtype=weight_dtype),
                    img_ids=latent_image_ids,
                    return_dict=False,
                )[0]

                loss = F.mse_loss(noise_pred.float(), (noise - pixel_latents).float(), reduction="mean")
                accelerator.backward(loss)
                # Check if the gradient of each model parameter contains NaN
                for name, param in flux_controlnet.named_parameters():
                    if param.grad is not None and torch.isnan(param.grad).any():
                        logger.error(f"Gradient for {name} contains NaN!")

                if accelerator.sync_gradients:
                    params_to_clip = flux_controlnet.parameters()
                    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

                # DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.
                if accelerator.distributed_type == DistributedType.DEEPSPEED or 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=vae,
                            flux_transformer=flux_transformer,
                            flux_controlnet=flux_controlnet,
                            args=args,
                            accelerator=accelerator,
                            weight_dtype=weight_dtype,
                            step=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:
        flux_controlnet = unwrap_model(flux_controlnet)
        save_weight_dtype = torch.float32
        if args.save_weight_dtype == "fp16":
            save_weight_dtype = torch.float16
        elif args.save_weight_dtype == "bf16":
            save_weight_dtype = torch.bfloat16
        flux_controlnet.to(save_weight_dtype)
        if args.save_weight_dtype != "fp32":
            flux_controlnet.save_pretrained(args.output_dir, variant=args.save_weight_dtype)
        else:
            flux_controlnet.save_pretrained(args.output_dir)
        # Run a final round of validation.
        # Setting `vae`, `unet`, and `controlnet` to None to load automatically from `args.output_dir`.
        image_logs = None
        if args.validation_prompt is not None:
            image_logs = log_validation(
                vae=vae,
                flux_transformer=flux_transformer,
                flux_controlnet=None,
                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,
                folder_path=args.output_dir,
                commit_message="End of training",
                ignore_patterns=["step_*", "epoch_*"],
            )

    accelerator.end_training()


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