create a script to train autoencoderkl (#10605)

* create a script to train vae

* update main.py

* update train_autoencoderkl.py

* update train_autoencoderkl.py

* add a check of --pretrained_model_name_or_path and --model_config_name_or_path

* remove the comment, remove diffusers in requiremnets.txt, add validation_image ote

* update autoencoderkl.py

* quality

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

examples/research_projects/autoencoderkl/README.md

+# AutoencoderKL training example
+
+## Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+**Important**
+
+To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install .
+```
+
+Then cd in the example folder  and run
+```bash
+pip install -r requirements.txt
+```
+
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+## Training on CIFAR10
+
+Please replace the validation image with your own image.
+
+```bash
+accelerate launch train_autoencoderkl.py \
+    --pretrained_model_name_or_path stabilityai/sd-vae-ft-mse \
+    --dataset_name=cifar10 \
+    --image_column=img \
+    --validation_image images/bird.jpg images/car.jpg images/dog.jpg images/frog.jpg \
+    --num_train_epochs 100 \
+    --gradient_accumulation_steps 2 \
+    --learning_rate 4.5e-6 \
+    --lr_scheduler cosine \
+    --report_to wandb \
+```
+
+## Training on ImageNet
+
+```bash
+accelerate launch train_autoencoderkl.py \
+    --pretrained_model_name_or_path stabilityai/sd-vae-ft-mse \
+    --num_train_epochs 100 \
+    --gradient_accumulation_steps 2 \
+    --learning_rate 4.5e-6 \
+    --lr_scheduler cosine \
+    --report_to wandb \
+    --mixed_precision bf16 \
+    --train_data_dir /path/to/ImageNet/train \
+    --validation_image ./image.png \
+    --decoder_only
+```

examples/research_projects/autoencoderkl/requirements.txt

+accelerate>=0.16.0
+bitsandbytes
+datasets
+huggingface_hub
+lpips
+numpy
+packaging
+Pillow
+taming_transformers
+torch
+torchvision
+tqdm
+transformers
+wandb
+xformers

examples/research_projects/autoencoderkl/train_autoencoderkl.py

+#!/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 contextlib
+import gc
+import logging
+import math
+import os
+import shutil
+from pathlib import Path
+
+import accelerate
+import lpips
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torchvision
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from datasets import load_dataset
+from huggingface_hub import create_repo, upload_folder
+from packaging import version
+from PIL import Image
+from taming.modules.losses.vqperceptual import NLayerDiscriminator, hinge_d_loss, vanilla_d_loss, weights_init
+from torchvision import transforms
+from tqdm.auto import tqdm
+
+import diffusers
+from diffusers import AutoencoderKL
+from diffusers.optimization import get_scheduler
+from diffusers.training_utils import EMAModel
+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_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("0.33.0.dev0")
+
+logger = get_logger(__name__)
+
+
+@torch.no_grad()
+def log_validation(vae, args, accelerator, weight_dtype, step, is_final_validation=False):
+    logger.info("Running validation... ")
+
+    if not is_final_validation:
+        vae = accelerator.unwrap_model(vae)
+    else:
+        vae = AutoencoderKL.from_pretrained(args.output_dir, torch_dtype=weight_dtype)
+
+    images = []
+    inference_ctx = contextlib.nullcontext() if is_final_validation else torch.autocast("cuda")
+
+    image_transforms = transforms.Compose(
+        [
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.CenterCrop(args.resolution),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5], [0.5]),
+        ]
+    )
+
+    for i, validation_image in enumerate(args.validation_image):
+        validation_image = Image.open(validation_image).convert("RGB")
+        targets = image_transforms(validation_image).to(accelerator.device, weight_dtype)
+        targets = targets.unsqueeze(0)
+
+        with inference_ctx:
+            reconstructions = vae(targets).sample
+
+        images.append(torch.cat([targets.cpu(), reconstructions.cpu()], axis=0))
+
+    tracker_key = "test" if is_final_validation else "validation"
+    for tracker in accelerator.trackers:
+        if tracker.name == "tensorboard":
+            np_images = np.stack([np.asarray(img) for img in images])
+            tracker.writer.add_images(f"{tracker_key}: Original (left), Reconstruction (right)", np_images, step)
+        elif tracker.name == "wandb":
+            tracker.log(
+                {
+                    f"{tracker_key}: Original (left), Reconstruction (right)": [
+                        wandb.Image(torchvision.utils.make_grid(image)) for _, image in enumerate(images)
+                    ]
+                }
+            )
+        else:
+            logger.warn(f"image logging not implemented for {tracker.name}")
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    return images
+
+
+def save_model_card(repo_id: str, images=None, base_model=str, repo_folder=None):
+    img_str = ""
+    if images is not None:
+        img_str = "You can find some example images below.\n\n"
+        make_image_grid(images, 1, len(images)).save(os.path.join(repo_folder, "images.png"))
+        img_str += "![images](./images.png)\n"
+
+    model_description = f"""
+# autoencoderkl-{repo_id}
+
+These are autoencoderkl 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",
+        "image-to-image",
+        "diffusers",
+        "autoencoderkl",
+        "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 AutoencoderKL training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--model_config_name_or_path",
+        type=str,
+        default=None,
+        help="The config of the VAE model to train, leave as None to use standard VAE model configuration.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="autoencoderkl-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=4.5e-6,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--disc_learning_rate",
+        type=float,
+        default=4.5e-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(
+        "--disc_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_ema", action="store_true", help="Whether to use EMA model.")
+    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(
+        "--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(
+        "--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(
+        "--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(
+        "--validation_image",
+        type=str,
+        default=None,
+        nargs="+",
+        help="A set of paths to the image be evaluated every `--validation_steps` and logged to `--report_to`.",
+    )
+    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_autoencoderkl",
+        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(
+        "--rec_loss",
+        type=str,
+        default="l2",
+        help="The loss function for VAE reconstruction loss.",
+    )
+    parser.add_argument(
+        "--kl_scale",
+        type=float,
+        default=1e-6,
+        help="Scaling factor for the Kullback-Leibler divergence penalty term.",
+    )
+    parser.add_argument(
+        "--perceptual_scale",
+        type=float,
+        default=0.5,
+        help="Scaling factor for the LPIPS metric",
+    )
+    parser.add_argument(
+        "--disc_start",
+        type=int,
+        default=50001,
+        help="Start for the discriminator",
+    )
+    parser.add_argument(
+        "--disc_factor",
+        type=float,
+        default=1.0,
+        help="Scaling factor for the discriminator",
+    )
+    parser.add_argument(
+        "--disc_scale",
+        type=float,
+        default=1.0,
+        help="Scaling factor for the discriminator",
+    )
+    parser.add_argument(
+        "--disc_loss",
+        type=str,
+        default="hinge",
+        help="Loss function for the discriminator",
+    )
+    parser.add_argument(
+        "--decoder_only",
+        action="store_true",
+        help="Only train the VAE decoder.",
+    )
+
+    if input_args is not None:
+        args = parser.parse_args(input_args)
+    else:
+        args = parser.parse_args()
+
+    if args.pretrained_model_name_or_path is not None and args.model_config_name_or_path is not None:
+        raise ValueError("Cannot specify both `--pretrained_model_name_or_path` and `--model_config_name_or_path`")
+
+    if args.dataset_name is None and args.train_data_dir is None:
+        raise ValueError("Specify either `--dataset_name` or `--train_data_dir`")
+
+    if args.resolution % 8 != 0:
+        raise ValueError(
+            "`--resolution` must be divisible by 8 for consistently sized encoded images between the VAE and the diffusion model."
+        )
+
+    return args
+
+
+def make_train_dataset(args, accelerator):
+    # Get the datasets: you can either provide your own training and evaluation files (see below)
+    # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub).
+
+    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
+    # download the dataset.
+    if args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        dataset = load_dataset(
+            args.dataset_name,
+            args.dataset_config_name,
+            cache_dir=args.cache_dir,
+            data_dir=args.train_data_dir,
+        )
+    else:
+        data_files = {}
+        if args.train_data_dir is not None:
+            data_files["train"] = os.path.join(args.train_data_dir, "**")
+        dataset = load_dataset(
+            "imagefolder",
+            data_files=data_files,
+            cache_dir=args.cache_dir,
+        )
+        # See more about loading custom images at
+        # https://huggingface.co/docs/datasets/v2.0.0/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:
+        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)}"
+            )
+
+    image_transforms = transforms.Compose(
+        [
+            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.CenterCrop(args.resolution),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5], [0.5]),
+        ]
+    )
+
+    def preprocess_train(examples):
+        images = [image.convert("RGB") for image in examples[image_column]]
+        images = [image_transforms(image) for image in images]
+
+        examples["pixel_values"] = images
+
+        return examples
+
+    with accelerator.main_process_first():
+        if args.max_train_samples is not None:
+            dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
+        # Set the training transforms
+        train_dataset = dataset["train"].with_transform(preprocess_train)
+
+    return train_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()
+
+    return {"pixel_values": pixel_values}
+
+
+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_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=args.hub_token
+            ).repo_id
+
+    # Load AutoencoderKL
+    if args.pretrained_model_name_or_path is None and args.model_config_name_or_path is None:
+        config = AutoencoderKL.load_config("stabilityai/sd-vae-ft-mse")
+        vae = AutoencoderKL.from_config(config)
+    elif args.pretrained_model_name_or_path is not None:
+        vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, revision=args.revision)
+    else:
+        config = AutoencoderKL.load_config(args.model_config_name_or_path)
+        vae = AutoencoderKL.from_config(config)
+    if args.use_ema:
+        ema_vae = EMAModel(vae.parameters(), model_cls=AutoencoderKL, model_config=vae.config)
+    perceptual_loss = lpips.LPIPS(net="vgg").eval()
+    discriminator = NLayerDiscriminator(input_nc=3, n_layers=3, use_actnorm=False).apply(weights_init)
+
+    # 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
+
+    # `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:
+                if args.use_ema:
+                    sub_dir = "autoencoderkl_ema"
+                    ema_vae.save_pretrained(os.path.join(output_dir, sub_dir))
+
+                i = len(weights) - 1
+
+                while len(weights) > 0:
+                    weights.pop()
+                    model = models[i]
+
+                    if isinstance(model, AutoencoderKL):
+                        sub_dir = "autoencoderkl"
+                        model.save_pretrained(os.path.join(output_dir, sub_dir))
+                    else:
+                        sub_dir = "discriminator"
+                        os.makedirs(os.path.join(output_dir, sub_dir), exist_ok=True)
+                        torch.save(model.state_dict(), os.path.join(output_dir, sub_dir, "pytorch_model.bin"))
+
+                    i -= 1
+
+        def load_model_hook(models, input_dir):
+            while len(models) > 0:
+                if args.use_ema:
+                    sub_dir = "autoencoderkl_ema"
+                    load_model = EMAModel.from_pretrained(os.path.join(input_dir, sub_dir), AutoencoderKL)
+                    ema_vae.load_state_dict(load_model.state_dict())
+                    ema_vae.to(accelerator.device)
+                    del load_model
+
+                # pop models so that they are not loaded again
+                model = models.pop()
+                load_model = NLayerDiscriminator(input_nc=3, n_layers=3, use_actnorm=False).load_state_dict(
+                    os.path.join(input_dir, "discriminator", "pytorch_model.bin")
+                )
+                model.load_state_dict(load_model.state_dict())
+                del load_model
+
+                model = models.pop()
+                load_model = AutoencoderKL.from_pretrained(input_dir, subfolder="autoencoderkl")
+                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)
+
+    vae.requires_grad_(True)
+    if args.decoder_only:
+        vae.encoder.requires_grad_(False)
+        if getattr(vae, "quant_conv", None):
+            vae.quant_conv.requires_grad_(False)
+    vae.train()
+    discriminator.requires_grad_(True)
+    discriminator.train()
+
+    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."
+                )
+            vae.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    if args.gradient_checkpointing:
+        vae.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(vae).dtype != torch.float32:
+        raise ValueError(f"VAE loaded as datatype {unwrap_model(vae).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
+
+    params_to_optimize = filter(lambda p: p.requires_grad, vae.parameters())
+    disc_params_to_optimize = filter(lambda p: p.requires_grad, discriminator.parameters())
+    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,
+    )
+    disc_optimizer = optimizer_class(
+        disc_params_to_optimize,
+        lr=args.disc_learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    train_dataset = make_train_dataset(args, 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,
+    )
+    disc_lr_scheduler = get_scheduler(
+        args.disc_lr_scheduler,
+        optimizer=disc_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`.
+    (
+        vae,
+        discriminator,
+        optimizer,
+        disc_optimizer,
+        train_dataloader,
+        lr_scheduler,
+        disc_lr_scheduler,
+    ) = accelerator.prepare(
+        vae, discriminator, optimizer, disc_optimizer, train_dataloader, lr_scheduler, disc_lr_scheduler
+    )
+
+    # For mixed precision training we cast the text_encoder and vae weights to half-precision
+    # as these models are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move VAE, perceptual loss and discriminator to device and cast to weight_dtype
+    vae.to(accelerator.device, dtype=weight_dtype)
+    perceptual_loss.to(accelerator.device, dtype=weight_dtype)
+    discriminator.to(accelerator.device, dtype=weight_dtype)
+    if args.use_ema:
+        ema_vae.to(accelerator.device, dtype=weight_dtype)
+
+    # 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))
+        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):
+        vae.train()
+        discriminator.train()
+        for step, batch in enumerate(train_dataloader):
+            # Convert images to latent space and reconstruct from them
+            targets = batch["pixel_values"].to(dtype=weight_dtype)
+            posterior = accelerator.unwrap_model(vae).encode(targets).latent_dist
+            latents = posterior.sample()
+            reconstructions = accelerator.unwrap_model(vae).decode(latents).sample
+
+            if (step // args.gradient_accumulation_steps) % 2 == 0 or global_step < args.disc_start:
+                with accelerator.accumulate(vae):
+                    # reconstruction loss. Pixel level differences between input vs output
+                    if args.rec_loss == "l2":
+                        rec_loss = F.mse_loss(reconstructions.float(), targets.float(), reduction="none")
+                    elif args.rec_loss == "l1":
+                        rec_loss = F.l1_loss(reconstructions.float(), targets.float(), reduction="none")
+                    else:
+                        raise ValueError(f"Invalid reconstruction loss type: {args.rec_loss}")
+                    # perceptual loss. The high level feature mean squared error loss
+                    with torch.no_grad():
+                        p_loss = perceptual_loss(reconstructions, targets)
+
+                    rec_loss = rec_loss + args.perceptual_scale * p_loss
+                    nll_loss = rec_loss
+                    nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+
+                    kl_loss = posterior.kl()
+                    kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
+
+                    logits_fake = discriminator(reconstructions)
+                    g_loss = -torch.mean(logits_fake)
+                    last_layer = accelerator.unwrap_model(vae).decoder.conv_out.weight
+                    nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+                    g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+                    disc_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+                    disc_weight = torch.clamp(disc_weight, 0.0, 1e4).detach()
+                    disc_weight = disc_weight * args.disc_scale
+                    disc_factor = args.disc_factor if global_step >= args.disc_start else 0.0
+
+                    loss = nll_loss + args.kl_scale * kl_loss + disc_weight * disc_factor * g_loss
+
+                    logs = {
+                        "loss": loss.detach().mean().item(),
+                        "nll_loss": nll_loss.detach().mean().item(),
+                        "rec_loss": rec_loss.detach().mean().item(),
+                        "p_loss": p_loss.detach().mean().item(),
+                        "kl_loss": kl_loss.detach().mean().item(),
+                        "disc_weight": disc_weight.detach().mean().item(),
+                        "disc_factor": disc_factor,
+                        "g_loss": g_loss.detach().mean().item(),
+                        "lr": lr_scheduler.get_last_lr()[0],
+                    }
+
+                    accelerator.backward(loss)
+                    if accelerator.sync_gradients:
+                        params_to_clip = vae.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)
+            else:
+                with accelerator.accumulate(discriminator):
+                    logits_real = discriminator(targets)
+                    logits_fake = discriminator(reconstructions)
+                    disc_loss = hinge_d_loss if args.disc_loss == "hinge" else vanilla_d_loss
+                    disc_factor = args.disc_factor if global_step >= args.disc_start else 0.0
+                    disc_loss = disc_factor * disc_loss(logits_real, logits_fake)
+                    logs = {
+                        "disc_loss": disc_loss.detach().mean().item(),
+                        "logits_real": logits_real.detach().mean().item(),
+                        "logits_fake": logits_fake.detach().mean().item(),
+                        "disc_lr": disc_lr_scheduler.get_last_lr()[0],
+                    }
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+                if args.use_ema:
+                    ema_vae.step(vae.parameters())
+
+                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 global_step == 1 or global_step % args.validation_steps == 0:
+                        if args.use_ema:
+                            ema_vae.store(vae.parameters())
+                            ema_vae.copy_to(vae.parameters())
+                        image_logs = log_validation(
+                            vae,
+                            args,
+                            accelerator,
+                            weight_dtype,
+                            global_step,
+                        )
+                        if args.use_ema:
+                            ema_vae.restore(vae.parameters())
+
+            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:
+        vae = accelerator.unwrap_model(vae)
+        discriminator = accelerator.unwrap_model(discriminator)
+        if args.use_ema:
+            ema_vae.copy_to(vae.parameters())
+        vae.save_pretrained(args.output_dir)
+        torch.save(discriminator.state_dict(), os.path.join(args.output_dir, "pytorch_model.bin"))
+        # Run a final round of validation.
+        image_logs = None
+        image_logs = log_validation(
+            vae=vae,
+            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)